SC_VitC_normalizing.RNA.and.hMeDIP.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataSciene Spring 2019 Now You \"C\" Me Group\n",
    "link to video: https://youtu.be/lhodcPtmywc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Schwann Cell Vitamin C Data Normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction to what I am doing here\n",
    "\n",
    "This is going to be a copy of the ARPE-19 Data Normalization. I am going to keep all of the information about how I normalize the data and simply update the actual code for changes in file names, sample numbers, cutoff points, etc. This should mirror the ARPE_normalizing.RNA.and.hMeDIP.ipynb notebook very closely.\n",
    "\n",
    "To start with, I have my raw count files. I have combined them already (using bash scripting), so it is in table format where it is tab delimited like this:\n",
    "\n",
    "    Gene_ID | Sample 1 | Sample 2 | Sample 3 | Sample 4\n",
    "    ENSRN0001 | 6 | 8 | 24| 29\n",
    "    ENSRN0002 | 12 | 13 | 14 | 13\n",
    "    ENSRN0003 | 18 | 17 | 1 | 0\n",
    "\n",
    "For the RNA I also have results from edgeR and DESeq2. I have the transcript info from the GTF file in a csv file that I will use to determine transcript length.\n",
    "\n",
    "For the hMeDIP samples I also have the edgeR statistical analysis, the bed file stating the location of the each peak (chromosome, start, stop, name of peak), the count data for genomic input (which was used for the peak calling and will be used for determining the cutoff of expression) and the region_analysis output which tells which gene the peak is close to. \n",
    "\n",
    "RNA-seq samples were aligned using STAR and then quantified using htseq-count. Both edgeR and DESeq2 are used for differential analysis and genes below a false discovery rate (FDR, also called adjusted P-value) of 0.05 by both DESeq2 and edgeR will be called differential. A limit of detection will be calculated using the genomic input from the hMeDIP-seq portion of the experiment. \n",
    "\n",
    "hMeDIP-seq peaks were called using the irreproducible discovery rate pipeline designed for the ChIP-seq portion of the ENCODE consortium. Counts were generated using htseq-count. Differential analysis was calculated using edgeR. To account for not having a second statistical program (DESeq2 can't normalize to all data), a cutoff of 1.5-fold change will be used in conjunction with an FDR of 0.05 by edgeR to determine differential peaks.\n",
    "\n",
    "Genes that have more count data after treatment (increase in expression) or peaks that have higher count data (increase in enrichment from the antibody) are considered \"Upregulated\". Genes that have fewer counts or lower coverage (decreae in expression) or peaks that have lower coverage (decreased enrichment from the antibody) are considered \"Downregulated\". Genes that have relatively similar count data or that do not achieve statistical significant are considered \"Nondifferential\".\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### hMeDIP-seq normalization \n",
    "\n",
    "In this particular jupyter notebook, I am going to be cleaning up the data. This has many steps, and I am going to save many intermediate files. Most of those files will not be used in the correlation or classification parts of this project, but the files are still useful for this project and may be used in future directions from this sampleset.\n",
    "\n",
    "The first step to cleaning up the hMeDIP-seq data is to normalize the counts to the total number of reads. This is a Read Count Per Million (RCPM), but this first one does not exclude outliers. \n",
    "\n",
    "The next step is to determine which peaks are outliers. The reason for this is that outliers can bias your normalization. As an example, if you have two samples that are identical, and then you spike in DNA that will be in one region for 50% of one of the samples, your normalization will all be wrong. Instead of saying that your spiked-sample has one peak that is significantly different than what is found in the other sample, all of your peaks look like they have half as much coverage as the non-spiked sample and all regions appear differential. If you remove the one spiked region from the normalization, now the unchanged regions all appear the same between the samples (as they should), but the spiked in reads still appear only in the one sample. For this reason, this step includes a linear regression between the two sample types (in this case control and vitamin C), and then a Cook's value of 1 (can be changed), is used to determine the outliers. \n",
    "\n",
    "Once you have identified outliers, you can go back to the raw count data and normalize to total read counts excluding the outliers. This gives you the RCPM. This file is saved as the RCPM table.\n",
    "\n",
    "The next step is to normalize to the RCPM by the width of each peak. This allows you to compare one peak to another. If you don't do this and you compare one peak that covers 100 base pairs (bp) to another peak that covers 100,000 bp, you would just by chance expect that the secon peak has 1,000 times as many reads. To normalize this, we take the RCPM and multiply by 1,000 and divide by the length of the peak. This is referred to as \"Fragments Per Kilobase per Million mapped reads\" (FPKM, see Trapnell et al. 2012, Nature Protocols). To do this, I use the peaks bed file, which contains information about the start and stop positions of each peak that can be used to calculate the length of each peak. This file is saved as the FPKM table. \n",
    "\n",
    "Once reads are normalized correctly, I incorporate the statistical data. For hMeDIP-seq, I use edgeR and a fold change. Peaks that receive a false-discovery rate (FDR, benjamini-hocherberg correction) below 0.05 and a fold change of 1.5x or greater are considered differential.\n",
    "\n",
    "The next step is to determine which genes are most likely affected by each peak. To do this I used region_analysis, which is a program developed for the ENCODE project. This tells you which gene is closest to the center of the peak and how far the peak is from the transcription start site (TSS) of the gene. Using this data, I can assign each peak to a gene (or classify it as intergenic), and classify each to a region of the gene (upstream promoter, TSS, downstream promoter or gene body). \n",
    "\n",
    "There are a lot more peaks than genes, and most genes contain many peaks. As such, the next step is to determine the counts of peaks in each region per gene. Additionally, these peaks are divided by how they respond to vitamin C (upregulated, downregulated or nondifferential). This file is saved as a counts per gene region file. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalizing RNA-seq data\n",
    "\n",
    "Like the hMeDIP-seq data, the RNA-seq data needs to be normalized. The first step is to normalize to the total reads within annotated features, and then to find outliers, just like is done for the hMeDIP-seq. You next go back to the raw count data and normalize to the total read counts within annotated features that are not outliers to get the RCPM table. Next, you use the genomic info file to get the lengths of each transcript and calculate the FPKM. This is essentially identical to the first few steps of hMeDIP-seq, except that we don't include reads that don't lie within annotated features for RNA. \n",
    "\n",
    "Next, we need to determine differential genes. For RNA-seq data, we use both edgeR and DESeq2. Genes that receive an FDR value below 0.05 by both edgeR and DESeq2 are considered differential. No fold change limit is taken into account and using two algorithms for differential analysis is thought to limit false positives. \n",
    "\n",
    "Again, the fold change is used to split differential genes into upregulated and downregulated genes.\n",
    "\n",
    "Now, the counts of peaks per gene region are integrated with the RNA-seq data to generate a table that contains information that will be used for our classification analysis. Other information that is kept in the file includes gene biotype (e.g. protein coding, long noncoding RNA, antisense RNA, etc.) and the length of the gene. \n",
    "\n",
    "The next step is different than was used for hMeDIP-seq. For the hMeDIP-seq, I used the IDR pipeline (from ENCODE) to determine which regions were highly enriched. However, for RNA we look at all annotated regions. Most of these regions, however, are not going to have any reads. This step is determining which genes are above our limit of detection. To do this, I perform the normalization of the genomic input data. The genomic input data was generated for hMeDIP-seq, but has no enrichment. It is meant to represent what we would get if we sequenced just noise. For the RNA, I draw out density plots of the RNA-seq samples and the genomic input. If we use a limit of detection (LOD) below the highest expressed genomic input, then those are essentially false positives for the LOD. If we use a LOD above the bottom of the true genes, then those true genes below that point ar false negatives for our LOD. For this, I look at the density plot and take the point where the RNA-seq data overlaps the genomic input to find the minimum number of false positives and false negatives. Only the transcripts above the determined LOD are used for the rest of this project. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reading in the count files and normalizing them\n",
    "The first step is going to be reading in the count files and normalizing them to total read counts. There will be multiple steps of normalization, but this is the first one.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['.DS_Store',\n",
       " 'Merged.SC.LAA.Final.peaks.bed',\n",
       " 'SC.VitC.hMeDIP.counts.txt',\n",
       " 'SC.VitC.INPUT.Final.counts.txt',\n",
       " 'SC.VitC.RNA.DESeq2.csv',\n",
       " 'SC.VitC.RNA.edgeR.csv',\n",
       " 'SC.VitC.RNA.counts.txt',\n",
       " 'SC_VitC_Region_Analysis.bed.txt',\n",
       " 'SC_VitC.hMeDIP.edgeR.csv']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.listdir(\"Input_Data/SC-VitC_Dataset\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MM10.Consensus.Transcript.Info.csv',\n",
       " '.DS_Store',\n",
       " 'SC-cAMP_Dataset',\n",
       " 'HG38.90.Consensus.transcript.info.csv',\n",
       " 'RN6.91.Consensus.transcript.info.csv',\n",
       " 'ARPE-19_Dataset',\n",
       " 'SC-VitC_Dataset']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(\"Input_Data/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/david/Documents/David_Sant/Classes/Data_Science_COMP_5360/2019-datascience-homeworks/Final Project/now-u-c-me-datascience-final-project'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "Input_path = r\"Input_Data/SC-VitC_Dataset/\"\n",
    "Output_Path = r\"Output_Tables_and_Results/SC-VitC_Intermediate_Files/\"\n",
    "\n",
    "Input_hMeDIP_Counts_File = Input_path+\"SC.VitC.hMeDIP.counts.txt\"\n",
    "Input_hMeDIP_edgeR = Input_path+\"SC_VitC.hMeDIP.edgeR.csv\"\n",
    "Input_region_analysis = Input_path+\"SC_VitC_Region_Analysis.bed.txt\"\n",
    "Input_Genomic_Input_Counts_File = Input_path+\"SC.VitC.INPUT.Final.counts.txt\"\n",
    "Input_bed_file = Input_path+\"Merged.SC.LAA.Final.peaks.bed\"\n",
    "\n",
    "Input_RNA_Counts_File = Input_path+\"SC.VitC.RNA.counts.txt\"\n",
    "Input_RNA_edgeR = Input_path+\"SC.VitC.RNA.edgeR.csv\"\n",
    "Input_RNA_DESeq2 = Input_path+\"SC.VitC.RNA.DESeq2.csv\"\n",
    "\n",
    "Input_genomic_info_file = \"Input_Data/RN6.91.Consensus.transcript.info.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_hMeDIP_raw = pd.read_csv(Input_hMeDIP_Counts_File, sep = \"\\t\", index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "215956\n"
     ]
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SC_Control_1_IP</th>\n",
       "      <th>SC_Control_2_IP</th>\n",
       "      <th>SC_Control_3_IP</th>\n",
       "      <th>SC_VitC_1_IP</th>\n",
       "      <th>SC_VitC_2_IP</th>\n",
       "      <th>SC_VitC_3_IP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peak_Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merged_peak_92032</th>\n",
       "      <td>153</td>\n",
       "      <td>153</td>\n",
       "      <td>209</td>\n",
       "      <td>96</td>\n",
       "      <td>89</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merged_peak_92041</th>\n",
       "      <td>119</td>\n",
       "      <td>112</td>\n",
       "      <td>172</td>\n",
       "      <td>116</td>\n",
       "      <td>122</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100020</th>\n",
       "      <td>72</td>\n",
       "      <td>73</td>\n",
       "      <td>86</td>\n",
       "      <td>56</td>\n",
       "      <td>50</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100028</th>\n",
       "      <td>66</td>\n",
       "      <td>71</td>\n",
       "      <td>93</td>\n",
       "      <td>50</td>\n",
       "      <td>49</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_10003</th>\n",
       "      <td>73</td>\n",
       "      <td>90</td>\n",
       "      <td>84</td>\n",
       "      <td>53</td>\n",
       "      <td>76</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                                    SC_Control_1_IP  \\\n",
       "Peak_Name                                                             \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...              153   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...              119   \n",
       "SC_Control_IP_Merged_peak_100020                                 72   \n",
       "SC_Control_IP_Merged_peak_100028                                 66   \n",
       "SC_Control_IP_Merged_peak_10003                                  73   \n",
       "\n",
       "                                                    SC_Control_2_IP  \\\n",
       "Peak_Name                                                             \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...              153   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...              112   \n",
       "SC_Control_IP_Merged_peak_100020                                 73   \n",
       "SC_Control_IP_Merged_peak_100028                                 71   \n",
       "SC_Control_IP_Merged_peak_10003                                  90   \n",
       "\n",
       "                                                    SC_Control_3_IP  \\\n",
       "Peak_Name                                                             \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...              209   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...              172   \n",
       "SC_Control_IP_Merged_peak_100020                                 86   \n",
       "SC_Control_IP_Merged_peak_100028                                 93   \n",
       "SC_Control_IP_Merged_peak_10003                                  84   \n",
       "\n",
       "                                                    SC_VitC_1_IP  \\\n",
       "Peak_Name                                                          \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...            96   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...           116   \n",
       "SC_Control_IP_Merged_peak_100020                              56   \n",
       "SC_Control_IP_Merged_peak_100028                              50   \n",
       "SC_Control_IP_Merged_peak_10003                               53   \n",
       "\n",
       "                                                    SC_VitC_2_IP  SC_VitC_3_IP  \n",
       "Peak_Name                                                                       \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...            89            86  \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...           122           130  \n",
       "SC_Control_IP_Merged_peak_100020                              50            67  \n",
       "SC_Control_IP_Merged_peak_100028                              49            57  \n",
       "SC_Control_IP_Merged_peak_10003                               76            69  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(SC_VitC_hMeDIP_raw))\n",
    "SC_VitC_hMeDIP_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dave notes, axis = 0 means column wise; axis = 1 means row wise\n",
    "# so a sum of axis=0 means the counts for the entire column\n",
    "total_counts_SC_VitC_hMeDIP_Raw = SC_VitC_hMeDIP_raw.sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SC_Control_1_IP    86086627\n",
       "SC_Control_2_IP    77868883\n",
       "SC_Control_3_IP    92722840\n",
       "SC_VitC_1_IP       84388035\n",
       "SC_VitC_2_IP       89722376\n",
       "SC_VitC_3_IP       97792960\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_counts_SC_VitC_hMeDIP_Raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86086627"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_counts_SC_VitC_hMeDIP_Raw[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_hMeDIP_raw[\"SC_VitC_Control_1_IP_First_RCPM\"] = SC_VitC_hMeDIP_raw[\"SC_Control_1_IP\"]*1000000/total_counts_SC_VitC_hMeDIP_Raw[0]\n",
    "SC_VitC_hMeDIP_raw[\"SC_VitC_Control_2_IP_First_RCPM\"] = SC_VitC_hMeDIP_raw[\"SC_Control_2_IP\"]*1000000/total_counts_SC_VitC_hMeDIP_Raw[1]\n",
    "SC_VitC_hMeDIP_raw[\"SC_VitC_Control_3_IP_First_RCPM\"] = SC_VitC_hMeDIP_raw[\"SC_Control_3_IP\"]*1000000/total_counts_SC_VitC_hMeDIP_Raw[2]\n",
    "\n",
    "SC_VitC_hMeDIP_raw[\"SC_VitC_VitC_1_IP_First_RCPM\"] = SC_VitC_hMeDIP_raw[\"SC_VitC_1_IP\"]*1000000/total_counts_SC_VitC_hMeDIP_Raw[3]\n",
    "SC_VitC_hMeDIP_raw[\"SC_VitC_VitC_2_IP_First_RCPM\"] = SC_VitC_hMeDIP_raw[\"SC_VitC_2_IP\"]*1000000/total_counts_SC_VitC_hMeDIP_Raw[4]\n",
    "SC_VitC_hMeDIP_raw[\"SC_VitC_VitC_3_IP_First_RCPM\"] = SC_VitC_hMeDIP_raw[\"SC_VitC_3_IP\"]*1000000/total_counts_SC_VitC_hMeDIP_Raw[5]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "SC_VitC_hMeDIP_raw[\"Control_Average\"] = SC_VitC_hMeDIP_raw[[\"SC_VitC_Control_1_IP_First_RCPM\",\"SC_VitC_Control_2_IP_First_RCPM\", \"SC_VitC_Control_3_IP_First_RCPM\"]].mean(axis=1)\n",
    "SC_VitC_hMeDIP_raw[\"VitC_Average\"] = SC_VitC_hMeDIP_raw[[\"SC_VitC_VitC_1_IP_First_RCPM\",\"SC_VitC_VitC_2_IP_First_RCPM\", \"SC_VitC_VitC_3_IP_First_RCPM\"]].mean(axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>SC_Control_1_IP</th>\n",
       "      <th>SC_Control_2_IP</th>\n",
       "      <th>SC_Control_3_IP</th>\n",
       "      <th>SC_VitC_1_IP</th>\n",
       "      <th>SC_VitC_2_IP</th>\n",
       "      <th>SC_VitC_3_IP</th>\n",
       "      <th>SC_VitC_Control_1_IP_First_RCPM</th>\n",
       "      <th>SC_VitC_Control_2_IP_First_RCPM</th>\n",
       "      <th>SC_VitC_Control_3_IP_First_RCPM</th>\n",
       "      <th>SC_VitC_VitC_1_IP_First_RCPM</th>\n",
       "      <th>SC_VitC_VitC_2_IP_First_RCPM</th>\n",
       "      <th>SC_VitC_VitC_3_IP_First_RCPM</th>\n",
       "      <th>Control_Average</th>\n",
       "      <th>VitC_Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peak_Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merged_peak_92032</th>\n",
       "      <td>153</td>\n",
       "      <td>153</td>\n",
       "      <td>209</td>\n",
       "      <td>96</td>\n",
       "      <td>89</td>\n",
       "      <td>86</td>\n",
       "      <td>1.777280</td>\n",
       "      <td>1.964841</td>\n",
       "      <td>2.254029</td>\n",
       "      <td>1.137602</td>\n",
       "      <td>0.991949</td>\n",
       "      <td>0.879409</td>\n",
       "      <td>1.998717</td>\n",
       "      <td>1.002987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merged_peak_92041</th>\n",
       "      <td>119</td>\n",
       "      <td>112</td>\n",
       "      <td>172</td>\n",
       "      <td>116</td>\n",
       "      <td>122</td>\n",
       "      <td>130</td>\n",
       "      <td>1.382329</td>\n",
       "      <td>1.438315</td>\n",
       "      <td>1.854991</td>\n",
       "      <td>1.374602</td>\n",
       "      <td>1.359750</td>\n",
       "      <td>1.329339</td>\n",
       "      <td>1.558545</td>\n",
       "      <td>1.354564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100020</th>\n",
       "      <td>72</td>\n",
       "      <td>73</td>\n",
       "      <td>86</td>\n",
       "      <td>56</td>\n",
       "      <td>50</td>\n",
       "      <td>67</td>\n",
       "      <td>0.836367</td>\n",
       "      <td>0.937473</td>\n",
       "      <td>0.927495</td>\n",
       "      <td>0.663601</td>\n",
       "      <td>0.557275</td>\n",
       "      <td>0.685121</td>\n",
       "      <td>0.900445</td>\n",
       "      <td>0.635332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100028</th>\n",
       "      <td>66</td>\n",
       "      <td>71</td>\n",
       "      <td>93</td>\n",
       "      <td>50</td>\n",
       "      <td>49</td>\n",
       "      <td>57</td>\n",
       "      <td>0.766670</td>\n",
       "      <td>0.911789</td>\n",
       "      <td>1.002989</td>\n",
       "      <td>0.592501</td>\n",
       "      <td>0.546129</td>\n",
       "      <td>0.582864</td>\n",
       "      <td>0.893816</td>\n",
       "      <td>0.573831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_10003</th>\n",
       "      <td>73</td>\n",
       "      <td>90</td>\n",
       "      <td>84</td>\n",
       "      <td>53</td>\n",
       "      <td>76</td>\n",
       "      <td>69</td>\n",
       "      <td>0.847983</td>\n",
       "      <td>1.155789</td>\n",
       "      <td>0.905926</td>\n",
       "      <td>0.628051</td>\n",
       "      <td>0.847057</td>\n",
       "      <td>0.705572</td>\n",
       "      <td>0.969899</td>\n",
       "      <td>0.726894</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    SC_Control_1_IP  \\\n",
       "Peak_Name                                                             \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...              153   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...              119   \n",
       "SC_Control_IP_Merged_peak_100020                                 72   \n",
       "SC_Control_IP_Merged_peak_100028                                 66   \n",
       "SC_Control_IP_Merged_peak_10003                                  73   \n",
       "\n",
       "                                                    SC_Control_2_IP  \\\n",
       "Peak_Name                                                             \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...              153   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...              112   \n",
       "SC_Control_IP_Merged_peak_100020                                 73   \n",
       "SC_Control_IP_Merged_peak_100028                                 71   \n",
       "SC_Control_IP_Merged_peak_10003                                  90   \n",
       "\n",
       "                                                    SC_Control_3_IP  \\\n",
       "Peak_Name                                                             \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...              209   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...              172   \n",
       "SC_Control_IP_Merged_peak_100020                                 86   \n",
       "SC_Control_IP_Merged_peak_100028                                 93   \n",
       "SC_Control_IP_Merged_peak_10003                                  84   \n",
       "\n",
       "                                                    SC_VitC_1_IP  \\\n",
       "Peak_Name                                                          \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...            96   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...           116   \n",
       "SC_Control_IP_Merged_peak_100020                              56   \n",
       "SC_Control_IP_Merged_peak_100028                              50   \n",
       "SC_Control_IP_Merged_peak_10003                               53   \n",
       "\n",
       "                                                    SC_VitC_2_IP  \\\n",
       "Peak_Name                                                          \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...            89   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...           122   \n",
       "SC_Control_IP_Merged_peak_100020                              50   \n",
       "SC_Control_IP_Merged_peak_100028                              49   \n",
       "SC_Control_IP_Merged_peak_10003                               76   \n",
       "\n",
       "                                                    SC_VitC_3_IP  \\\n",
       "Peak_Name                                                          \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...            86   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...           130   \n",
       "SC_Control_IP_Merged_peak_100020                              67   \n",
       "SC_Control_IP_Merged_peak_100028                              57   \n",
       "SC_Control_IP_Merged_peak_10003                               69   \n",
       "\n",
       "                                                    SC_VitC_Control_1_IP_First_RCPM  \\\n",
       "Peak_Name                                                                             \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...                         1.777280   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...                         1.382329   \n",
       "SC_Control_IP_Merged_peak_100020                                           0.836367   \n",
       "SC_Control_IP_Merged_peak_100028                                           0.766670   \n",
       "SC_Control_IP_Merged_peak_10003                                            0.847983   \n",
       "\n",
       "                                                    SC_VitC_Control_2_IP_First_RCPM  \\\n",
       "Peak_Name                                                                             \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...                         1.964841   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...                         1.438315   \n",
       "SC_Control_IP_Merged_peak_100020                                           0.937473   \n",
       "SC_Control_IP_Merged_peak_100028                                           0.911789   \n",
       "SC_Control_IP_Merged_peak_10003                                            1.155789   \n",
       "\n",
       "                                                    SC_VitC_Control_3_IP_First_RCPM  \\\n",
       "Peak_Name                                                                             \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...                         2.254029   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...                         1.854991   \n",
       "SC_Control_IP_Merged_peak_100020                                           0.927495   \n",
       "SC_Control_IP_Merged_peak_100028                                           1.002989   \n",
       "SC_Control_IP_Merged_peak_10003                                            0.905926   \n",
       "\n",
       "                                                    SC_VitC_VitC_1_IP_First_RCPM  \\\n",
       "Peak_Name                                                                          \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...                      1.137602   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...                      1.374602   \n",
       "SC_Control_IP_Merged_peak_100020                                        0.663601   \n",
       "SC_Control_IP_Merged_peak_100028                                        0.592501   \n",
       "SC_Control_IP_Merged_peak_10003                                         0.628051   \n",
       "\n",
       "                                                    SC_VitC_VitC_2_IP_First_RCPM  \\\n",
       "Peak_Name                                                                          \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...                      0.991949   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...                      1.359750   \n",
       "SC_Control_IP_Merged_peak_100020                                        0.557275   \n",
       "SC_Control_IP_Merged_peak_100028                                        0.546129   \n",
       "SC_Control_IP_Merged_peak_10003                                         0.847057   \n",
       "\n",
       "                                                    SC_VitC_VitC_3_IP_First_RCPM  \\\n",
       "Peak_Name                                                                          \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...                      0.879409   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...                      1.329339   \n",
       "SC_Control_IP_Merged_peak_100020                                        0.685121   \n",
       "SC_Control_IP_Merged_peak_100028                                        0.582864   \n",
       "SC_Control_IP_Merged_peak_10003                                         0.705572   \n",
       "\n",
       "                                                    Control_Average  \\\n",
       "Peak_Name                                                             \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...         1.998717   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...         1.558545   \n",
       "SC_Control_IP_Merged_peak_100020                           0.900445   \n",
       "SC_Control_IP_Merged_peak_100028                           0.893816   \n",
       "SC_Control_IP_Merged_peak_10003                            0.969899   \n",
       "\n",
       "                                                    VitC_Average  \n",
       "Peak_Name                                                         \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...      1.002987  \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...      1.354564  \n",
       "SC_Control_IP_Merged_peak_100020                        0.635332  \n",
       "SC_Control_IP_Merged_peak_100028                        0.573831  \n",
       "SC_Control_IP_Merged_peak_10003                         0.726894  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_hMeDIP_raw.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Control_Average</th>\n",
       "      <th>VitC_Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>215956.000000</td>\n",
       "      <td>215956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.630573</td>\n",
       "      <td>4.630573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1447.013116</td>\n",
       "      <td>1247.218434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.751918</td>\n",
       "      <td>0.903140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.128989</td>\n",
       "      <td>1.414585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.848437</td>\n",
       "      <td>2.354225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>672443.674108</td>\n",
       "      <td>579597.048471</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Control_Average   VitC_Average\n",
       "count    215956.000000  215956.000000\n",
       "mean          4.630573       4.630573\n",
       "std        1447.013116    1247.218434\n",
       "min           0.000000       0.000000\n",
       "25%           0.751918       0.903140\n",
       "50%           1.128989       1.414585\n",
       "75%           1.848437       2.354225\n",
       "max      672443.674108  579597.048471"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dave Notes\n",
    "# The sum of the FPKM columns is always going to be 1,000,000 (when I include all rows) because that is how I normalized it\n",
    "# 1M/215956 gives the 4.630573 average\n",
    "\n",
    "SC_VitC_hMeDIP_raw[[\"Control_Average\",\"VitC_Average\" ]].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SC_Control_1_IP                    86086627.0\n",
       "SC_Control_2_IP                    77868883.0\n",
       "SC_Control_3_IP                    92722840.0\n",
       "SC_VitC_1_IP                       84388035.0\n",
       "SC_VitC_2_IP                       89722376.0\n",
       "SC_VitC_3_IP                       97792960.0\n",
       "SC_VitC_Control_1_IP_First_RCPM     1000000.0\n",
       "SC_VitC_Control_2_IP_First_RCPM     1000000.0\n",
       "SC_VitC_Control_3_IP_First_RCPM     1000000.0\n",
       "SC_VitC_VitC_1_IP_First_RCPM        1000000.0\n",
       "SC_VitC_VitC_2_IP_First_RCPM        1000000.0\n",
       "SC_VitC_VitC_3_IP_First_RCPM        1000000.0\n",
       "Control_Average                     1000000.0\n",
       "VitC_Average                        1000000.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_hMeDIP_raw.sum(axis=0) # should be 1M for all normalized columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I have read in the hMeDIP count files and done the first normalization, a read count per million (RCPM). This does not take out any outliers, nor does it take out any of the columns that are added by htseq-count that are reads not within annotated features. Notice that the RCPM files all add up to 1,000,000 which is why this is read counts per million. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step will be to determine if there are any outliers that should be taken out of the normalization. I will determine outliers without the htseq columns, but they will be added back in for the final RCPM normalization. For this step, I run a linear regression of the averages per treatment type (vitamin C and control). I look for a cook's cutoff value (usually a value of 1 is used), and then I remove those points that have a high cooks value for the normalization. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_added_by_htseq = [\"__no_feature\", \"__ambiguous\", \"__too_low_aQual\", \"__not_aligned\", \"__not_aligned\", \"__alignment_not_unique\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_hMeDIP_first_RCPM_averages = SC_VitC_hMeDIP_raw[[\"Control_Average\",\"VitC_Average\" ]].drop(columns_added_by_htseq, axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "215951"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(SC_VitC_hMeDIP_first_RCPM_averages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Control_Average</th>\n",
       "      <th>VitC_Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peak_Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merged_peak_108098,SC_Control_IP_Merged_peak_108099</th>\n",
       "      <td>3.928521</td>\n",
       "      <td>5.171365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_LAA_IP_Merged_peak_99990</th>\n",
       "      <td>0.577147</td>\n",
       "      <td>1.013082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_LAA_IP_Merged_peak_99994</th>\n",
       "      <td>0.704897</td>\n",
       "      <td>1.050162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_LAA_IP_Merged_peak_99995</th>\n",
       "      <td>0.672715</td>\n",
       "      <td>1.438460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_LAA_IP_Merged_peak_99999</th>\n",
       "      <td>0.676871</td>\n",
       "      <td>1.259336</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Control_Average  \\\n",
       "Peak_Name                                                             \n",
       "SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merge...         3.928521   \n",
       "SC_LAA_IP_Merged_peak_99990                                0.577147   \n",
       "SC_LAA_IP_Merged_peak_99994                                0.704897   \n",
       "SC_LAA_IP_Merged_peak_99995                                0.672715   \n",
       "SC_LAA_IP_Merged_peak_99999                                0.676871   \n",
       "\n",
       "                                                    VitC_Average  \n",
       "Peak_Name                                                         \n",
       "SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merge...      5.171365  \n",
       "SC_LAA_IP_Merged_peak_99990                             1.013082  \n",
       "SC_LAA_IP_Merged_peak_99994                             1.050162  \n",
       "SC_LAA_IP_Merged_peak_99995                             1.438460  \n",
       "SC_LAA_IP_Merged_peak_99999                             1.259336  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_hMeDIP_first_RCPM_averages.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Control_Average</th>\n",
       "      <th>VitC_Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>215951.000000</td>\n",
       "      <td>215951.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.516772</td>\n",
       "      <td>1.946717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.289185</td>\n",
       "      <td>1.799536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.114734</td>\n",
       "      <td>0.033293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.751923</td>\n",
       "      <td>0.903152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.128992</td>\n",
       "      <td>1.414600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.848421</td>\n",
       "      <td>2.354188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>38.952486</td>\n",
       "      <td>74.566169</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Control_Average   VitC_Average\n",
       "count    215951.000000  215951.000000\n",
       "mean          1.516772       1.946717\n",
       "std           1.289185       1.799536\n",
       "min           0.114734       0.033293\n",
       "25%           0.751923       0.903152\n",
       "50%           1.128992       1.414600\n",
       "75%           1.848421       2.354188\n",
       "max          38.952486      74.566169"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_hMeDIP_first_RCPM_averages.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like the mean for vitamin C is slightly higher, but that is not too surprising because vitamin C leads to a global increase in 5hmC. However, the minimum is lower in the vitamin C treated. I have seen this in all the other datasets too."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I have to make a linear regression using the averages and figure out which ones are outliers.\n",
    "<br>I will not graph it using the ones that start with \"_\" because they need to be included in normalization because it is immunoprecipitation and this makes it include reads not within peaks.\n",
    "<br>I am choosing to use cook's cutoff to determine outliers because it takes both X and Y axes into consideration, and it is used by DESeq2 for their normalization. Additionally, it calculates a P-value giving an easier time to determining a cutoff and it runs quickly. I am going to exclude points where Cook's distance > 1 because this looks correct in the graph and this is the default for DESeq2.   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports and setup \n",
    "import pandas as pd\n",
    "import scipy as sc\n",
    "import numpy as np\n",
    "\n",
    "import statsmodels.formula.api as sm\n",
    "import statsmodels as stats\n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "import matplotlib as mpl\n",
    "plt.style.use('ggplot')\n",
    "%matplotlib inline  \n",
    "plt.rcParams['figure.figsize'] = (10, 6) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.stats import outliers_influence\n",
    "import statsmodels.stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>VitC_Average</td>   <th>  R-squared:         </th>  <td>   0.761</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.761</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>6.891e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 16 Apr 2019</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>20:48:56</td>     <th>  Log-Likelihood:    </th> <td>-2.7857e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>215951</td>      <th>  AIC:               </th>  <td>5.571e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>215949</td>      <th>  BIC:               </th>  <td>5.572e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>       <td>    0.0993</td> <td>    0.003</td> <td>   33.986</td> <td> 0.000</td> <td>    0.094</td> <td>    0.105</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Control_Average</th> <td>    1.2180</td> <td>    0.001</td> <td>  830.145</td> <td> 0.000</td> <td>    1.215</td> <td>    1.221</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>98885.946</td> <th>  Durbin-Watson:     </th>  <td>   1.219</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>6466490.506</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 1.381</td>   <th>  Prob(JB):          </th>  <td>    0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>29.665</td>   <th>  Cond. No.          </th>  <td>    3.57</td>  \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:           VitC_Average   R-squared:                       0.761\n",
       "Model:                            OLS   Adj. R-squared:                  0.761\n",
       "Method:                 Least Squares   F-statistic:                 6.891e+05\n",
       "Date:                Tue, 16 Apr 2019   Prob (F-statistic):               0.00\n",
       "Time:                        20:48:56   Log-Likelihood:            -2.7857e+05\n",
       "No. Observations:              215951   AIC:                         5.571e+05\n",
       "Df Residuals:                  215949   BIC:                         5.572e+05\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===================================================================================\n",
       "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------\n",
       "Intercept           0.0993      0.003     33.986      0.000       0.094       0.105\n",
       "Control_Average     1.2180      0.001    830.145      0.000       1.215       1.221\n",
       "==============================================================================\n",
       "Omnibus:                    98885.946   Durbin-Watson:                   1.219\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          6466490.506\n",
       "Skew:                           1.381   Prob(JB):                         0.00\n",
       "Kurtosis:                      29.665   Cond. No.                         3.57\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "linear_model = sm.ols(formula=\"VitC_Average ~ Control_Average\", data=SC_VitC_hMeDIP_first_RCPM_averages).fit()\n",
    "linear_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "influence = linear_model.get_influence()\n",
    "(cook,p_cook) = influence.cooks_distance\n",
    "SC_VitC_hMeDIP_first_RCPM_averages[\"Cook_Value\"] = cook\n",
    "SC_VitC_hMeDIP_first_RCPM_averages[\"Cook_P_Value\"] = p_cook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Control_Average</th>\n",
       "      <th>VitC_Average</th>\n",
       "      <th>Cook_Value</th>\n",
       "      <th>Cook_P_Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peak_Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merged_peak_108098,SC_Control_IP_Merged_peak_108099</th>\n",
       "      <td>3.928521</td>\n",
       "      <td>5.171365</td>\n",
       "      <td>1.111452e-06</td>\n",
       "      <td>0.999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_LAA_IP_Merged_peak_99990</th>\n",
       "      <td>0.577147</td>\n",
       "      <td>1.013082</td>\n",
       "      <td>2.039855e-07</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_LAA_IP_Merged_peak_99994</th>\n",
       "      <td>0.704897</td>\n",
       "      <td>1.050162</td>\n",
       "      <td>3.567120e-08</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_LAA_IP_Merged_peak_99995</th>\n",
       "      <td>0.672715</td>\n",
       "      <td>1.438460</td>\n",
       "      <td>1.156832e-06</td>\n",
       "      <td>0.999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_LAA_IP_Merged_peak_99999</th>\n",
       "      <td>0.676871</td>\n",
       "      <td>1.259336</td>\n",
       "      <td>4.808550e-07</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Control_Average  \\\n",
       "Peak_Name                                                             \n",
       "SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merge...         3.928521   \n",
       "SC_LAA_IP_Merged_peak_99990                                0.577147   \n",
       "SC_LAA_IP_Merged_peak_99994                                0.704897   \n",
       "SC_LAA_IP_Merged_peak_99995                                0.672715   \n",
       "SC_LAA_IP_Merged_peak_99999                                0.676871   \n",
       "\n",
       "                                                    VitC_Average  \\\n",
       "Peak_Name                                                          \n",
       "SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merge...      5.171365   \n",
       "SC_LAA_IP_Merged_peak_99990                             1.013082   \n",
       "SC_LAA_IP_Merged_peak_99994                             1.050162   \n",
       "SC_LAA_IP_Merged_peak_99995                             1.438460   \n",
       "SC_LAA_IP_Merged_peak_99999                             1.259336   \n",
       "\n",
       "                                                      Cook_Value  Cook_P_Value  \n",
       "Peak_Name                                                                       \n",
       "SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merge...  1.111452e-06      0.999999  \n",
       "SC_LAA_IP_Merged_peak_99990                         2.039855e-07      1.000000  \n",
       "SC_LAA_IP_Merged_peak_99994                         3.567120e-08      1.000000  \n",
       "SC_LAA_IP_Merged_peak_99995                         1.156832e-06      0.999999  \n",
       "SC_LAA_IP_Merged_peak_99999                         4.808550e-07      1.000000  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_hMeDIP_first_RCPM_averages.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_hMeDIP_removing_outliers = SC_VitC_hMeDIP_first_RCPM_averages[SC_VitC_hMeDIP_first_RCPM_averages.Cook_Value <= 1]\n",
    "SC_VitC_hMeDIP_outliers = SC_VitC_hMeDIP_first_RCPM_averages[SC_VitC_hMeDIP_first_RCPM_averages.Cook_Value > 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Control_Average</th>\n",
       "      <th>VitC_Average</th>\n",
       "      <th>Cook_Value</th>\n",
       "      <th>Cook_P_Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peak_Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SC_LAA_IP_Merged_peak_398730,SC_Control_IP_Merged_peak_435301,SC_LAA_IP_Merged_peak_398731,SC_Control_IP_Merged_peak_435302,SC_Control_IP_Merged_peak_435303,SC_Control_IP_Merged_peak_435306,SC_Control_IP_Merged_peak_435309,SC_Control_IP_Merged_peak_435311,SC_Control_IP_Merged_peak_435312,SC_Control_IP_Merged_peak_435313</th>\n",
       "      <td>38.952486</td>\n",
       "      <td>74.566169</td>\n",
       "      <td>1.861793</td>\n",
       "      <td>0.155396</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Control_Average  \\\n",
       "Peak_Name                                                             \n",
       "SC_LAA_IP_Merged_peak_398730,SC_Control_IP_Merg...        38.952486   \n",
       "\n",
       "                                                    VitC_Average  Cook_Value  \\\n",
       "Peak_Name                                                                      \n",
       "SC_LAA_IP_Merged_peak_398730,SC_Control_IP_Merg...     74.566169    1.861793   \n",
       "\n",
       "                                                    Cook_P_Value  \n",
       "Peak_Name                                                         \n",
       "SC_LAA_IP_Merged_peak_398730,SC_Control_IP_Merg...      0.155396  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_hMeDIP_outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>VitC_Average</td>   <th>  R-squared:         </th>  <td>   0.761</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.761</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>6.863e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 16 Apr 2019</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>20:48:56</td>     <th>  Log-Likelihood:    </th> <td>-2.7809e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>215950</td>      <th>  AIC:               </th>  <td>5.562e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>215948</td>      <th>  BIC:               </th>  <td>5.562e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>       <td>    0.1034</td> <td>    0.003</td> <td>   35.452</td> <td> 0.000</td> <td>    0.098</td> <td>    0.109</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Control_Average</th> <td>    1.2152</td> <td>    0.001</td> <td>  828.418</td> <td> 0.000</td> <td>    1.212</td> <td>    1.218</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>91835.391</td> <th>  Durbin-Watson:     </th>  <td>   1.219</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>4794693.889</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 1.282</td>   <th>  Prob(JB):          </th>  <td>    0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>25.941</td>   <th>  Cond. No.          </th>  <td>    3.57</td>  \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:           VitC_Average   R-squared:                       0.761\n",
       "Model:                            OLS   Adj. R-squared:                  0.761\n",
       "Method:                 Least Squares   F-statistic:                 6.863e+05\n",
       "Date:                Tue, 16 Apr 2019   Prob (F-statistic):               0.00\n",
       "Time:                        20:48:56   Log-Likelihood:            -2.7809e+05\n",
       "No. Observations:              215950   AIC:                         5.562e+05\n",
       "Df Residuals:                  215948   BIC:                         5.562e+05\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===================================================================================\n",
       "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------\n",
       "Intercept           0.1034      0.003     35.452      0.000       0.098       0.109\n",
       "Control_Average     1.2152      0.001    828.418      0.000       1.212       1.218\n",
       "==============================================================================\n",
       "Omnibus:                    91835.391   Durbin-Watson:                   1.219\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          4794693.889\n",
       "Skew:                           1.282   Prob(JB):                         0.00\n",
       "Kurtosis:                      25.941   Cond. No.                         3.57\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outliers_removed_model = sm.ols(formula=\"VitC_Average ~ Control_Average\", data=SC_VitC_hMeDIP_removing_outliers).fit()\n",
    "outliers_removed_model.summary()\n",
    "# Same R-2 value, but there was only one outlier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the statsmodels website: http://www.statsmodels.org/dev/generated/statsmodels.stats.outliers_influence.OLSInfluence.summary_frame.html\n",
    "<br>cooks_d : Cook’s Distance defined in Influence.cooks_distance\n",
    "<br>standard_resid : Standardized residuals defined in Influence.resid_studentized_internal\n",
    "<br>hat_diag : The diagonal of the projection, or hat, matrix defined in Influence.hat_matrix_diag\n",
    "<br>dffits_internal : DFFITS statistics using internally Studentized residuals defined in Influence.dffits_internal\n",
    "<br>dffits : DFFITS statistics using externally Studentized residuals defined in Influence.dffits\n",
    "<br>student_resid : Externally Studentized residuals defined in Influence.resid_studentized_external"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#outliers = ARPE_hMeDIP_outliers[\"VitC_Average\"].values\n",
    "fig, tmp = plt.subplots()\n",
    "ax = SC_VitC_hMeDIP_removing_outliers.plot.scatter(y = \"VitC_Average\", x = \"Control_Average\", \n",
    "                                                ax=tmp, color = 'k', label = \"Not Outliers\", s = 10)\n",
    "ax.scatter(y = SC_VitC_hMeDIP_outliers[\"VitC_Average\"], x = SC_VitC_hMeDIP_outliers[\"Control_Average\"], \n",
    "           color = 'r', label=\"Outliers\", s = 10)\n",
    "\n",
    "x_straight = sc.linspace(0, 350, 10)\n",
    "y_straight = x_straight # I want this line to be a straight line with a slope of 1\n",
    "\n",
    "plt.title(\"Linear Regression hMeDIP Control vs Vit C\", color='black')\n",
    "plt.ylabel(\"Vitamin C\", color='black')\n",
    "plt.xlabel(\"Control\", color='black')\n",
    "ax.set_facecolor('white')\n",
    "ax.tick_params(color='black', labelcolor='black')\n",
    "ax.spines['bottom'].set_color('black')\n",
    "ax.spines['left'].set_color('black')\n",
    "ax.grid(color='lightslategray', linestyle='--', linewidth=0.5, alpha=0.4)\n",
    "plt.tick_params(axis = 'both', which = 'major', labelsize='10')\n",
    "\n",
    "parameters = linear_model.params\n",
    "x = sc.linspace(0, 350, 10)\n",
    "y = parameters[0] + parameters[1]*x\n",
    "\n",
    "outliers_removed_model\n",
    "outlier_parameters = outliers_removed_model.params\n",
    "#x = sc.linspace(0, 350, 10)\n",
    "outlier_y = outlier_parameters[0] + outlier_parameters[1]*x\n",
    "\n",
    "\n",
    "plt.plot(x_straight,y_straight,label=\"Slope = 1\", linewidth=2, color='k')\n",
    "plt.plot(x,y,label=\"Regression Line\", linewidth=2, color='r')\n",
    "plt.plot(x,outlier_y,label=\"Removing Outliers Regression Line\", linewidth=2, color='g')\n",
    "plt.legend(loc='upper left')\n",
    "plt.savefig(Output_Path+\"SC_VitC_hMeDIP_Finding_Outliers.png\", dpi=300) # size above specified at 10x6\n",
    "plt.show()\n",
    "#plt.savefig(Output_Path+\"ARPE_hMeDIP_Finding_Outliers.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "remove_outliers = SC_VitC_hMeDIP_raw.drop(SC_VitC_hMeDIP_outliers.index, axis = 0)\n",
    "total_counts_removing_outliers = remove_outliers.sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SC_Control_1_IP                    8.608344e+07\n",
       "SC_Control_2_IP                    7.786615e+07\n",
       "SC_Control_3_IP                    9.271869e+07\n",
       "SC_VitC_1_IP                       8.438199e+07\n",
       "SC_VitC_2_IP                       8.971516e+07\n",
       "SC_VitC_3_IP                       9.778596e+07\n",
       "SC_VitC_Control_1_IP_First_RCPM    9.999630e+05\n",
       "SC_VitC_Control_2_IP_First_RCPM    9.999649e+05\n",
       "SC_VitC_Control_3_IP_First_RCPM    9.999553e+05\n",
       "SC_VitC_VitC_1_IP_First_RCPM       9.999283e+05\n",
       "SC_VitC_VitC_2_IP_First_RCPM       9.999196e+05\n",
       "SC_VitC_VitC_3_IP_First_RCPM       9.999284e+05\n",
       "Control_Average                    9.999610e+05\n",
       "VitC_Average                       9.999254e+05\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_counts_removing_outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LAA means Vitamin C. I am changing the name out for VitC because more people will recognize it\n",
    "SC_VitC_hMeDIP_raw[\"Control_1_Corrected_RCPM\"]= SC_VitC_hMeDIP_raw[\"SC_Control_1_IP\"]*1000000/total_counts_removing_outliers[0]\n",
    "SC_VitC_hMeDIP_raw[\"Control_2_Corrected_RCPM\"]= SC_VitC_hMeDIP_raw[\"SC_Control_2_IP\"]*1000000/total_counts_removing_outliers[1]\n",
    "SC_VitC_hMeDIP_raw[\"Control_3_Corrected_RCPM\"]= SC_VitC_hMeDIP_raw[\"SC_Control_3_IP\"]*1000000/total_counts_removing_outliers[2]\n",
    "\n",
    "SC_VitC_hMeDIP_raw[\"VitC_1_Corrected_RCPM\"]= SC_VitC_hMeDIP_raw[\"SC_VitC_1_IP\"]*1000000/total_counts_removing_outliers[3]\n",
    "SC_VitC_hMeDIP_raw[\"VitC_2_Corrected_RCPM\"]= SC_VitC_hMeDIP_raw[\"SC_VitC_2_IP\"]*1000000/total_counts_removing_outliers[4]\n",
    "SC_VitC_hMeDIP_raw[\"VitC_3_Corrected_RCPM\"]= SC_VitC_hMeDIP_raw[\"SC_VitC_3_IP\"]*1000000/total_counts_removing_outliers[5]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_hMeDIP_Corrected_RCPM = SC_VitC_hMeDIP_raw[[\"Control_1_Corrected_RCPM\", \"Control_2_Corrected_RCPM\", \"Control_3_Corrected_RCPM\", \"VitC_1_Corrected_RCPM\", \"VitC_2_Corrected_RCPM\", \"VitC_3_Corrected_RCPM\"]].drop(columns_added_by_htseq, axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "215951"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(SC_VitC_hMeDIP_Corrected_RCPM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Control_1_Corrected_RCPM</th>\n",
       "      <th>Control_2_Corrected_RCPM</th>\n",
       "      <th>Control_3_Corrected_RCPM</th>\n",
       "      <th>VitC_1_Corrected_RCPM</th>\n",
       "      <th>VitC_2_Corrected_RCPM</th>\n",
       "      <th>VitC_3_Corrected_RCPM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peak_Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merged_peak_92032</th>\n",
       "      <td>1.777345</td>\n",
       "      <td>1.964910</td>\n",
       "      <td>2.254130</td>\n",
       "      <td>1.137684</td>\n",
       "      <td>0.992029</td>\n",
       "      <td>0.879472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merged_peak_92041</th>\n",
       "      <td>1.382380</td>\n",
       "      <td>1.438366</td>\n",
       "      <td>1.855074</td>\n",
       "      <td>1.374701</td>\n",
       "      <td>1.359859</td>\n",
       "      <td>1.329434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100020</th>\n",
       "      <td>0.836398</td>\n",
       "      <td>0.937506</td>\n",
       "      <td>0.927537</td>\n",
       "      <td>0.663649</td>\n",
       "      <td>0.557319</td>\n",
       "      <td>0.685170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100028</th>\n",
       "      <td>0.766698</td>\n",
       "      <td>0.911821</td>\n",
       "      <td>1.003034</td>\n",
       "      <td>0.592544</td>\n",
       "      <td>0.546173</td>\n",
       "      <td>0.582906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_10003</th>\n",
       "      <td>0.848014</td>\n",
       "      <td>1.155830</td>\n",
       "      <td>0.905966</td>\n",
       "      <td>0.628096</td>\n",
       "      <td>0.847126</td>\n",
       "      <td>0.705623</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Control_1_Corrected_RCPM  \\\n",
       "Peak_Name                                                                      \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...                  1.777345   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...                  1.382380   \n",
       "SC_Control_IP_Merged_peak_100020                                    0.836398   \n",
       "SC_Control_IP_Merged_peak_100028                                    0.766698   \n",
       "SC_Control_IP_Merged_peak_10003                                     0.848014   \n",
       "\n",
       "                                                    Control_2_Corrected_RCPM  \\\n",
       "Peak_Name                                                                      \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...                  1.964910   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...                  1.438366   \n",
       "SC_Control_IP_Merged_peak_100020                                    0.937506   \n",
       "SC_Control_IP_Merged_peak_100028                                    0.911821   \n",
       "SC_Control_IP_Merged_peak_10003                                     1.155830   \n",
       "\n",
       "                                                    Control_3_Corrected_RCPM  \\\n",
       "Peak_Name                                                                      \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...                  2.254130   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...                  1.855074   \n",
       "SC_Control_IP_Merged_peak_100020                                    0.927537   \n",
       "SC_Control_IP_Merged_peak_100028                                    1.003034   \n",
       "SC_Control_IP_Merged_peak_10003                                     0.905966   \n",
       "\n",
       "                                                    VitC_1_Corrected_RCPM  \\\n",
       "Peak_Name                                                                   \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...               1.137684   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...               1.374701   \n",
       "SC_Control_IP_Merged_peak_100020                                 0.663649   \n",
       "SC_Control_IP_Merged_peak_100028                                 0.592544   \n",
       "SC_Control_IP_Merged_peak_10003                                  0.628096   \n",
       "\n",
       "                                                    VitC_2_Corrected_RCPM  \\\n",
       "Peak_Name                                                                   \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...               0.992029   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...               1.359859   \n",
       "SC_Control_IP_Merged_peak_100020                                 0.557319   \n",
       "SC_Control_IP_Merged_peak_100028                                 0.546173   \n",
       "SC_Control_IP_Merged_peak_10003                                  0.847126   \n",
       "\n",
       "                                                    VitC_3_Corrected_RCPM  \n",
       "Peak_Name                                                                  \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...               0.879472  \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...               1.329434  \n",
       "SC_Control_IP_Merged_peak_100020                                 0.685170  \n",
       "SC_Control_IP_Merged_peak_100028                                 0.582906  \n",
       "SC_Control_IP_Merged_peak_10003                                  0.705623  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_hMeDIP_Corrected_RCPM.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "samples = [\"SC_Control_1_hMeDIP_RCPM\", \"SC_Control_2_hMeDIP_RCPM\", \"SC_Control_3_hMeDIP_RCPM\",\n",
    "           \"SC_VitC_1_hMeDIP_RCPM\", \"SC_VitC_2_hMeDIP_RCPM\", \"SC_VitC_3_hMeDIP_RCPM\"]\n",
    "\n",
    "SC_VitC_hMeDIP_Corrected_RCPM.columns= samples\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SC_Control_1_hMeDIP_RCPM</th>\n",
       "      <th>SC_Control_2_hMeDIP_RCPM</th>\n",
       "      <th>SC_Control_3_hMeDIP_RCPM</th>\n",
       "      <th>SC_VitC_1_hMeDIP_RCPM</th>\n",
       "      <th>SC_VitC_2_hMeDIP_RCPM</th>\n",
       "      <th>SC_VitC_3_hMeDIP_RCPM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peak_Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merged_peak_92032</th>\n",
       "      <td>1.777345</td>\n",
       "      <td>1.964910</td>\n",
       "      <td>2.254130</td>\n",
       "      <td>1.137684</td>\n",
       "      <td>0.992029</td>\n",
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       "      <th>SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merged_peak_92041</th>\n",
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       "      <td>1.438366</td>\n",
       "      <td>1.855074</td>\n",
       "      <td>1.374701</td>\n",
       "      <td>1.359859</td>\n",
       "      <td>1.329434</td>\n",
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       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100020</th>\n",
       "      <td>0.836398</td>\n",
       "      <td>0.937506</td>\n",
       "      <td>0.927537</td>\n",
       "      <td>0.663649</td>\n",
       "      <td>0.557319</td>\n",
       "      <td>0.685170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100028</th>\n",
       "      <td>0.766698</td>\n",
       "      <td>0.911821</td>\n",
       "      <td>1.003034</td>\n",
       "      <td>0.592544</td>\n",
       "      <td>0.546173</td>\n",
       "      <td>0.582906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_10003</th>\n",
       "      <td>0.848014</td>\n",
       "      <td>1.155830</td>\n",
       "      <td>0.905966</td>\n",
       "      <td>0.628096</td>\n",
       "      <td>0.847126</td>\n",
       "      <td>0.705623</td>\n",
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      ],
      "text/plain": [
       "                                                    SC_Control_1_hMeDIP_RCPM  \\\n",
       "Peak_Name                                                                      \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...                  1.777345   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...                  1.382380   \n",
       "SC_Control_IP_Merged_peak_100020                                    0.836398   \n",
       "SC_Control_IP_Merged_peak_100028                                    0.766698   \n",
       "SC_Control_IP_Merged_peak_10003                                     0.848014   \n",
       "\n",
       "                                                    SC_Control_2_hMeDIP_RCPM  \\\n",
       "Peak_Name                                                                      \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...                  1.964910   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...                  1.438366   \n",
       "SC_Control_IP_Merged_peak_100020                                    0.937506   \n",
       "SC_Control_IP_Merged_peak_100028                                    0.911821   \n",
       "SC_Control_IP_Merged_peak_10003                                     1.155830   \n",
       "\n",
       "                                                    SC_Control_3_hMeDIP_RCPM  \\\n",
       "Peak_Name                                                                      \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...                  2.254130   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...                  1.855074   \n",
       "SC_Control_IP_Merged_peak_100020                                    0.927537   \n",
       "SC_Control_IP_Merged_peak_100028                                    1.003034   \n",
       "SC_Control_IP_Merged_peak_10003                                     0.905966   \n",
       "\n",
       "                                                    SC_VitC_1_hMeDIP_RCPM  \\\n",
       "Peak_Name                                                                   \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...               1.137684   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...               1.374701   \n",
       "SC_Control_IP_Merged_peak_100020                                 0.663649   \n",
       "SC_Control_IP_Merged_peak_100028                                 0.592544   \n",
       "SC_Control_IP_Merged_peak_10003                                  0.628096   \n",
       "\n",
       "                                                    SC_VitC_2_hMeDIP_RCPM  \\\n",
       "Peak_Name                                                                   \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...               0.992029   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...               1.359859   \n",
       "SC_Control_IP_Merged_peak_100020                                 0.557319   \n",
       "SC_Control_IP_Merged_peak_100028                                 0.546173   \n",
       "SC_Control_IP_Merged_peak_10003                                  0.847126   \n",
       "\n",
       "                                                    SC_VitC_3_hMeDIP_RCPM  \n",
       "Peak_Name                                                                  \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...               0.879472  \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...               1.329434  \n",
       "SC_Control_IP_Merged_peak_100020                                 0.685170  \n",
       "SC_Control_IP_Merged_peak_100028                                 0.582906  \n",
       "SC_Control_IP_Merged_peak_10003                                  0.705623  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_hMeDIP_Corrected_RCPM.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "SC_VitC_hMeDIP_Corrected_RCPM.to_csv(Output_Path+\"SC_VitC_hMeDIP_RCPM_table.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I officially have excluded outliers from the normalization and made the read counts per million. The next step is to divide by length and multiply by 1000 to get \"Fragments per Kilobase per Million\", also known as FPKM. \n",
    "I have the bed file that has the info about peak length for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/david/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3020: DtypeWarning: Columns (0) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "#SC_VitC_hMeDIP_bed = pd.read_csv(Input_bed_file, sep = \"\\t\", index_col = 3, names=[\"Chromosome\", \"Start\", \"Stop\", \"Peak_Name\"], dtype = {\"Chromosome\" : 'str', \"Start\" : 'int', \"Stop\" : 'int', \"Peak_Name\" : 'str'})\n",
    "SC_VitC_hMeDIP_bed = pd.read_csv(Input_bed_file, sep = \"\\t\", index_col = 3)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Chr</th>\n",
       "      <th>Start</th>\n",
       "      <th>Stop</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peak_Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_3</th>\n",
       "      <td>1</td>\n",
       "      <td>50022</td>\n",
       "      <td>50495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_4</th>\n",
       "      <td>1</td>\n",
       "      <td>110730</td>\n",
       "      <td>111377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_17</th>\n",
       "      <td>1</td>\n",
       "      <td>562156</td>\n",
       "      <td>562456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_23</th>\n",
       "      <td>1</td>\n",
       "      <td>759781</td>\n",
       "      <td>760211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_33</th>\n",
       "      <td>1</td>\n",
       "      <td>1227704</td>\n",
       "      <td>1228255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Chr    Start     Stop\n",
       "Peak_Name                                         \n",
       "SC_Control_IP_Merged_peak_3    1    50022    50495\n",
       "SC_Control_IP_Merged_peak_4    1   110730   111377\n",
       "SC_Control_IP_Merged_peak_17   1   562156   562456\n",
       "SC_Control_IP_Merged_peak_23   1   759781   760211\n",
       "SC_Control_IP_Merged_peak_33   1  1227704  1228255"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_hMeDIP_bed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "215951\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "215951"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(SC_VitC_hMeDIP_bed))\n",
    "len(SC_VitC_hMeDIP_Corrected_RCPM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chr      object\n",
       "Start     int64\n",
       "Stop      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_hMeDIP_bed.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_hMeDIP_bed[\"Width\"] = (SC_VitC_hMeDIP_bed[\"Stop\"] - SC_VitC_hMeDIP_bed[\"Start\"] + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Chr</th>\n",
       "      <th>Start</th>\n",
       "      <th>Stop</th>\n",
       "      <th>Width</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peak_Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_3</th>\n",
       "      <td>1</td>\n",
       "      <td>50022</td>\n",
       "      <td>50495</td>\n",
       "      <td>474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_4</th>\n",
       "      <td>1</td>\n",
       "      <td>110730</td>\n",
       "      <td>111377</td>\n",
       "      <td>648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_17</th>\n",
       "      <td>1</td>\n",
       "      <td>562156</td>\n",
       "      <td>562456</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_23</th>\n",
       "      <td>1</td>\n",
       "      <td>759781</td>\n",
       "      <td>760211</td>\n",
       "      <td>431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_33</th>\n",
       "      <td>1</td>\n",
       "      <td>1227704</td>\n",
       "      <td>1228255</td>\n",
       "      <td>552</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Chr    Start     Stop  Width\n",
       "Peak_Name                                                \n",
       "SC_Control_IP_Merged_peak_3    1    50022    50495    474\n",
       "SC_Control_IP_Merged_peak_4    1   110730   111377    648\n",
       "SC_Control_IP_Merged_peak_17   1   562156   562456    301\n",
       "SC_Control_IP_Merged_peak_23   1   759781   760211    431\n",
       "SC_Control_IP_Merged_peak_33   1  1227704  1228255    552"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_hMeDIP_bed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SC_Control_1_hMeDIP_RCPM</th>\n",
       "      <th>SC_Control_2_hMeDIP_RCPM</th>\n",
       "      <th>SC_Control_3_hMeDIP_RCPM</th>\n",
       "      <th>SC_VitC_1_hMeDIP_RCPM</th>\n",
       "      <th>SC_VitC_2_hMeDIP_RCPM</th>\n",
       "      <th>SC_VitC_3_hMeDIP_RCPM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peak_Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merged_peak_92032</th>\n",
       "      <td>1.777345</td>\n",
       "      <td>1.964910</td>\n",
       "      <td>2.254130</td>\n",
       "      <td>1.137684</td>\n",
       "      <td>0.992029</td>\n",
       "      <td>0.879472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merged_peak_92041</th>\n",
       "      <td>1.382380</td>\n",
       "      <td>1.438366</td>\n",
       "      <td>1.855074</td>\n",
       "      <td>1.374701</td>\n",
       "      <td>1.359859</td>\n",
       "      <td>1.329434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100020</th>\n",
       "      <td>0.836398</td>\n",
       "      <td>0.937506</td>\n",
       "      <td>0.927537</td>\n",
       "      <td>0.663649</td>\n",
       "      <td>0.557319</td>\n",
       "      <td>0.685170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100028</th>\n",
       "      <td>0.766698</td>\n",
       "      <td>0.911821</td>\n",
       "      <td>1.003034</td>\n",
       "      <td>0.592544</td>\n",
       "      <td>0.546173</td>\n",
       "      <td>0.582906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_10003</th>\n",
       "      <td>0.848014</td>\n",
       "      <td>1.155830</td>\n",
       "      <td>0.905966</td>\n",
       "      <td>0.628096</td>\n",
       "      <td>0.847126</td>\n",
       "      <td>0.705623</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    SC_Control_1_hMeDIP_RCPM  \\\n",
       "Peak_Name                                                                      \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...                  1.777345   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...                  1.382380   \n",
       "SC_Control_IP_Merged_peak_100020                                    0.836398   \n",
       "SC_Control_IP_Merged_peak_100028                                    0.766698   \n",
       "SC_Control_IP_Merged_peak_10003                                     0.848014   \n",
       "\n",
       "                                                    SC_Control_2_hMeDIP_RCPM  \\\n",
       "Peak_Name                                                                      \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...                  1.964910   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...                  1.438366   \n",
       "SC_Control_IP_Merged_peak_100020                                    0.937506   \n",
       "SC_Control_IP_Merged_peak_100028                                    0.911821   \n",
       "SC_Control_IP_Merged_peak_10003                                     1.155830   \n",
       "\n",
       "                                                    SC_Control_3_hMeDIP_RCPM  \\\n",
       "Peak_Name                                                                      \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...                  2.254130   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...                  1.855074   \n",
       "SC_Control_IP_Merged_peak_100020                                    0.927537   \n",
       "SC_Control_IP_Merged_peak_100028                                    1.003034   \n",
       "SC_Control_IP_Merged_peak_10003                                     0.905966   \n",
       "\n",
       "                                                    SC_VitC_1_hMeDIP_RCPM  \\\n",
       "Peak_Name                                                                   \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...               1.137684   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...               1.374701   \n",
       "SC_Control_IP_Merged_peak_100020                                 0.663649   \n",
       "SC_Control_IP_Merged_peak_100028                                 0.592544   \n",
       "SC_Control_IP_Merged_peak_10003                                  0.628096   \n",
       "\n",
       "                                                    SC_VitC_2_hMeDIP_RCPM  \\\n",
       "Peak_Name                                                                   \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...               0.992029   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...               1.359859   \n",
       "SC_Control_IP_Merged_peak_100020                                 0.557319   \n",
       "SC_Control_IP_Merged_peak_100028                                 0.546173   \n",
       "SC_Control_IP_Merged_peak_10003                                  0.847126   \n",
       "\n",
       "                                                    SC_VitC_3_hMeDIP_RCPM  \n",
       "Peak_Name                                                                  \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...               0.879472  \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...               1.329434  \n",
       "SC_Control_IP_Merged_peak_100020                                 0.685170  \n",
       "SC_Control_IP_Merged_peak_100028                                 0.582906  \n",
       "SC_Control_IP_Merged_peak_10003                                  0.705623  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_hMeDIP_Corrected_RCPM.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "SC_VitC_hMeDIP_Corrected_RCPM = SC_VitC_hMeDIP_Corrected_RCPM.join(SC_VitC_hMeDIP_bed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SC_Control_1_hMeDIP_RCPM</th>\n",
       "      <th>SC_Control_2_hMeDIP_RCPM</th>\n",
       "      <th>SC_Control_3_hMeDIP_RCPM</th>\n",
       "      <th>SC_VitC_1_hMeDIP_RCPM</th>\n",
       "      <th>SC_VitC_2_hMeDIP_RCPM</th>\n",
       "      <th>SC_VitC_3_hMeDIP_RCPM</th>\n",
       "      <th>Chr</th>\n",
       "      <th>Start</th>\n",
       "      <th>Stop</th>\n",
       "      <th>Width</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peak_Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merged_peak_92032</th>\n",
       "      <td>1.777345</td>\n",
       "      <td>1.964910</td>\n",
       "      <td>2.254130</td>\n",
       "      <td>1.137684</td>\n",
       "      <td>0.992029</td>\n",
       "      <td>0.879472</td>\n",
       "      <td>11</td>\n",
       "      <td>58427350</td>\n",
       "      <td>58428140</td>\n",
       "      <td>791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merged_peak_92041</th>\n",
       "      <td>1.382380</td>\n",
       "      <td>1.438366</td>\n",
       "      <td>1.855074</td>\n",
       "      <td>1.374701</td>\n",
       "      <td>1.359859</td>\n",
       "      <td>1.329434</td>\n",
       "      <td>11</td>\n",
       "      <td>58467708</td>\n",
       "      <td>58468197</td>\n",
       "      <td>490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100020</th>\n",
       "      <td>0.836398</td>\n",
       "      <td>0.937506</td>\n",
       "      <td>0.927537</td>\n",
       "      <td>0.663649</td>\n",
       "      <td>0.557319</td>\n",
       "      <td>0.685170</td>\n",
       "      <td>11</td>\n",
       "      <td>58509719</td>\n",
       "      <td>58510097</td>\n",
       "      <td>379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100028</th>\n",
       "      <td>0.766698</td>\n",
       "      <td>0.911821</td>\n",
       "      <td>1.003034</td>\n",
       "      <td>0.592544</td>\n",
       "      <td>0.546173</td>\n",
       "      <td>0.582906</td>\n",
       "      <td>11</td>\n",
       "      <td>58518547</td>\n",
       "      <td>58518878</td>\n",
       "      <td>332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_10003</th>\n",
       "      <td>0.848014</td>\n",
       "      <td>1.155830</td>\n",
       "      <td>0.905966</td>\n",
       "      <td>0.628096</td>\n",
       "      <td>0.847126</td>\n",
       "      <td>0.705623</td>\n",
       "      <td>1</td>\n",
       "      <td>48456350</td>\n",
       "      <td>48456761</td>\n",
       "      <td>412</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    SC_Control_1_hMeDIP_RCPM  \\\n",
       "Peak_Name                                                                      \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...                  1.777345   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...                  1.382380   \n",
       "SC_Control_IP_Merged_peak_100020                                    0.836398   \n",
       "SC_Control_IP_Merged_peak_100028                                    0.766698   \n",
       "SC_Control_IP_Merged_peak_10003                                     0.848014   \n",
       "\n",
       "                                                    SC_Control_2_hMeDIP_RCPM  \\\n",
       "Peak_Name                                                                      \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...                  1.964910   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...                  1.438366   \n",
       "SC_Control_IP_Merged_peak_100020                                    0.937506   \n",
       "SC_Control_IP_Merged_peak_100028                                    0.911821   \n",
       "SC_Control_IP_Merged_peak_10003                                     1.155830   \n",
       "\n",
       "                                                    SC_Control_3_hMeDIP_RCPM  \\\n",
       "Peak_Name                                                                      \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...                  2.254130   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...                  1.855074   \n",
       "SC_Control_IP_Merged_peak_100020                                    0.927537   \n",
       "SC_Control_IP_Merged_peak_100028                                    1.003034   \n",
       "SC_Control_IP_Merged_peak_10003                                     0.905966   \n",
       "\n",
       "                                                    SC_VitC_1_hMeDIP_RCPM  \\\n",
       "Peak_Name                                                                   \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...               1.137684   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...               1.374701   \n",
       "SC_Control_IP_Merged_peak_100020                                 0.663649   \n",
       "SC_Control_IP_Merged_peak_100028                                 0.592544   \n",
       "SC_Control_IP_Merged_peak_10003                                  0.628096   \n",
       "\n",
       "                                                    SC_VitC_2_hMeDIP_RCPM  \\\n",
       "Peak_Name                                                                   \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...               0.992029   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...               1.359859   \n",
       "SC_Control_IP_Merged_peak_100020                                 0.557319   \n",
       "SC_Control_IP_Merged_peak_100028                                 0.546173   \n",
       "SC_Control_IP_Merged_peak_10003                                  0.847126   \n",
       "\n",
       "                                                    SC_VitC_3_hMeDIP_RCPM Chr  \\\n",
       "Peak_Name                                                                       \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...               0.879472  11   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...               1.329434  11   \n",
       "SC_Control_IP_Merged_peak_100020                                 0.685170  11   \n",
       "SC_Control_IP_Merged_peak_100028                                 0.582906  11   \n",
       "SC_Control_IP_Merged_peak_10003                                  0.705623   1   \n",
       "\n",
       "                                                       Start      Stop  Width  \n",
       "Peak_Name                                                                      \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...  58427350  58428140    791  \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...  58467708  58468197    490  \n",
       "SC_Control_IP_Merged_peak_100020                    58509719  58510097    379  \n",
       "SC_Control_IP_Merged_peak_100028                    58518547  58518878    332  \n",
       "SC_Control_IP_Merged_peak_10003                     48456350  48456761    412  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_hMeDIP_Corrected_RCPM.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_hMeDIP_Corrected_RCPM[\"SC_Control_1_FPKM\"]= SC_VitC_hMeDIP_Corrected_RCPM[\"SC_Control_1_hMeDIP_RCPM\"]*1000/SC_VitC_hMeDIP_Corrected_RCPM[\"Width\"]\n",
    "SC_VitC_hMeDIP_Corrected_RCPM[\"SC_Control_2_FPKM\"]= SC_VitC_hMeDIP_Corrected_RCPM[\"SC_Control_2_hMeDIP_RCPM\"]*1000/SC_VitC_hMeDIP_Corrected_RCPM[\"Width\"]\n",
    "SC_VitC_hMeDIP_Corrected_RCPM[\"SC_Control_3_FPKM\"]= SC_VitC_hMeDIP_Corrected_RCPM[\"SC_Control_3_hMeDIP_RCPM\"]*1000/SC_VitC_hMeDIP_Corrected_RCPM[\"Width\"]\n",
    "\n",
    "SC_VitC_hMeDIP_Corrected_RCPM[\"SC_VitC_1_FPKM\"]= SC_VitC_hMeDIP_Corrected_RCPM[\"SC_VitC_1_hMeDIP_RCPM\"]*1000/SC_VitC_hMeDIP_Corrected_RCPM[\"Width\"]\n",
    "SC_VitC_hMeDIP_Corrected_RCPM[\"SC_VitC_2_FPKM\"]= SC_VitC_hMeDIP_Corrected_RCPM[\"SC_VitC_2_hMeDIP_RCPM\"]*1000/SC_VitC_hMeDIP_Corrected_RCPM[\"Width\"]\n",
    "SC_VitC_hMeDIP_Corrected_RCPM[\"SC_VitC_3_FPKM\"]= SC_VitC_hMeDIP_Corrected_RCPM[\"SC_VitC_3_hMeDIP_RCPM\"]*1000/SC_VitC_hMeDIP_Corrected_RCPM[\"Width\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SC_Control_1_hMeDIP_RCPM</th>\n",
       "      <th>SC_Control_2_hMeDIP_RCPM</th>\n",
       "      <th>SC_Control_3_hMeDIP_RCPM</th>\n",
       "      <th>SC_VitC_1_hMeDIP_RCPM</th>\n",
       "      <th>SC_VitC_2_hMeDIP_RCPM</th>\n",
       "      <th>SC_VitC_3_hMeDIP_RCPM</th>\n",
       "      <th>Chr</th>\n",
       "      <th>Start</th>\n",
       "      <th>Stop</th>\n",
       "      <th>Width</th>\n",
       "      <th>SC_Control_1_FPKM</th>\n",
       "      <th>SC_Control_2_FPKM</th>\n",
       "      <th>SC_Control_3_FPKM</th>\n",
       "      <th>SC_VitC_1_FPKM</th>\n",
       "      <th>SC_VitC_2_FPKM</th>\n",
       "      <th>SC_VitC_3_FPKM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peak_Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merged_peak_92032</th>\n",
       "      <td>1.777345</td>\n",
       "      <td>1.964910</td>\n",
       "      <td>2.254130</td>\n",
       "      <td>1.137684</td>\n",
       "      <td>0.992029</td>\n",
       "      <td>0.879472</td>\n",
       "      <td>11</td>\n",
       "      <td>58427350</td>\n",
       "      <td>58428140</td>\n",
       "      <td>791</td>\n",
       "      <td>2.246960</td>\n",
       "      <td>2.484084</td>\n",
       "      <td>2.849722</td>\n",
       "      <td>1.438285</td>\n",
       "      <td>1.254145</td>\n",
       "      <td>1.111848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merged_peak_92041</th>\n",
       "      <td>1.382380</td>\n",
       "      <td>1.438366</td>\n",
       "      <td>1.855074</td>\n",
       "      <td>1.374701</td>\n",
       "      <td>1.359859</td>\n",
       "      <td>1.329434</td>\n",
       "      <td>11</td>\n",
       "      <td>58467708</td>\n",
       "      <td>58468197</td>\n",
       "      <td>490</td>\n",
       "      <td>2.821183</td>\n",
       "      <td>2.935440</td>\n",
       "      <td>3.785865</td>\n",
       "      <td>2.805512</td>\n",
       "      <td>2.775223</td>\n",
       "      <td>2.713131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100020</th>\n",
       "      <td>0.836398</td>\n",
       "      <td>0.937506</td>\n",
       "      <td>0.927537</td>\n",
       "      <td>0.663649</td>\n",
       "      <td>0.557319</td>\n",
       "      <td>0.685170</td>\n",
       "      <td>11</td>\n",
       "      <td>58509719</td>\n",
       "      <td>58510097</td>\n",
       "      <td>379</td>\n",
       "      <td>2.206854</td>\n",
       "      <td>2.473631</td>\n",
       "      <td>2.447327</td>\n",
       "      <td>1.751052</td>\n",
       "      <td>1.470500</td>\n",
       "      <td>1.807836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100028</th>\n",
       "      <td>0.766698</td>\n",
       "      <td>0.911821</td>\n",
       "      <td>1.003034</td>\n",
       "      <td>0.592544</td>\n",
       "      <td>0.546173</td>\n",
       "      <td>0.582906</td>\n",
       "      <td>11</td>\n",
       "      <td>58518547</td>\n",
       "      <td>58518878</td>\n",
       "      <td>332</td>\n",
       "      <td>2.309331</td>\n",
       "      <td>2.746449</td>\n",
       "      <td>3.021187</td>\n",
       "      <td>1.784770</td>\n",
       "      <td>1.645099</td>\n",
       "      <td>1.755740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_10003</th>\n",
       "      <td>0.848014</td>\n",
       "      <td>1.155830</td>\n",
       "      <td>0.905966</td>\n",
       "      <td>0.628096</td>\n",
       "      <td>0.847126</td>\n",
       "      <td>0.705623</td>\n",
       "      <td>1</td>\n",
       "      <td>48456350</td>\n",
       "      <td>48456761</td>\n",
       "      <td>412</td>\n",
       "      <td>2.058287</td>\n",
       "      <td>2.805412</td>\n",
       "      <td>2.198947</td>\n",
       "      <td>1.524505</td>\n",
       "      <td>2.056130</td>\n",
       "      <td>1.712677</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "                                                    SC_Control_1_hMeDIP_RCPM  \\\n",
       "Peak_Name                                                                      \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...                  1.777345   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...                  1.382380   \n",
       "SC_Control_IP_Merged_peak_100020                                    0.836398   \n",
       "SC_Control_IP_Merged_peak_100028                                    0.766698   \n",
       "SC_Control_IP_Merged_peak_10003                                     0.848014   \n",
       "\n",
       "                                                    SC_Control_2_hMeDIP_RCPM  \\\n",
       "Peak_Name                                                                      \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...                  1.964910   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...                  1.438366   \n",
       "SC_Control_IP_Merged_peak_100020                                    0.937506   \n",
       "SC_Control_IP_Merged_peak_100028                                    0.911821   \n",
       "SC_Control_IP_Merged_peak_10003                                     1.155830   \n",
       "\n",
       "                                                    SC_Control_3_hMeDIP_RCPM  \\\n",
       "Peak_Name                                                                      \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...                  2.254130   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...                  1.855074   \n",
       "SC_Control_IP_Merged_peak_100020                                    0.927537   \n",
       "SC_Control_IP_Merged_peak_100028                                    1.003034   \n",
       "SC_Control_IP_Merged_peak_10003                                     0.905966   \n",
       "\n",
       "                                                    SC_VitC_1_hMeDIP_RCPM  \\\n",
       "Peak_Name                                                                   \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...               1.137684   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...               1.374701   \n",
       "SC_Control_IP_Merged_peak_100020                                 0.663649   \n",
       "SC_Control_IP_Merged_peak_100028                                 0.592544   \n",
       "SC_Control_IP_Merged_peak_10003                                  0.628096   \n",
       "\n",
       "                                                    SC_VitC_2_hMeDIP_RCPM  \\\n",
       "Peak_Name                                                                   \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...               0.992029   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...               1.359859   \n",
       "SC_Control_IP_Merged_peak_100020                                 0.557319   \n",
       "SC_Control_IP_Merged_peak_100028                                 0.546173   \n",
       "SC_Control_IP_Merged_peak_10003                                  0.847126   \n",
       "\n",
       "                                                    SC_VitC_3_hMeDIP_RCPM Chr  \\\n",
       "Peak_Name                                                                       \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...               0.879472  11   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...               1.329434  11   \n",
       "SC_Control_IP_Merged_peak_100020                                 0.685170  11   \n",
       "SC_Control_IP_Merged_peak_100028                                 0.582906  11   \n",
       "SC_Control_IP_Merged_peak_10003                                  0.705623   1   \n",
       "\n",
       "                                                       Start      Stop  Width  \\\n",
       "Peak_Name                                                                       \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...  58427350  58428140    791   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...  58467708  58468197    490   \n",
       "SC_Control_IP_Merged_peak_100020                    58509719  58510097    379   \n",
       "SC_Control_IP_Merged_peak_100028                    58518547  58518878    332   \n",
       "SC_Control_IP_Merged_peak_10003                     48456350  48456761    412   \n",
       "\n",
       "                                                    SC_Control_1_FPKM  \\\n",
       "Peak_Name                                                               \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...           2.246960   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...           2.821183   \n",
       "SC_Control_IP_Merged_peak_100020                             2.206854   \n",
       "SC_Control_IP_Merged_peak_100028                             2.309331   \n",
       "SC_Control_IP_Merged_peak_10003                              2.058287   \n",
       "\n",
       "                                                    SC_Control_2_FPKM  \\\n",
       "Peak_Name                                                               \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...           2.484084   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...           2.935440   \n",
       "SC_Control_IP_Merged_peak_100020                             2.473631   \n",
       "SC_Control_IP_Merged_peak_100028                             2.746449   \n",
       "SC_Control_IP_Merged_peak_10003                              2.805412   \n",
       "\n",
       "                                                    SC_Control_3_FPKM  \\\n",
       "Peak_Name                                                               \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...           2.849722   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...           3.785865   \n",
       "SC_Control_IP_Merged_peak_100020                             2.447327   \n",
       "SC_Control_IP_Merged_peak_100028                             3.021187   \n",
       "SC_Control_IP_Merged_peak_10003                              2.198947   \n",
       "\n",
       "                                                    SC_VitC_1_FPKM  \\\n",
       "Peak_Name                                                            \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...        1.438285   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...        2.805512   \n",
       "SC_Control_IP_Merged_peak_100020                          1.751052   \n",
       "SC_Control_IP_Merged_peak_100028                          1.784770   \n",
       "SC_Control_IP_Merged_peak_10003                           1.524505   \n",
       "\n",
       "                                                    SC_VitC_2_FPKM  \\\n",
       "Peak_Name                                                            \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...        1.254145   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...        2.775223   \n",
       "SC_Control_IP_Merged_peak_100020                          1.470500   \n",
       "SC_Control_IP_Merged_peak_100028                          1.645099   \n",
       "SC_Control_IP_Merged_peak_10003                           2.056130   \n",
       "\n",
       "                                                    SC_VitC_3_FPKM  \n",
       "Peak_Name                                                           \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...        1.111848  \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...        2.713131  \n",
       "SC_Control_IP_Merged_peak_100020                          1.807836  \n",
       "SC_Control_IP_Merged_peak_100028                          1.755740  \n",
       "SC_Control_IP_Merged_peak_10003                           1.712677  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_hMeDIP_Corrected_RCPM.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_hMeDIP_FPKM_table = SC_VitC_hMeDIP_Corrected_RCPM[[\"SC_Control_1_FPKM\", \"SC_Control_2_FPKM\", \"SC_Control_3_FPKM\", \n",
    "                                                        \"SC_VitC_1_FPKM\", \"SC_VitC_2_FPKM\", \"SC_VitC_3_FPKM\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SC_Control_1_FPKM</th>\n",
       "      <th>SC_Control_2_FPKM</th>\n",
       "      <th>SC_Control_3_FPKM</th>\n",
       "      <th>SC_VitC_1_FPKM</th>\n",
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       "      <th>Peak_Name</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merged_peak_92032</th>\n",
       "      <td>2.246960</td>\n",
       "      <td>2.484084</td>\n",
       "      <td>2.849722</td>\n",
       "      <td>1.438285</td>\n",
       "      <td>1.254145</td>\n",
       "      <td>1.111848</td>\n",
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       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merged_peak_92041</th>\n",
       "      <td>2.821183</td>\n",
       "      <td>2.935440</td>\n",
       "      <td>3.785865</td>\n",
       "      <td>2.805512</td>\n",
       "      <td>2.775223</td>\n",
       "      <td>2.713131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100020</th>\n",
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       "      <td>2.473631</td>\n",
       "      <td>2.447327</td>\n",
       "      <td>1.751052</td>\n",
       "      <td>1.470500</td>\n",
       "      <td>1.807836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100028</th>\n",
       "      <td>2.309331</td>\n",
       "      <td>2.746449</td>\n",
       "      <td>3.021187</td>\n",
       "      <td>1.784770</td>\n",
       "      <td>1.645099</td>\n",
       "      <td>1.755740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_10003</th>\n",
       "      <td>2.058287</td>\n",
       "      <td>2.805412</td>\n",
       "      <td>2.198947</td>\n",
       "      <td>1.524505</td>\n",
       "      <td>2.056130</td>\n",
       "      <td>1.712677</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    SC_Control_1_FPKM  \\\n",
       "Peak_Name                                                               \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...           2.246960   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...           2.821183   \n",
       "SC_Control_IP_Merged_peak_100020                             2.206854   \n",
       "SC_Control_IP_Merged_peak_100028                             2.309331   \n",
       "SC_Control_IP_Merged_peak_10003                              2.058287   \n",
       "\n",
       "                                                    SC_Control_2_FPKM  \\\n",
       "Peak_Name                                                               \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...           2.484084   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...           2.935440   \n",
       "SC_Control_IP_Merged_peak_100020                             2.473631   \n",
       "SC_Control_IP_Merged_peak_100028                             2.746449   \n",
       "SC_Control_IP_Merged_peak_10003                              2.805412   \n",
       "\n",
       "                                                    SC_Control_3_FPKM  \\\n",
       "Peak_Name                                                               \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...           2.849722   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...           3.785865   \n",
       "SC_Control_IP_Merged_peak_100020                             2.447327   \n",
       "SC_Control_IP_Merged_peak_100028                             3.021187   \n",
       "SC_Control_IP_Merged_peak_10003                              2.198947   \n",
       "\n",
       "                                                    SC_VitC_1_FPKM  \\\n",
       "Peak_Name                                                            \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...        1.438285   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...        2.805512   \n",
       "SC_Control_IP_Merged_peak_100020                          1.751052   \n",
       "SC_Control_IP_Merged_peak_100028                          1.784770   \n",
       "SC_Control_IP_Merged_peak_10003                           1.524505   \n",
       "\n",
       "                                                    SC_VitC_2_FPKM  \\\n",
       "Peak_Name                                                            \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...        1.254145   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...        2.775223   \n",
       "SC_Control_IP_Merged_peak_100020                          1.470500   \n",
       "SC_Control_IP_Merged_peak_100028                          1.645099   \n",
       "SC_Control_IP_Merged_peak_10003                           2.056130   \n",
       "\n",
       "                                                    SC_VitC_3_FPKM  \n",
       "Peak_Name                                                           \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...        1.111848  \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...        2.713131  \n",
       "SC_Control_IP_Merged_peak_100020                          1.807836  \n",
       "SC_Control_IP_Merged_peak_100028                          1.755740  \n",
       "SC_Control_IP_Merged_peak_10003                           1.712677  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_hMeDIP_FPKM_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_hMeDIP_FPKM_table.to_csv(Output_Path+\"SC_VitC_hMeDIP_FPKM_table.csv\")\n",
    "SC_VitC_hMeDIP_FPKM_table.to_pickle(Output_Path+\"SC_VitC_hMeDIP_FPKM_table.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I have the FPKM values calculated correctly. Next I need to figure out which ones go up, which ones go down, and which ones don't change. I am going to use a fold change cutoff of 1.5X and a corrected P-value from edgeR cutoff of 0.05. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_hMeDIP_FPKM_table = pd.read_pickle(Output_Path+\"SC_VitC_hMeDIP_FPKM_table.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_hMeDIP_FPKM_table[\"Control_Average_FPKM\"] = SC_VitC_hMeDIP_FPKM_table[[\"SC_Control_1_FPKM\", \"SC_Control_2_FPKM\", \"SC_Control_3_FPKM\"]].mean(axis=1)\n",
    "SC_VitC_hMeDIP_FPKM_table[\"VitC_Average_FPKM\"] = SC_VitC_hMeDIP_FPKM_table[[\"SC_VitC_1_FPKM\", \"SC_VitC_2_FPKM\", \"SC_VitC_3_FPKM\"]].mean(axis=1)\n",
    "SC_VitC_hMeDIP_FPKM_table[\"Fold_Change\"] =  (SC_VitC_hMeDIP_FPKM_table[\"VitC_Average_FPKM\"])/(SC_VitC_hMeDIP_FPKM_table[\"Control_Average_FPKM\"])\n",
    "SC_VitC_hMeDIP_FPKM_table[\"Log2FC\"] = np.log2(SC_VitC_hMeDIP_FPKM_table[\"Fold_Change\"])\n",
    "# All 4 of these return warnings, if you have run this before because you are replacing the old values with new ones (which are the same as the old)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SC_Control_1_FPKM</th>\n",
       "      <th>SC_Control_2_FPKM</th>\n",
       "      <th>SC_Control_3_FPKM</th>\n",
       "      <th>SC_VitC_1_FPKM</th>\n",
       "      <th>SC_VitC_2_FPKM</th>\n",
       "      <th>SC_VitC_3_FPKM</th>\n",
       "      <th>Control_Average_FPKM</th>\n",
       "      <th>VitC_Average_FPKM</th>\n",
       "      <th>Fold_Change</th>\n",
       "      <th>Log2FC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peak_Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merged_peak_108098,SC_Control_IP_Merged_peak_108099</th>\n",
       "      <td>1.914374</td>\n",
       "      <td>1.919830</td>\n",
       "      <td>2.179073</td>\n",
       "      <td>2.605982</td>\n",
       "      <td>2.826406</td>\n",
       "      <td>2.483558</td>\n",
       "      <td>2.004426</td>\n",
       "      <td>2.638649</td>\n",
       "      <td>1.316411</td>\n",
       "      <td>0.396610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_LAA_IP_Merged_peak_99990</th>\n",
       "      <td>0.982752</td>\n",
       "      <td>1.392901</td>\n",
       "      <td>1.380333</td>\n",
       "      <td>2.210791</td>\n",
       "      <td>2.031012</td>\n",
       "      <td>2.351410</td>\n",
       "      <td>1.251995</td>\n",
       "      <td>2.197738</td>\n",
       "      <td>1.755388</td>\n",
       "      <td>0.811790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_LAA_IP_Merged_peak_99994</th>\n",
       "      <td>1.089609</td>\n",
       "      <td>1.049165</td>\n",
       "      <td>1.060583</td>\n",
       "      <td>1.559797</td>\n",
       "      <td>1.551388</td>\n",
       "      <td>1.655411</td>\n",
       "      <td>1.066452</td>\n",
       "      <td>1.588865</td>\n",
       "      <td>1.489861</td>\n",
       "      <td>0.575178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_LAA_IP_Merged_peak_99995</th>\n",
       "      <td>0.807334</td>\n",
       "      <td>0.776834</td>\n",
       "      <td>1.013292</td>\n",
       "      <td>1.708234</td>\n",
       "      <td>2.108776</td>\n",
       "      <td>1.737306</td>\n",
       "      <td>0.865820</td>\n",
       "      <td>1.851439</td>\n",
       "      <td>2.138365</td>\n",
       "      <td>1.096508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_LAA_IP_Merged_peak_99999</th>\n",
       "      <td>1.013815</td>\n",
       "      <td>1.050754</td>\n",
       "      <td>1.627602</td>\n",
       "      <td>2.262439</td>\n",
       "      <td>2.654867</td>\n",
       "      <td>1.952316</td>\n",
       "      <td>1.230724</td>\n",
       "      <td>2.289874</td>\n",
       "      <td>1.860591</td>\n",
       "      <td>0.895761</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    SC_Control_1_FPKM  \\\n",
       "Peak_Name                                                               \n",
       "SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merge...           1.914374   \n",
       "SC_LAA_IP_Merged_peak_99990                                  0.982752   \n",
       "SC_LAA_IP_Merged_peak_99994                                  1.089609   \n",
       "SC_LAA_IP_Merged_peak_99995                                  0.807334   \n",
       "SC_LAA_IP_Merged_peak_99999                                  1.013815   \n",
       "\n",
       "                                                    SC_Control_2_FPKM  \\\n",
       "Peak_Name                                                               \n",
       "SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merge...           1.919830   \n",
       "SC_LAA_IP_Merged_peak_99990                                  1.392901   \n",
       "SC_LAA_IP_Merged_peak_99994                                  1.049165   \n",
       "SC_LAA_IP_Merged_peak_99995                                  0.776834   \n",
       "SC_LAA_IP_Merged_peak_99999                                  1.050754   \n",
       "\n",
       "                                                    SC_Control_3_FPKM  \\\n",
       "Peak_Name                                                               \n",
       "SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merge...           2.179073   \n",
       "SC_LAA_IP_Merged_peak_99990                                  1.380333   \n",
       "SC_LAA_IP_Merged_peak_99994                                  1.060583   \n",
       "SC_LAA_IP_Merged_peak_99995                                  1.013292   \n",
       "SC_LAA_IP_Merged_peak_99999                                  1.627602   \n",
       "\n",
       "                                                    SC_VitC_1_FPKM  \\\n",
       "Peak_Name                                                            \n",
       "SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merge...        2.605982   \n",
       "SC_LAA_IP_Merged_peak_99990                               2.210791   \n",
       "SC_LAA_IP_Merged_peak_99994                               1.559797   \n",
       "SC_LAA_IP_Merged_peak_99995                               1.708234   \n",
       "SC_LAA_IP_Merged_peak_99999                               2.262439   \n",
       "\n",
       "                                                    SC_VitC_2_FPKM  \\\n",
       "Peak_Name                                                            \n",
       "SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merge...        2.826406   \n",
       "SC_LAA_IP_Merged_peak_99990                               2.031012   \n",
       "SC_LAA_IP_Merged_peak_99994                               1.551388   \n",
       "SC_LAA_IP_Merged_peak_99995                               2.108776   \n",
       "SC_LAA_IP_Merged_peak_99999                               2.654867   \n",
       "\n",
       "                                                    SC_VitC_3_FPKM  \\\n",
       "Peak_Name                                                            \n",
       "SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merge...        2.483558   \n",
       "SC_LAA_IP_Merged_peak_99990                               2.351410   \n",
       "SC_LAA_IP_Merged_peak_99994                               1.655411   \n",
       "SC_LAA_IP_Merged_peak_99995                               1.737306   \n",
       "SC_LAA_IP_Merged_peak_99999                               1.952316   \n",
       "\n",
       "                                                    Control_Average_FPKM  \\\n",
       "Peak_Name                                                                  \n",
       "SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merge...              2.004426   \n",
       "SC_LAA_IP_Merged_peak_99990                                     1.251995   \n",
       "SC_LAA_IP_Merged_peak_99994                                     1.066452   \n",
       "SC_LAA_IP_Merged_peak_99995                                     0.865820   \n",
       "SC_LAA_IP_Merged_peak_99999                                     1.230724   \n",
       "\n",
       "                                                    VitC_Average_FPKM  \\\n",
       "Peak_Name                                                               \n",
       "SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merge...           2.638649   \n",
       "SC_LAA_IP_Merged_peak_99990                                  2.197738   \n",
       "SC_LAA_IP_Merged_peak_99994                                  1.588865   \n",
       "SC_LAA_IP_Merged_peak_99995                                  1.851439   \n",
       "SC_LAA_IP_Merged_peak_99999                                  2.289874   \n",
       "\n",
       "                                                    Fold_Change    Log2FC  \n",
       "Peak_Name                                                                  \n",
       "SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merge...     1.316411  0.396610  \n",
       "SC_LAA_IP_Merged_peak_99990                            1.755388  0.811790  \n",
       "SC_LAA_IP_Merged_peak_99994                            1.489861  0.575178  \n",
       "SC_LAA_IP_Merged_peak_99995                            2.138365  1.096508  \n",
       "SC_LAA_IP_Merged_peak_99999                            1.860591  0.895761  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_hMeDIP_FPKM_table.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_hMeDIP_edgeR = pd.read_csv(Input_hMeDIP_edgeR, sep = \",\", index_col=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>logFC</th>\n",
       "      <th>logCPM</th>\n",
       "      <th>LR</th>\n",
       "      <th>PValue</th>\n",
       "      <th>FDR</th>\n",
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       "    <tr>\n",
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       "      <th>__alignment_not_unique</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.46102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
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       "    <tr>\n",
       "      <th>__ambiguous</th>\n",
       "      <td>0.013459</td>\n",
       "      <td>2.86418</td>\n",
       "      <td>0.018216</td>\n",
       "      <td>8.926368e-01</td>\n",
       "      <td>9.003917e-01</td>\n",
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       "    <tr>\n",
       "      <th>__no_feature</th>\n",
       "      <td>0.214363</td>\n",
       "      <td>19.25585</td>\n",
       "      <td>7.587856</td>\n",
       "      <td>5.876279e-03</td>\n",
       "      <td>7.438775e-03</td>\n",
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       "    <tr>\n",
       "      <th>__not_aligned</th>\n",
       "      <td>1.443403</td>\n",
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       "      <td>43.034528</td>\n",
       "      <td>5.380000e-11</td>\n",
       "      <td>2.070000e-10</td>\n",
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       "    <tr>\n",
       "      <th>__too_low_aQual</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.46102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           logFC    logCPM         LR        PValue  \\\n",
       "Peak_Name                                                             \n",
       "__alignment_not_unique  0.000000  -5.46102   0.000000  1.000000e+00   \n",
       "__ambiguous             0.013459   2.86418   0.018216  8.926368e-01   \n",
       "__no_feature            0.214363  19.25585   7.587856  5.876279e-03   \n",
       "__not_aligned           1.443403  -1.00651  43.034528  5.380000e-11   \n",
       "__too_low_aQual         0.000000  -5.46102   0.000000  1.000000e+00   \n",
       "\n",
       "                                 FDR  \n",
       "Peak_Name                             \n",
       "__alignment_not_unique  1.000000e+00  \n",
       "__ambiguous             9.003917e-01  \n",
       "__no_feature            7.438775e-03  \n",
       "__not_aligned           2.070000e-10  \n",
       "__too_low_aQual         1.000000e+00  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_hMeDIP_edgeR.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_hMeDIP_FPKM_table = SC_VitC_hMeDIP_FPKM_table.join(SC_VitC_hMeDIP_edgeR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SC_Control_1_FPKM</th>\n",
       "      <th>SC_Control_2_FPKM</th>\n",
       "      <th>SC_Control_3_FPKM</th>\n",
       "      <th>SC_VitC_1_FPKM</th>\n",
       "      <th>SC_VitC_2_FPKM</th>\n",
       "      <th>SC_VitC_3_FPKM</th>\n",
       "      <th>Control_Average_FPKM</th>\n",
       "      <th>VitC_Average_FPKM</th>\n",
       "      <th>Fold_Change</th>\n",
       "      <th>Log2FC</th>\n",
       "      <th>logFC</th>\n",
       "      <th>logCPM</th>\n",
       "      <th>LR</th>\n",
       "      <th>PValue</th>\n",
       "      <th>FDR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peak_Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merged_peak_108098,SC_Control_IP_Merged_peak_108099</th>\n",
       "      <td>1.914374</td>\n",
       "      <td>1.919830</td>\n",
       "      <td>2.179073</td>\n",
       "      <td>2.605982</td>\n",
       "      <td>2.826406</td>\n",
       "      <td>2.483558</td>\n",
       "      <td>2.004426</td>\n",
       "      <td>2.638649</td>\n",
       "      <td>1.316411</td>\n",
       "      <td>0.396610</td>\n",
       "      <td>-0.393831</td>\n",
       "      <td>2.195562</td>\n",
       "      <td>15.294069</td>\n",
       "      <td>9.200000e-05</td>\n",
       "      <td>1.409190e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_LAA_IP_Merged_peak_99990</th>\n",
       "      <td>0.982752</td>\n",
       "      <td>1.392901</td>\n",
       "      <td>1.380333</td>\n",
       "      <td>2.210791</td>\n",
       "      <td>2.031012</td>\n",
       "      <td>2.351410</td>\n",
       "      <td>1.251995</td>\n",
       "      <td>2.197738</td>\n",
       "      <td>1.755388</td>\n",
       "      <td>0.811790</td>\n",
       "      <td>-0.814093</td>\n",
       "      <td>-0.280716</td>\n",
       "      <td>29.868011</td>\n",
       "      <td>4.620000e-08</td>\n",
       "      <td>1.130000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_LAA_IP_Merged_peak_99994</th>\n",
       "      <td>1.089609</td>\n",
       "      <td>1.049165</td>\n",
       "      <td>1.060583</td>\n",
       "      <td>1.559797</td>\n",
       "      <td>1.551388</td>\n",
       "      <td>1.655411</td>\n",
       "      <td>1.066452</td>\n",
       "      <td>1.588865</td>\n",
       "      <td>1.489861</td>\n",
       "      <td>0.575178</td>\n",
       "      <td>-0.575932</td>\n",
       "      <td>-0.144461</td>\n",
       "      <td>18.103571</td>\n",
       "      <td>2.090000e-05</td>\n",
       "      <td>3.470000e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_LAA_IP_Merged_peak_99995</th>\n",
       "      <td>0.807334</td>\n",
       "      <td>0.776834</td>\n",
       "      <td>1.013292</td>\n",
       "      <td>1.708234</td>\n",
       "      <td>2.108776</td>\n",
       "      <td>1.737306</td>\n",
       "      <td>0.865820</td>\n",
       "      <td>1.851439</td>\n",
       "      <td>2.138365</td>\n",
       "      <td>1.096508</td>\n",
       "      <td>-1.085555</td>\n",
       "      <td>0.121553</td>\n",
       "      <td>55.087521</td>\n",
       "      <td>1.150000e-13</td>\n",
       "      <td>6.740000e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_LAA_IP_Merged_peak_99999</th>\n",
       "      <td>1.013815</td>\n",
       "      <td>1.050754</td>\n",
       "      <td>1.627602</td>\n",
       "      <td>2.262439</td>\n",
       "      <td>2.654867</td>\n",
       "      <td>1.952316</td>\n",
       "      <td>1.230724</td>\n",
       "      <td>2.289874</td>\n",
       "      <td>1.860591</td>\n",
       "      <td>0.895761</td>\n",
       "      <td>-0.880321</td>\n",
       "      <td>-0.002540</td>\n",
       "      <td>24.100901</td>\n",
       "      <td>9.140000e-07</td>\n",
       "      <td>1.830000e-06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    SC_Control_1_FPKM  \\\n",
       "Peak_Name                                                               \n",
       "SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merge...           1.914374   \n",
       "SC_LAA_IP_Merged_peak_99990                                  0.982752   \n",
       "SC_LAA_IP_Merged_peak_99994                                  1.089609   \n",
       "SC_LAA_IP_Merged_peak_99995                                  0.807334   \n",
       "SC_LAA_IP_Merged_peak_99999                                  1.013815   \n",
       "\n",
       "                                                    SC_Control_2_FPKM  \\\n",
       "Peak_Name                                                               \n",
       "SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merge...           1.919830   \n",
       "SC_LAA_IP_Merged_peak_99990                                  1.392901   \n",
       "SC_LAA_IP_Merged_peak_99994                                  1.049165   \n",
       "SC_LAA_IP_Merged_peak_99995                                  0.776834   \n",
       "SC_LAA_IP_Merged_peak_99999                                  1.050754   \n",
       "\n",
       "                                                    SC_Control_3_FPKM  \\\n",
       "Peak_Name                                                               \n",
       "SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merge...           2.179073   \n",
       "SC_LAA_IP_Merged_peak_99990                                  1.380333   \n",
       "SC_LAA_IP_Merged_peak_99994                                  1.060583   \n",
       "SC_LAA_IP_Merged_peak_99995                                  1.013292   \n",
       "SC_LAA_IP_Merged_peak_99999                                  1.627602   \n",
       "\n",
       "                                                    SC_VitC_1_FPKM  \\\n",
       "Peak_Name                                                            \n",
       "SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merge...        2.605982   \n",
       "SC_LAA_IP_Merged_peak_99990                               2.210791   \n",
       "SC_LAA_IP_Merged_peak_99994                               1.559797   \n",
       "SC_LAA_IP_Merged_peak_99995                               1.708234   \n",
       "SC_LAA_IP_Merged_peak_99999                               2.262439   \n",
       "\n",
       "                                                    SC_VitC_2_FPKM  \\\n",
       "Peak_Name                                                            \n",
       "SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merge...        2.826406   \n",
       "SC_LAA_IP_Merged_peak_99990                               2.031012   \n",
       "SC_LAA_IP_Merged_peak_99994                               1.551388   \n",
       "SC_LAA_IP_Merged_peak_99995                               2.108776   \n",
       "SC_LAA_IP_Merged_peak_99999                               2.654867   \n",
       "\n",
       "                                                    SC_VitC_3_FPKM  \\\n",
       "Peak_Name                                                            \n",
       "SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merge...        2.483558   \n",
       "SC_LAA_IP_Merged_peak_99990                               2.351410   \n",
       "SC_LAA_IP_Merged_peak_99994                               1.655411   \n",
       "SC_LAA_IP_Merged_peak_99995                               1.737306   \n",
       "SC_LAA_IP_Merged_peak_99999                               1.952316   \n",
       "\n",
       "                                                    Control_Average_FPKM  \\\n",
       "Peak_Name                                                                  \n",
       "SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merge...              2.004426   \n",
       "SC_LAA_IP_Merged_peak_99990                                     1.251995   \n",
       "SC_LAA_IP_Merged_peak_99994                                     1.066452   \n",
       "SC_LAA_IP_Merged_peak_99995                                     0.865820   \n",
       "SC_LAA_IP_Merged_peak_99999                                     1.230724   \n",
       "\n",
       "                                                    VitC_Average_FPKM  \\\n",
       "Peak_Name                                                               \n",
       "SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merge...           2.638649   \n",
       "SC_LAA_IP_Merged_peak_99990                                  2.197738   \n",
       "SC_LAA_IP_Merged_peak_99994                                  1.588865   \n",
       "SC_LAA_IP_Merged_peak_99995                                  1.851439   \n",
       "SC_LAA_IP_Merged_peak_99999                                  2.289874   \n",
       "\n",
       "                                                    Fold_Change    Log2FC  \\\n",
       "Peak_Name                                                                   \n",
       "SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merge...     1.316411  0.396610   \n",
       "SC_LAA_IP_Merged_peak_99990                            1.755388  0.811790   \n",
       "SC_LAA_IP_Merged_peak_99994                            1.489861  0.575178   \n",
       "SC_LAA_IP_Merged_peak_99995                            2.138365  1.096508   \n",
       "SC_LAA_IP_Merged_peak_99999                            1.860591  0.895761   \n",
       "\n",
       "                                                       logFC    logCPM  \\\n",
       "Peak_Name                                                                \n",
       "SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merge... -0.393831  2.195562   \n",
       "SC_LAA_IP_Merged_peak_99990                        -0.814093 -0.280716   \n",
       "SC_LAA_IP_Merged_peak_99994                        -0.575932 -0.144461   \n",
       "SC_LAA_IP_Merged_peak_99995                        -1.085555  0.121553   \n",
       "SC_LAA_IP_Merged_peak_99999                        -0.880321 -0.002540   \n",
       "\n",
       "                                                           LR        PValue  \\\n",
       "Peak_Name                                                                     \n",
       "SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merge...  15.294069  9.200000e-05   \n",
       "SC_LAA_IP_Merged_peak_99990                         29.868011  4.620000e-08   \n",
       "SC_LAA_IP_Merged_peak_99994                         18.103571  2.090000e-05   \n",
       "SC_LAA_IP_Merged_peak_99995                         55.087521  1.150000e-13   \n",
       "SC_LAA_IP_Merged_peak_99999                         24.100901  9.140000e-07   \n",
       "\n",
       "                                                             FDR  \n",
       "Peak_Name                                                         \n",
       "SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merge...  1.409190e-04  \n",
       "SC_LAA_IP_Merged_peak_99990                         1.130000e-07  \n",
       "SC_LAA_IP_Merged_peak_99994                         3.470000e-05  \n",
       "SC_LAA_IP_Merged_peak_99995                         6.740000e-13  \n",
       "SC_LAA_IP_Merged_peak_99999                         1.830000e-06  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_hMeDIP_FPKM_table.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_hMeDIP_FPKM_table = SC_VitC_hMeDIP_FPKM_table.drop([\"logFC\", \"logCPM\", \"LR\", \"PValue\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "def direction(row):\n",
    "    if row[\"FDR\"] < 0.05 :\n",
    "        if row[\"Log2FC\"] >= 0.584962500721156:\n",
    "            return \"Upregulated\"\n",
    "        elif row[\"Log2FC\"] <= -0.584962500721156:\n",
    "            return \"Downregulated\"\n",
    "        else:\n",
    "            return \"Nondifferential\"\n",
    "    else:\n",
    "        return \"Nondifferential\"\n",
    "\n",
    "SC_VitC_hMeDIP_FPKM_table[\"Change\"] =  SC_VitC_hMeDIP_FPKM_table.apply(direction, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SC_Control_1_FPKM</th>\n",
       "      <th>SC_Control_2_FPKM</th>\n",
       "      <th>SC_Control_3_FPKM</th>\n",
       "      <th>SC_VitC_1_FPKM</th>\n",
       "      <th>SC_VitC_2_FPKM</th>\n",
       "      <th>SC_VitC_3_FPKM</th>\n",
       "      <th>Control_Average_FPKM</th>\n",
       "      <th>VitC_Average_FPKM</th>\n",
       "      <th>Fold_Change</th>\n",
       "      <th>Log2FC</th>\n",
       "      <th>FDR</th>\n",
       "      <th>Change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peak_Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merged_peak_92032</th>\n",
       "      <td>2.246960</td>\n",
       "      <td>2.484084</td>\n",
       "      <td>2.849722</td>\n",
       "      <td>1.438285</td>\n",
       "      <td>1.254145</td>\n",
       "      <td>1.111848</td>\n",
       "      <td>2.526922</td>\n",
       "      <td>1.268093</td>\n",
       "      <td>0.501833</td>\n",
       "      <td>-0.994721</td>\n",
       "      <td>6.770000e-13</td>\n",
       "      <td>Downregulated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merged_peak_92041</th>\n",
       "      <td>2.821183</td>\n",
       "      <td>2.935440</td>\n",
       "      <td>3.785865</td>\n",
       "      <td>2.805512</td>\n",
       "      <td>2.775223</td>\n",
       "      <td>2.713131</td>\n",
       "      <td>3.180829</td>\n",
       "      <td>2.764622</td>\n",
       "      <td>0.869151</td>\n",
       "      <td>-0.202321</td>\n",
       "      <td>1.260042e-01</td>\n",
       "      <td>Nondifferential</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100020</th>\n",
       "      <td>2.206854</td>\n",
       "      <td>2.473631</td>\n",
       "      <td>2.447327</td>\n",
       "      <td>1.751052</td>\n",
       "      <td>1.470500</td>\n",
       "      <td>1.807836</td>\n",
       "      <td>2.375937</td>\n",
       "      <td>1.676463</td>\n",
       "      <td>0.705601</td>\n",
       "      <td>-0.503076</td>\n",
       "      <td>7.430420e-04</td>\n",
       "      <td>Nondifferential</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100028</th>\n",
       "      <td>2.309331</td>\n",
       "      <td>2.746449</td>\n",
       "      <td>3.021187</td>\n",
       "      <td>1.784770</td>\n",
       "      <td>1.645099</td>\n",
       "      <td>1.755740</td>\n",
       "      <td>2.692322</td>\n",
       "      <td>1.728536</td>\n",
       "      <td>0.642024</td>\n",
       "      <td>-0.639300</td>\n",
       "      <td>3.810000e-05</td>\n",
       "      <td>Downregulated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_10003</th>\n",
       "      <td>2.058287</td>\n",
       "      <td>2.805412</td>\n",
       "      <td>2.198947</td>\n",
       "      <td>1.524505</td>\n",
       "      <td>2.056130</td>\n",
       "      <td>1.712677</td>\n",
       "      <td>2.354215</td>\n",
       "      <td>1.764437</td>\n",
       "      <td>0.749480</td>\n",
       "      <td>-0.416038</td>\n",
       "      <td>1.296685e-02</td>\n",
       "      <td>Nondifferential</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    SC_Control_1_FPKM  \\\n",
       "Peak_Name                                                               \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...           2.246960   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...           2.821183   \n",
       "SC_Control_IP_Merged_peak_100020                             2.206854   \n",
       "SC_Control_IP_Merged_peak_100028                             2.309331   \n",
       "SC_Control_IP_Merged_peak_10003                              2.058287   \n",
       "\n",
       "                                                    SC_Control_2_FPKM  \\\n",
       "Peak_Name                                                               \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...           2.484084   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...           2.935440   \n",
       "SC_Control_IP_Merged_peak_100020                             2.473631   \n",
       "SC_Control_IP_Merged_peak_100028                             2.746449   \n",
       "SC_Control_IP_Merged_peak_10003                              2.805412   \n",
       "\n",
       "                                                    SC_Control_3_FPKM  \\\n",
       "Peak_Name                                                               \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...           2.849722   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...           3.785865   \n",
       "SC_Control_IP_Merged_peak_100020                             2.447327   \n",
       "SC_Control_IP_Merged_peak_100028                             3.021187   \n",
       "SC_Control_IP_Merged_peak_10003                              2.198947   \n",
       "\n",
       "                                                    SC_VitC_1_FPKM  \\\n",
       "Peak_Name                                                            \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...        1.438285   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...        2.805512   \n",
       "SC_Control_IP_Merged_peak_100020                          1.751052   \n",
       "SC_Control_IP_Merged_peak_100028                          1.784770   \n",
       "SC_Control_IP_Merged_peak_10003                           1.524505   \n",
       "\n",
       "                                                    SC_VitC_2_FPKM  \\\n",
       "Peak_Name                                                            \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...        1.254145   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...        2.775223   \n",
       "SC_Control_IP_Merged_peak_100020                          1.470500   \n",
       "SC_Control_IP_Merged_peak_100028                          1.645099   \n",
       "SC_Control_IP_Merged_peak_10003                           2.056130   \n",
       "\n",
       "                                                    SC_VitC_3_FPKM  \\\n",
       "Peak_Name                                                            \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...        1.111848   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...        2.713131   \n",
       "SC_Control_IP_Merged_peak_100020                          1.807836   \n",
       "SC_Control_IP_Merged_peak_100028                          1.755740   \n",
       "SC_Control_IP_Merged_peak_10003                           1.712677   \n",
       "\n",
       "                                                    Control_Average_FPKM  \\\n",
       "Peak_Name                                                                  \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...              2.526922   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...              3.180829   \n",
       "SC_Control_IP_Merged_peak_100020                                2.375937   \n",
       "SC_Control_IP_Merged_peak_100028                                2.692322   \n",
       "SC_Control_IP_Merged_peak_10003                                 2.354215   \n",
       "\n",
       "                                                    VitC_Average_FPKM  \\\n",
       "Peak_Name                                                               \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...           1.268093   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...           2.764622   \n",
       "SC_Control_IP_Merged_peak_100020                             1.676463   \n",
       "SC_Control_IP_Merged_peak_100028                             1.728536   \n",
       "SC_Control_IP_Merged_peak_10003                              1.764437   \n",
       "\n",
       "                                                    Fold_Change    Log2FC  \\\n",
       "Peak_Name                                                                   \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...     0.501833 -0.994721   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...     0.869151 -0.202321   \n",
       "SC_Control_IP_Merged_peak_100020                       0.705601 -0.503076   \n",
       "SC_Control_IP_Merged_peak_100028                       0.642024 -0.639300   \n",
       "SC_Control_IP_Merged_peak_10003                        0.749480 -0.416038   \n",
       "\n",
       "                                                             FDR  \\\n",
       "Peak_Name                                                          \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...  6.770000e-13   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...  1.260042e-01   \n",
       "SC_Control_IP_Merged_peak_100020                    7.430420e-04   \n",
       "SC_Control_IP_Merged_peak_100028                    3.810000e-05   \n",
       "SC_Control_IP_Merged_peak_10003                     1.296685e-02   \n",
       "\n",
       "                                                             Change  \n",
       "Peak_Name                                                            \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...    Downregulated  \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...  Nondifferential  \n",
       "SC_Control_IP_Merged_peak_100020                    Nondifferential  \n",
       "SC_Control_IP_Merged_peak_100028                      Downregulated  \n",
       "SC_Control_IP_Merged_peak_10003                     Nondifferential  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_hMeDIP_FPKM_table.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I need to read in the region_analysis to put each peak with a gene or region of a gene"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/david/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3020: DtypeWarning: Columns (0) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "SC_VitC_region_analysis = pd.read_csv(Input_region_analysis, sep = \"\\t\", index_col=3)\n",
    "SC_VitC_region_analysis.rename(columns={'Gene_Region':'Genomic_Region'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Chr</th>\n",
       "      <th>Start</th>\n",
       "      <th>Stop</th>\n",
       "      <th>Gene_ID</th>\n",
       "      <th>Transcript_ID</th>\n",
       "      <th>Sense</th>\n",
       "      <th>Feature_Start</th>\n",
       "      <th>Feature_Stop</th>\n",
       "      <th>Genomic_Region</th>\n",
       "      <th>Distance_From_TSS</th>\n",
       "      <th>Gene_Biotype</th>\n",
       "      <th>Gene_Name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peak_Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merged_peak_92032</th>\n",
       "      <td>11</td>\n",
       "      <td>58427350</td>\n",
       "      <td>58428140</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OtherIntergenic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No_anno</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merged_peak_92041</th>\n",
       "      <td>11</td>\n",
       "      <td>58467708</td>\n",
       "      <td>58468197</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OtherIntergenic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No_anno</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100020</th>\n",
       "      <td>11</td>\n",
       "      <td>58509719</td>\n",
       "      <td>58510097</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OtherIntergenic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No_anno</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100028</th>\n",
       "      <td>11</td>\n",
       "      <td>58518547</td>\n",
       "      <td>58518878</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OtherIntergenic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No_anno</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_10003</th>\n",
       "      <td>1</td>\n",
       "      <td>48456350</td>\n",
       "      <td>48456761</td>\n",
       "      <td>ENSRNOG00000022946</td>\n",
       "      <td>ENSRNOT00000037369</td>\n",
       "      <td>+</td>\n",
       "      <td>48433079.0</td>\n",
       "      <td>48521256.0</td>\n",
       "      <td>Genebody</td>\n",
       "      <td>23476.0</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>Slc22a3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   Chr     Start      Stop  \\\n",
       "Peak_Name                                                                    \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...  11  58427350  58428140   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...  11  58467708  58468197   \n",
       "SC_Control_IP_Merged_peak_100020                    11  58509719  58510097   \n",
       "SC_Control_IP_Merged_peak_100028                    11  58518547  58518878   \n",
       "SC_Control_IP_Merged_peak_10003                      1  48456350  48456761   \n",
       "\n",
       "                                                               Gene_ID  \\\n",
       "Peak_Name                                                                \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...                 NaN   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...                 NaN   \n",
       "SC_Control_IP_Merged_peak_100020                                   NaN   \n",
       "SC_Control_IP_Merged_peak_100028                                   NaN   \n",
       "SC_Control_IP_Merged_peak_10003                     ENSRNOG00000022946   \n",
       "\n",
       "                                                         Transcript_ID Sense  \\\n",
       "Peak_Name                                                                      \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...                 NaN     .   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...                 NaN     .   \n",
       "SC_Control_IP_Merged_peak_100020                                   NaN     .   \n",
       "SC_Control_IP_Merged_peak_100028                                   NaN     .   \n",
       "SC_Control_IP_Merged_peak_10003                     ENSRNOT00000037369     +   \n",
       "\n",
       "                                                    Feature_Start  \\\n",
       "Peak_Name                                                           \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...            NaN   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...            NaN   \n",
       "SC_Control_IP_Merged_peak_100020                              NaN   \n",
       "SC_Control_IP_Merged_peak_100028                              NaN   \n",
       "SC_Control_IP_Merged_peak_10003                        48433079.0   \n",
       "\n",
       "                                                    Feature_Stop  \\\n",
       "Peak_Name                                                          \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...           NaN   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...           NaN   \n",
       "SC_Control_IP_Merged_peak_100020                             NaN   \n",
       "SC_Control_IP_Merged_peak_100028                             NaN   \n",
       "SC_Control_IP_Merged_peak_10003                       48521256.0   \n",
       "\n",
       "                                                     Genomic_Region  \\\n",
       "Peak_Name                                                             \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...  OtherIntergenic   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...  OtherIntergenic   \n",
       "SC_Control_IP_Merged_peak_100020                    OtherIntergenic   \n",
       "SC_Control_IP_Merged_peak_100028                    OtherIntergenic   \n",
       "SC_Control_IP_Merged_peak_10003                            Genebody   \n",
       "\n",
       "                                                    Distance_From_TSS  \\\n",
       "Peak_Name                                                               \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...                NaN   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...                NaN   \n",
       "SC_Control_IP_Merged_peak_100020                                  NaN   \n",
       "SC_Control_IP_Merged_peak_100028                                  NaN   \n",
       "SC_Control_IP_Merged_peak_10003                               23476.0   \n",
       "\n",
       "                                                      Gene_Biotype Gene_Name  \n",
       "Peak_Name                                                                     \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...         No_anno       NaN  \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...         No_anno       NaN  \n",
       "SC_Control_IP_Merged_peak_100020                           No_anno       NaN  \n",
       "SC_Control_IP_Merged_peak_100028                           No_anno       NaN  \n",
       "SC_Control_IP_Merged_peak_10003                     protein_coding   Slc22a3  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_region_analysis.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dave_region(row):\n",
    "    if row[\"Distance_From_TSS\"] >= -3000 and row[\"Distance_From_TSS\"] < -250:\n",
    "        return \"Upstream_Promoter\"\n",
    "    elif row[\"Distance_From_TSS\"] >= -250 and row[\"Distance_From_TSS\"] <= 250:\n",
    "        return \"TSS\"\n",
    "    elif row[\"Distance_From_TSS\"] > 250 and row[\"Distance_From_TSS\"] <= 3000:\n",
    "        return \"Downstream_Promoter\"\n",
    "    elif row[\"Distance_From_TSS\"] > 3000:\n",
    "        return \"Gene_Body\"\n",
    "    else:\n",
    "        return \"Intergenic\"\n",
    "    \n",
    "\n",
    "SC_VitC_region_analysis[\"Gene_Region\"] =  SC_VitC_region_analysis.apply(dave_region, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merged_peak_92032</th>\n",
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       "      <th>SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merged_peak_92041</th>\n",
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       "      <td>58467708</td>\n",
       "      <td>58468197</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OtherIntergenic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No_anno</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Intergenic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100020</th>\n",
       "      <td>11</td>\n",
       "      <td>58509719</td>\n",
       "      <td>58510097</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OtherIntergenic</td>\n",
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       "      <td>Intergenic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100028</th>\n",
       "      <td>11</td>\n",
       "      <td>58518547</td>\n",
       "      <td>58518878</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OtherIntergenic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No_anno</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Intergenic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_10003</th>\n",
       "      <td>1</td>\n",
       "      <td>48456350</td>\n",
       "      <td>48456761</td>\n",
       "      <td>ENSRNOG00000022946</td>\n",
       "      <td>ENSRNOT00000037369</td>\n",
       "      <td>+</td>\n",
       "      <td>48433079.0</td>\n",
       "      <td>48521256.0</td>\n",
       "      <td>Genebody</td>\n",
       "      <td>23476.0</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>Slc22a3</td>\n",
       "      <td>Gene_Body</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   Chr     Start      Stop  \\\n",
       "Peak_Name                                                                    \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...  11  58427350  58428140   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...  11  58467708  58468197   \n",
       "SC_Control_IP_Merged_peak_100020                    11  58509719  58510097   \n",
       "SC_Control_IP_Merged_peak_100028                    11  58518547  58518878   \n",
       "SC_Control_IP_Merged_peak_10003                      1  48456350  48456761   \n",
       "\n",
       "                                                               Gene_ID  \\\n",
       "Peak_Name                                                                \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...                 NaN   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...                 NaN   \n",
       "SC_Control_IP_Merged_peak_100020                                   NaN   \n",
       "SC_Control_IP_Merged_peak_100028                                   NaN   \n",
       "SC_Control_IP_Merged_peak_10003                     ENSRNOG00000022946   \n",
       "\n",
       "                                                         Transcript_ID Sense  \\\n",
       "Peak_Name                                                                      \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...                 NaN     .   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...                 NaN     .   \n",
       "SC_Control_IP_Merged_peak_100020                                   NaN     .   \n",
       "SC_Control_IP_Merged_peak_100028                                   NaN     .   \n",
       "SC_Control_IP_Merged_peak_10003                     ENSRNOT00000037369     +   \n",
       "\n",
       "                                                    Feature_Start  \\\n",
       "Peak_Name                                                           \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...            NaN   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...            NaN   \n",
       "SC_Control_IP_Merged_peak_100020                              NaN   \n",
       "SC_Control_IP_Merged_peak_100028                              NaN   \n",
       "SC_Control_IP_Merged_peak_10003                        48433079.0   \n",
       "\n",
       "                                                    Feature_Stop  \\\n",
       "Peak_Name                                                          \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...           NaN   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...           NaN   \n",
       "SC_Control_IP_Merged_peak_100020                             NaN   \n",
       "SC_Control_IP_Merged_peak_100028                             NaN   \n",
       "SC_Control_IP_Merged_peak_10003                       48521256.0   \n",
       "\n",
       "                                                     Genomic_Region  \\\n",
       "Peak_Name                                                             \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...  OtherIntergenic   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...  OtherIntergenic   \n",
       "SC_Control_IP_Merged_peak_100020                    OtherIntergenic   \n",
       "SC_Control_IP_Merged_peak_100028                    OtherIntergenic   \n",
       "SC_Control_IP_Merged_peak_10003                            Genebody   \n",
       "\n",
       "                                                    Distance_From_TSS  \\\n",
       "Peak_Name                                                               \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...                NaN   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...                NaN   \n",
       "SC_Control_IP_Merged_peak_100020                                  NaN   \n",
       "SC_Control_IP_Merged_peak_100028                                  NaN   \n",
       "SC_Control_IP_Merged_peak_10003                               23476.0   \n",
       "\n",
       "                                                      Gene_Biotype Gene_Name  \\\n",
       "Peak_Name                                                                      \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...         No_anno       NaN   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...         No_anno       NaN   \n",
       "SC_Control_IP_Merged_peak_100020                           No_anno       NaN   \n",
       "SC_Control_IP_Merged_peak_100028                           No_anno       NaN   \n",
       "SC_Control_IP_Merged_peak_10003                     protein_coding   Slc22a3   \n",
       "\n",
       "                                                   Gene_Region  \n",
       "Peak_Name                                                       \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...  Intergenic  \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...  Intergenic  \n",
       "SC_Control_IP_Merged_peak_100020                    Intergenic  \n",
       "SC_Control_IP_Merged_peak_100028                    Intergenic  \n",
       "SC_Control_IP_Merged_peak_10003                      Gene_Body  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_region_analysis.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "SC_VitC_region_analysis_cleaned = SC_VitC_region_analysis[[\"Gene_ID\", \"Gene_Region\", \"Distance_From_TSS\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gene_ID</th>\n",
       "      <th>Gene_Region</th>\n",
       "      <th>Distance_From_TSS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peak_Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merged_peak_92032</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Intergenic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merged_peak_92041</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Intergenic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100020</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Intergenic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100028</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Intergenic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_10003</th>\n",
       "      <td>ENSRNOG00000022946</td>\n",
       "      <td>Gene_Body</td>\n",
       "      <td>23476.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                               Gene_ID  \\\n",
       "Peak_Name                                                                \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...                 NaN   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...                 NaN   \n",
       "SC_Control_IP_Merged_peak_100020                                   NaN   \n",
       "SC_Control_IP_Merged_peak_100028                                   NaN   \n",
       "SC_Control_IP_Merged_peak_10003                     ENSRNOG00000022946   \n",
       "\n",
       "                                                   Gene_Region  \\\n",
       "Peak_Name                                                        \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...  Intergenic   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...  Intergenic   \n",
       "SC_Control_IP_Merged_peak_100020                    Intergenic   \n",
       "SC_Control_IP_Merged_peak_100028                    Intergenic   \n",
       "SC_Control_IP_Merged_peak_10003                      Gene_Body   \n",
       "\n",
       "                                                    Distance_From_TSS  \n",
       "Peak_Name                                                              \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...                NaN  \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...                NaN  \n",
       "SC_Control_IP_Merged_peak_100020                                  NaN  \n",
       "SC_Control_IP_Merged_peak_100028                                  NaN  \n",
       "SC_Control_IP_Merged_peak_10003                               23476.0  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_region_analysis_cleaned.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_hMeDIP_FPKM_table = SC_VitC_hMeDIP_FPKM_table.join(SC_VitC_region_analysis_cleaned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SC_Control_1_FPKM</th>\n",
       "      <th>SC_Control_2_FPKM</th>\n",
       "      <th>SC_Control_3_FPKM</th>\n",
       "      <th>SC_VitC_1_FPKM</th>\n",
       "      <th>SC_VitC_2_FPKM</th>\n",
       "      <th>SC_VitC_3_FPKM</th>\n",
       "      <th>Control_Average_FPKM</th>\n",
       "      <th>VitC_Average_FPKM</th>\n",
       "      <th>Fold_Change</th>\n",
       "      <th>Log2FC</th>\n",
       "      <th>FDR</th>\n",
       "      <th>Change</th>\n",
       "      <th>Gene_ID</th>\n",
       "      <th>Gene_Region</th>\n",
       "      <th>Distance_From_TSS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peak_Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merged_peak_92032</th>\n",
       "      <td>2.246960</td>\n",
       "      <td>2.484084</td>\n",
       "      <td>2.849722</td>\n",
       "      <td>1.438285</td>\n",
       "      <td>1.254145</td>\n",
       "      <td>1.111848</td>\n",
       "      <td>2.526922</td>\n",
       "      <td>1.268093</td>\n",
       "      <td>0.501833</td>\n",
       "      <td>-0.994721</td>\n",
       "      <td>6.770000e-13</td>\n",
       "      <td>Downregulated</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Intergenic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merged_peak_92041</th>\n",
       "      <td>2.821183</td>\n",
       "      <td>2.935440</td>\n",
       "      <td>3.785865</td>\n",
       "      <td>2.805512</td>\n",
       "      <td>2.775223</td>\n",
       "      <td>2.713131</td>\n",
       "      <td>3.180829</td>\n",
       "      <td>2.764622</td>\n",
       "      <td>0.869151</td>\n",
       "      <td>-0.202321</td>\n",
       "      <td>1.260042e-01</td>\n",
       "      <td>Nondifferential</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Intergenic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100020</th>\n",
       "      <td>2.206854</td>\n",
       "      <td>2.473631</td>\n",
       "      <td>2.447327</td>\n",
       "      <td>1.751052</td>\n",
       "      <td>1.470500</td>\n",
       "      <td>1.807836</td>\n",
       "      <td>2.375937</td>\n",
       "      <td>1.676463</td>\n",
       "      <td>0.705601</td>\n",
       "      <td>-0.503076</td>\n",
       "      <td>7.430420e-04</td>\n",
       "      <td>Nondifferential</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Intergenic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100028</th>\n",
       "      <td>2.309331</td>\n",
       "      <td>2.746449</td>\n",
       "      <td>3.021187</td>\n",
       "      <td>1.784770</td>\n",
       "      <td>1.645099</td>\n",
       "      <td>1.755740</td>\n",
       "      <td>2.692322</td>\n",
       "      <td>1.728536</td>\n",
       "      <td>0.642024</td>\n",
       "      <td>-0.639300</td>\n",
       "      <td>3.810000e-05</td>\n",
       "      <td>Downregulated</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Intergenic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_10003</th>\n",
       "      <td>2.058287</td>\n",
       "      <td>2.805412</td>\n",
       "      <td>2.198947</td>\n",
       "      <td>1.524505</td>\n",
       "      <td>2.056130</td>\n",
       "      <td>1.712677</td>\n",
       "      <td>2.354215</td>\n",
       "      <td>1.764437</td>\n",
       "      <td>0.749480</td>\n",
       "      <td>-0.416038</td>\n",
       "      <td>1.296685e-02</td>\n",
       "      <td>Nondifferential</td>\n",
       "      <td>ENSRNOG00000022946</td>\n",
       "      <td>Gene_Body</td>\n",
       "      <td>23476.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    SC_Control_1_FPKM  \\\n",
       "Peak_Name                                                               \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...           2.246960   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...           2.821183   \n",
       "SC_Control_IP_Merged_peak_100020                             2.206854   \n",
       "SC_Control_IP_Merged_peak_100028                             2.309331   \n",
       "SC_Control_IP_Merged_peak_10003                              2.058287   \n",
       "\n",
       "                                                    SC_Control_2_FPKM  \\\n",
       "Peak_Name                                                               \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...           2.484084   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...           2.935440   \n",
       "SC_Control_IP_Merged_peak_100020                             2.473631   \n",
       "SC_Control_IP_Merged_peak_100028                             2.746449   \n",
       "SC_Control_IP_Merged_peak_10003                              2.805412   \n",
       "\n",
       "                                                    SC_Control_3_FPKM  \\\n",
       "Peak_Name                                                               \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...           2.849722   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...           3.785865   \n",
       "SC_Control_IP_Merged_peak_100020                             2.447327   \n",
       "SC_Control_IP_Merged_peak_100028                             3.021187   \n",
       "SC_Control_IP_Merged_peak_10003                              2.198947   \n",
       "\n",
       "                                                    SC_VitC_1_FPKM  \\\n",
       "Peak_Name                                                            \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...        1.438285   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...        2.805512   \n",
       "SC_Control_IP_Merged_peak_100020                          1.751052   \n",
       "SC_Control_IP_Merged_peak_100028                          1.784770   \n",
       "SC_Control_IP_Merged_peak_10003                           1.524505   \n",
       "\n",
       "                                                    SC_VitC_2_FPKM  \\\n",
       "Peak_Name                                                            \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...        1.254145   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...        2.775223   \n",
       "SC_Control_IP_Merged_peak_100020                          1.470500   \n",
       "SC_Control_IP_Merged_peak_100028                          1.645099   \n",
       "SC_Control_IP_Merged_peak_10003                           2.056130   \n",
       "\n",
       "                                                    SC_VitC_3_FPKM  \\\n",
       "Peak_Name                                                            \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...        1.111848   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...        2.713131   \n",
       "SC_Control_IP_Merged_peak_100020                          1.807836   \n",
       "SC_Control_IP_Merged_peak_100028                          1.755740   \n",
       "SC_Control_IP_Merged_peak_10003                           1.712677   \n",
       "\n",
       "                                                    Control_Average_FPKM  \\\n",
       "Peak_Name                                                                  \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...              2.526922   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...              3.180829   \n",
       "SC_Control_IP_Merged_peak_100020                                2.375937   \n",
       "SC_Control_IP_Merged_peak_100028                                2.692322   \n",
       "SC_Control_IP_Merged_peak_10003                                 2.354215   \n",
       "\n",
       "                                                    VitC_Average_FPKM  \\\n",
       "Peak_Name                                                               \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...           1.268093   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...           2.764622   \n",
       "SC_Control_IP_Merged_peak_100020                             1.676463   \n",
       "SC_Control_IP_Merged_peak_100028                             1.728536   \n",
       "SC_Control_IP_Merged_peak_10003                              1.764437   \n",
       "\n",
       "                                                    Fold_Change    Log2FC  \\\n",
       "Peak_Name                                                                   \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...     0.501833 -0.994721   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...     0.869151 -0.202321   \n",
       "SC_Control_IP_Merged_peak_100020                       0.705601 -0.503076   \n",
       "SC_Control_IP_Merged_peak_100028                       0.642024 -0.639300   \n",
       "SC_Control_IP_Merged_peak_10003                        0.749480 -0.416038   \n",
       "\n",
       "                                                             FDR  \\\n",
       "Peak_Name                                                          \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...  6.770000e-13   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...  1.260042e-01   \n",
       "SC_Control_IP_Merged_peak_100020                    7.430420e-04   \n",
       "SC_Control_IP_Merged_peak_100028                    3.810000e-05   \n",
       "SC_Control_IP_Merged_peak_10003                     1.296685e-02   \n",
       "\n",
       "                                                             Change  \\\n",
       "Peak_Name                                                             \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...    Downregulated   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...  Nondifferential   \n",
       "SC_Control_IP_Merged_peak_100020                    Nondifferential   \n",
       "SC_Control_IP_Merged_peak_100028                      Downregulated   \n",
       "SC_Control_IP_Merged_peak_10003                     Nondifferential   \n",
       "\n",
       "                                                               Gene_ID  \\\n",
       "Peak_Name                                                                \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...                 NaN   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...                 NaN   \n",
       "SC_Control_IP_Merged_peak_100020                                   NaN   \n",
       "SC_Control_IP_Merged_peak_100028                                   NaN   \n",
       "SC_Control_IP_Merged_peak_10003                     ENSRNOG00000022946   \n",
       "\n",
       "                                                   Gene_Region  \\\n",
       "Peak_Name                                                        \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...  Intergenic   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...  Intergenic   \n",
       "SC_Control_IP_Merged_peak_100020                    Intergenic   \n",
       "SC_Control_IP_Merged_peak_100028                    Intergenic   \n",
       "SC_Control_IP_Merged_peak_10003                      Gene_Body   \n",
       "\n",
       "                                                    Distance_From_TSS  \n",
       "Peak_Name                                                              \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...                NaN  \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...                NaN  \n",
       "SC_Control_IP_Merged_peak_100020                                  NaN  \n",
       "SC_Control_IP_Merged_peak_100028                                  NaN  \n",
       "SC_Control_IP_Merged_peak_10003                               23476.0  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_hMeDIP_FPKM_table.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I have to figure out how to sum the counts per gene for both location and change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleanup = SC_VitC_hMeDIP_FPKM_table[[\"Change\", \"Gene_ID\", \"Gene_Region\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "summed_counts = pd.DataFrame(cleanup.groupby([\"Gene_ID\", \"Gene_Region\", \"Change\"], as_index = False).size().unstack().unstack())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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      "text/plain": [
       "Change                   Downregulated                                  \\\n",
       "Gene_Region        Downstream_Promoter Upstream_Promoter Gene_Body TSS   \n",
       "Gene_ID                                                                  \n",
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       "ENSRNOG00000000065                 NaN               NaN       1.0 NaN   \n",
       "ENSRNOG00000000066                 NaN               NaN       1.0 NaN   \n",
       "ENSRNOG00000000068                 NaN               NaN       1.0 NaN   \n",
       "ENSRNOG00000000075                 1.0               NaN       1.0 NaN   \n",
       "ENSRNOG00000000081                 NaN               NaN       NaN NaN   \n",
       "ENSRNOG00000000082                 NaN               NaN       1.0 NaN   \n",
       "ENSRNOG00000000095                 NaN               NaN       NaN NaN   \n",
       "ENSRNOG00000000096                 NaN               NaN       NaN NaN   \n",
       "ENSRNOG00000000098                 NaN               NaN       1.0 NaN   \n",
       "ENSRNOG00000000104                 1.0               1.0       NaN NaN   \n",
       "ENSRNOG00000000105                 NaN               NaN       1.0 NaN   \n",
       "ENSRNOG00000000111                 NaN               NaN       NaN NaN   \n",
       "\n",
       "Change                 Nondifferential                                   \\\n",
       "Gene_Region        Downstream_Promoter Upstream_Promoter Gene_Body  TSS   \n",
       "Gene_ID                                                                   \n",
       "ENSRNOG00000000001                 1.0               NaN       NaN  NaN   \n",
       "ENSRNOG00000000007                 NaN               NaN       NaN  NaN   \n",
       "ENSRNOG00000000008                 NaN               NaN       4.0  NaN   \n",
       "ENSRNOG00000000009                 NaN               NaN       NaN  NaN   \n",
       "ENSRNOG00000000017                 NaN               NaN       NaN  NaN   \n",
       "ENSRNOG00000000024                 NaN               NaN       5.0  NaN   \n",
       "ENSRNOG00000000033                 1.0               1.0       4.0  NaN   \n",
       "ENSRNOG00000000034                 NaN               NaN       NaN  NaN   \n",
       "ENSRNOG00000000036                 NaN               1.0       NaN  NaN   \n",
       "ENSRNOG00000000040                 1.0               NaN       1.0  1.0   \n",
       "ENSRNOG00000000041                 NaN               NaN       NaN  NaN   \n",
       "ENSRNOG00000000042                 1.0               NaN       6.0  NaN   \n",
       "ENSRNOG00000000044                 1.0               NaN       NaN  NaN   \n",
       "ENSRNOG00000000047                 2.0               1.0       3.0  NaN   \n",
       "ENSRNOG00000000048                 NaN               NaN       2.0  NaN   \n",
       "ENSRNOG00000000053                 NaN               NaN       NaN  NaN   \n",
       "ENSRNOG00000000054                 NaN               NaN       1.0  NaN   \n",
       "ENSRNOG00000000062                 NaN               NaN       NaN  NaN   \n",
       "ENSRNOG00000000065                 NaN               NaN       NaN  NaN   \n",
       "ENSRNOG00000000066                 NaN               NaN       NaN  NaN   \n",
       "ENSRNOG00000000068                 NaN               NaN       1.0  NaN   \n",
       "ENSRNOG00000000075                 NaN               NaN       1.0  NaN   \n",
       "ENSRNOG00000000081                 1.0               NaN       5.0  NaN   \n",
       "ENSRNOG00000000082                 NaN               NaN       NaN  NaN   \n",
       "ENSRNOG00000000095                 NaN               NaN       NaN  NaN   \n",
       "ENSRNOG00000000096                 NaN               NaN       2.0  NaN   \n",
       "ENSRNOG00000000098                 NaN               NaN       4.0  NaN   \n",
       "ENSRNOG00000000104                 NaN               NaN       NaN  NaN   \n",
       "ENSRNOG00000000105                 1.0               NaN       9.0  NaN   \n",
       "ENSRNOG00000000111                 NaN               2.0       NaN  NaN   \n",
       "\n",
       "Change                     Upregulated                                   \n",
       "Gene_Region        Downstream_Promoter Upstream_Promoter Gene_Body  TSS  \n",
       "Gene_ID                                                                  \n",
       "ENSRNOG00000000001                 NaN               NaN       NaN  NaN  \n",
       "ENSRNOG00000000007                 NaN               NaN       1.0  NaN  \n",
       "ENSRNOG00000000008                 1.0               1.0       4.0  NaN  \n",
       "ENSRNOG00000000009                 NaN               NaN       1.0  NaN  \n",
       "ENSRNOG00000000017                 NaN               NaN       NaN  NaN  \n",
       "ENSRNOG00000000024                 NaN               NaN       2.0  NaN  \n",
       "ENSRNOG00000000033                 NaN               NaN       8.0  NaN  \n",
       "ENSRNOG00000000034                 NaN               NaN       3.0  NaN  \n",
       "ENSRNOG00000000036                 NaN               NaN       NaN  1.0  \n",
       "ENSRNOG00000000040                 NaN               2.0       3.0  NaN  \n",
       "ENSRNOG00000000041                 NaN               1.0       NaN  NaN  \n",
       "ENSRNOG00000000042                 NaN               NaN       7.0  NaN  \n",
       "ENSRNOG00000000044                 NaN               NaN       NaN  NaN  \n",
       "ENSRNOG00000000047                 NaN               1.0      11.0  NaN  \n",
       "ENSRNOG00000000048                 1.0               NaN       3.0  NaN  \n",
       "ENSRNOG00000000053                 NaN               NaN       NaN  NaN  \n",
       "ENSRNOG00000000054                 NaN               NaN       NaN  NaN  \n",
       "ENSRNOG00000000062                 1.0               NaN       4.0  NaN  \n",
       "ENSRNOG00000000065                 NaN               NaN       1.0  NaN  \n",
       "ENSRNOG00000000066                 2.0               NaN       3.0  NaN  \n",
       "ENSRNOG00000000068                 1.0               NaN       5.0  NaN  \n",
       "ENSRNOG00000000075                 NaN               NaN       3.0  NaN  \n",
       "ENSRNOG00000000081                 NaN               1.0      19.0  NaN  \n",
       "ENSRNOG00000000082                 NaN               NaN       4.0  NaN  \n",
       "ENSRNOG00000000095                 NaN               NaN       3.0  NaN  \n",
       "ENSRNOG00000000096                 NaN               NaN       NaN  NaN  \n",
       "ENSRNOG00000000098                 NaN               NaN       4.0  NaN  \n",
       "ENSRNOG00000000104                 NaN               NaN       2.0  NaN  \n",
       "ENSRNOG00000000105                 NaN               NaN       3.0  NaN  \n",
       "ENSRNOG00000000111                 NaN               NaN       NaN  NaN  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summed_counts.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "summed_counts.columns = [\"Downstream_Promoter_Downregulated\", \"Upstream_Promoter_Downregulated\", \"Gene_Body_Downregulated\",\n",
    "                        \"TSS_Downregulated\", \"Downstream_Promoter_Nondifferential\",\"Upstream_Promoter_Nondifferential\",\n",
    "                        \"Gene_Body_Nondifferential\",\"TSS_Nondifferential\",\"Downstream_Promoter_Upregulated\",\n",
    "                         \"Upstream_Promoter_Upregulated\",\"Gene_Body_Upregulated\", \"TSS_Upregulated\"]\n",
    "\n",
    "  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "summed_counts = summed_counts.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Downstream_Promoter_Downregulated</th>\n",
       "      <th>Upstream_Promoter_Downregulated</th>\n",
       "      <th>Gene_Body_Downregulated</th>\n",
       "      <th>TSS_Downregulated</th>\n",
       "      <th>Downstream_Promoter_Nondifferential</th>\n",
       "      <th>Upstream_Promoter_Nondifferential</th>\n",
       "      <th>Gene_Body_Nondifferential</th>\n",
       "      <th>TSS_Nondifferential</th>\n",
       "      <th>Downstream_Promoter_Upregulated</th>\n",
       "      <th>Upstream_Promoter_Upregulated</th>\n",
       "      <th>Gene_Body_Upregulated</th>\n",
       "      <th>TSS_Upregulated</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gene_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000001</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>ENSRNOG00000000007</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000008</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000009</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000017</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Downstream_Promoter_Downregulated  \\\n",
       "Gene_ID                                                 \n",
       "ENSRNOG00000000001                                0.0   \n",
       "ENSRNOG00000000007                                0.0   \n",
       "ENSRNOG00000000008                                0.0   \n",
       "ENSRNOG00000000009                                0.0   \n",
       "ENSRNOG00000000017                                0.0   \n",
       "\n",
       "                    Upstream_Promoter_Downregulated  Gene_Body_Downregulated  \\\n",
       "Gene_ID                                                                        \n",
       "ENSRNOG00000000001                              1.0                      0.0   \n",
       "ENSRNOG00000000007                              0.0                      2.0   \n",
       "ENSRNOG00000000008                              0.0                      0.0   \n",
       "ENSRNOG00000000009                              0.0                      0.0   \n",
       "ENSRNOG00000000017                              1.0                      0.0   \n",
       "\n",
       "                    TSS_Downregulated  Downstream_Promoter_Nondifferential  \\\n",
       "Gene_ID                                                                      \n",
       "ENSRNOG00000000001                0.0                                  1.0   \n",
       "ENSRNOG00000000007                0.0                                  0.0   \n",
       "ENSRNOG00000000008                0.0                                  0.0   \n",
       "ENSRNOG00000000009                0.0                                  0.0   \n",
       "ENSRNOG00000000017                0.0                                  0.0   \n",
       "\n",
       "                    Upstream_Promoter_Nondifferential  \\\n",
       "Gene_ID                                                 \n",
       "ENSRNOG00000000001                                0.0   \n",
       "ENSRNOG00000000007                                0.0   \n",
       "ENSRNOG00000000008                                0.0   \n",
       "ENSRNOG00000000009                                0.0   \n",
       "ENSRNOG00000000017                                0.0   \n",
       "\n",
       "                    Gene_Body_Nondifferential  TSS_Nondifferential  \\\n",
       "Gene_ID                                                              \n",
       "ENSRNOG00000000001                        0.0                  0.0   \n",
       "ENSRNOG00000000007                        0.0                  0.0   \n",
       "ENSRNOG00000000008                        4.0                  0.0   \n",
       "ENSRNOG00000000009                        0.0                  0.0   \n",
       "ENSRNOG00000000017                        0.0                  0.0   \n",
       "\n",
       "                    Downstream_Promoter_Upregulated  \\\n",
       "Gene_ID                                               \n",
       "ENSRNOG00000000001                              0.0   \n",
       "ENSRNOG00000000007                              0.0   \n",
       "ENSRNOG00000000008                              1.0   \n",
       "ENSRNOG00000000009                              0.0   \n",
       "ENSRNOG00000000017                              0.0   \n",
       "\n",
       "                    Upstream_Promoter_Upregulated  Gene_Body_Upregulated  \\\n",
       "Gene_ID                                                                    \n",
       "ENSRNOG00000000001                            0.0                    0.0   \n",
       "ENSRNOG00000000007                            0.0                    1.0   \n",
       "ENSRNOG00000000008                            1.0                    4.0   \n",
       "ENSRNOG00000000009                            0.0                    1.0   \n",
       "ENSRNOG00000000017                            0.0                    0.0   \n",
       "\n",
       "                    TSS_Upregulated  \n",
       "Gene_ID                              \n",
       "ENSRNOG00000000001              0.0  \n",
       "ENSRNOG00000000007              0.0  \n",
       "ENSRNOG00000000008              0.0  \n",
       "ENSRNOG00000000009              0.0  \n",
       "ENSRNOG00000000017              0.0  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summed_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "summed_counts.to_csv(Output_Path+\"SC_VitC_Counts_of_peaks_per_region.csv\")\n",
    "summed_counts.to_pickle(Output_Path+\"SC_VitC_Counts_of_peaks_per_region.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16234"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(summed_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Moving on to the RNA-seq data\n",
    "Now I have officially gotten the hMeDIP-seq info cleaned up (removed outliers) and normalized correctly, then converted it to FPKM using the width of the peaks (from the bed file). Next I was able to get the counts of peaks within each gene region for each annotated gene. \n",
    "\n",
    "The next step is to normalize the RNA-seq data similar to the way we did for the hMeDIP-seq, except removing the htseq-count lines before the first RCPM. This again requires finding outliers for correct normalization and using the length of each gene (exonic length, not total genomic length) to calculate the FPKM values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_Raw_RNA_data = pd.read_csv(Input_RNA_Counts_File, sep = \"\\t\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SC_Control_01</th>\n",
       "      <th>SC_Control_02</th>\n",
       "      <th>SC_Control_03</th>\n",
       "      <th>SC_VitC_01</th>\n",
       "      <th>SC_VitC_02</th>\n",
       "      <th>SC_VitC_03</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ensembl_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000001</th>\n",
       "      <td>25</td>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "      <td>25</td>\n",
       "      <td>33</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000007</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000008</th>\n",
       "      <td>104</td>\n",
       "      <td>127</td>\n",
       "      <td>92</td>\n",
       "      <td>203</td>\n",
       "      <td>210</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000009</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000010</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    SC_Control_01  SC_Control_02  SC_Control_03  SC_VitC_01  \\\n",
       "Ensembl_ID                                                                    \n",
       "ENSRNOG00000000001             25             21             23          25   \n",
       "ENSRNOG00000000007              0              0              0           1   \n",
       "ENSRNOG00000000008            104            127             92         203   \n",
       "ENSRNOG00000000009              0              0              0           0   \n",
       "ENSRNOG00000000010              2              2              1           3   \n",
       "\n",
       "                    SC_VitC_02  SC_VitC_03  \n",
       "Ensembl_ID                                  \n",
       "ENSRNOG00000000001          33          24  \n",
       "ENSRNOG00000000007           0           1  \n",
       "ENSRNOG00000000008         210         231  \n",
       "ENSRNOG00000000009           0           1  \n",
       "ENSRNOG00000000010           2           5  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_Raw_RNA_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For this one I need to remove the HTSeq columns before I normalize because unmapped reads will interefere with RNA counts\n",
    "SC_VitC_Raw_RNA_data = SC_VitC_Raw_RNA_data.drop(columns_added_by_htseq, axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_counts_SC_VitC_RNA_Raw = SC_VitC_Raw_RNA_data.sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SC_Control_01    26831716\n",
       "SC_Control_02    33173282\n",
       "SC_Control_03    23381814\n",
       "SC_VitC_01       26556357\n",
       "SC_VitC_02       28090349\n",
       "SC_VitC_03       28564007\n",
       "dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_counts_SC_VitC_RNA_Raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_Raw_RNA_data[\"Control_1_first_RCPM\"] = SC_VitC_Raw_RNA_data[\"SC_Control_01\"]*1000000/total_counts_SC_VitC_RNA_Raw[0]\n",
    "SC_VitC_Raw_RNA_data[\"Control_2_first_RCPM\"] = SC_VitC_Raw_RNA_data[\"SC_Control_02\"]*1000000/total_counts_SC_VitC_RNA_Raw[1]\n",
    "SC_VitC_Raw_RNA_data[\"Control_3_first_RCPM\"] = SC_VitC_Raw_RNA_data[\"SC_Control_03\"]*1000000/total_counts_SC_VitC_RNA_Raw[2]\n",
    "SC_VitC_Raw_RNA_data[\"VitC_1_first_RCPM\"] = SC_VitC_Raw_RNA_data[\"SC_VitC_01\"]*1000000/total_counts_SC_VitC_RNA_Raw[3]\n",
    "SC_VitC_Raw_RNA_data[\"VitC_2_first_RCPM\"] = SC_VitC_Raw_RNA_data[\"SC_VitC_02\"]*1000000/total_counts_SC_VitC_RNA_Raw[4]\n",
    "SC_VitC_Raw_RNA_data[\"VitC_3_first_RCPM\"] = SC_VitC_Raw_RNA_data[\"SC_VitC_03\"]*1000000/total_counts_SC_VitC_RNA_Raw[5]\n",
    "SC_VitC_Raw_RNA_data[\"Control_Average\"] = SC_VitC_Raw_RNA_data[[\"Control_1_first_RCPM\", \"Control_2_first_RCPM\", \"Control_3_first_RCPM\"]].mean(axis=1)\n",
    "SC_VitC_Raw_RNA_data[\"VitC_Average\"] = SC_VitC_Raw_RNA_data[[\"VitC_1_first_RCPM\", \"VitC_2_first_RCPM\", \"VitC_3_first_RCPM\"]].mean(axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SC_Control_01</th>\n",
       "      <th>SC_Control_02</th>\n",
       "      <th>SC_Control_03</th>\n",
       "      <th>SC_VitC_01</th>\n",
       "      <th>SC_VitC_02</th>\n",
       "      <th>SC_VitC_03</th>\n",
       "      <th>Control_1_first_RCPM</th>\n",
       "      <th>Control_2_first_RCPM</th>\n",
       "      <th>Control_3_first_RCPM</th>\n",
       "      <th>VitC_1_first_RCPM</th>\n",
       "      <th>VitC_2_first_RCPM</th>\n",
       "      <th>VitC_3_first_RCPM</th>\n",
       "      <th>Control_Average</th>\n",
       "      <th>VitC_Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ensembl_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000001</th>\n",
       "      <td>25</td>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "      <td>25</td>\n",
       "      <td>33</td>\n",
       "      <td>24</td>\n",
       "      <td>0.931733</td>\n",
       "      <td>0.633040</td>\n",
       "      <td>0.983670</td>\n",
       "      <td>0.941394</td>\n",
       "      <td>1.174781</td>\n",
       "      <td>0.840218</td>\n",
       "      <td>0.849481</td>\n",
       "      <td>0.985464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000007</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.037656</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035009</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.024222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000008</th>\n",
       "      <td>104</td>\n",
       "      <td>127</td>\n",
       "      <td>92</td>\n",
       "      <td>203</td>\n",
       "      <td>210</td>\n",
       "      <td>231</td>\n",
       "      <td>3.876010</td>\n",
       "      <td>3.828382</td>\n",
       "      <td>3.934682</td>\n",
       "      <td>7.644121</td>\n",
       "      <td>7.475877</td>\n",
       "      <td>8.087101</td>\n",
       "      <td>3.879691</td>\n",
       "      <td>7.735700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000009</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035009</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000010</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.074539</td>\n",
       "      <td>0.060289</td>\n",
       "      <td>0.042768</td>\n",
       "      <td>0.112967</td>\n",
       "      <td>0.071199</td>\n",
       "      <td>0.175045</td>\n",
       "      <td>0.059199</td>\n",
       "      <td>0.119737</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    SC_Control_01  SC_Control_02  SC_Control_03  SC_VitC_01  \\\n",
       "Ensembl_ID                                                                    \n",
       "ENSRNOG00000000001             25             21             23          25   \n",
       "ENSRNOG00000000007              0              0              0           1   \n",
       "ENSRNOG00000000008            104            127             92         203   \n",
       "ENSRNOG00000000009              0              0              0           0   \n",
       "ENSRNOG00000000010              2              2              1           3   \n",
       "\n",
       "                    SC_VitC_02  SC_VitC_03  Control_1_first_RCPM  \\\n",
       "Ensembl_ID                                                         \n",
       "ENSRNOG00000000001          33          24              0.931733   \n",
       "ENSRNOG00000000007           0           1              0.000000   \n",
       "ENSRNOG00000000008         210         231              3.876010   \n",
       "ENSRNOG00000000009           0           1              0.000000   \n",
       "ENSRNOG00000000010           2           5              0.074539   \n",
       "\n",
       "                    Control_2_first_RCPM  Control_3_first_RCPM  \\\n",
       "Ensembl_ID                                                       \n",
       "ENSRNOG00000000001              0.633040              0.983670   \n",
       "ENSRNOG00000000007              0.000000              0.000000   \n",
       "ENSRNOG00000000008              3.828382              3.934682   \n",
       "ENSRNOG00000000009              0.000000              0.000000   \n",
       "ENSRNOG00000000010              0.060289              0.042768   \n",
       "\n",
       "                    VitC_1_first_RCPM  VitC_2_first_RCPM  VitC_3_first_RCPM  \\\n",
       "Ensembl_ID                                                                    \n",
       "ENSRNOG00000000001           0.941394           1.174781           0.840218   \n",
       "ENSRNOG00000000007           0.037656           0.000000           0.035009   \n",
       "ENSRNOG00000000008           7.644121           7.475877           8.087101   \n",
       "ENSRNOG00000000009           0.000000           0.000000           0.035009   \n",
       "ENSRNOG00000000010           0.112967           0.071199           0.175045   \n",
       "\n",
       "                    Control_Average  VitC_Average  \n",
       "Ensembl_ID                                         \n",
       "ENSRNOG00000000001         0.849481      0.985464  \n",
       "ENSRNOG00000000007         0.000000      0.024222  \n",
       "ENSRNOG00000000008         3.879691      7.735700  \n",
       "ENSRNOG00000000009         0.000000      0.011670  \n",
       "ENSRNOG00000000010         0.059199      0.119737  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_Raw_RNA_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_RNA_first_RCPM_averages = SC_VitC_Raw_RNA_data[[\"Control_Average\",\"VitC_Average\" ]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "                    Control_Average  VitC_Average\n",
       "Ensembl_ID                                       \n",
       "ENSRNOG00000000001         0.849481      0.985464\n",
       "ENSRNOG00000000007         0.000000      0.024222\n",
       "ENSRNOG00000000008         3.879691      7.735700\n",
       "ENSRNOG00000000009         0.000000      0.011670\n",
       "ENSRNOG00000000010         0.059199      0.119737"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_RNA_first_RCPM_averages.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Control_Average</th>\n",
       "      <th>VitC_Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>32883.000000</td>\n",
       "      <td>32883.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>30.410851</td>\n",
       "      <td>30.410851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>186.057067</td>\n",
       "      <td>184.819772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.024846</td>\n",
       "      <td>0.035599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13.284759</td>\n",
       "      <td>12.792898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14353.838049</td>\n",
       "      <td>12475.498538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Control_Average  VitC_Average\n",
       "count     32883.000000  32883.000000\n",
       "mean         30.410851     30.410851\n",
       "std         186.057067    184.819772\n",
       "min           0.000000      0.000000\n",
       "25%           0.000000      0.000000\n",
       "50%           0.024846      0.035599\n",
       "75%          13.284759     12.792898\n",
       "max       14353.838049  12475.498538"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_RNA_first_RCPM_averages.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Control_Average</th>\n",
       "      <th>VitC_Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.70</th>\n",
       "      <td>4.878563</td>\n",
       "      <td>5.076483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>13.284759</td>\n",
       "      <td>12.792898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.80</th>\n",
       "      <td>25.209702</td>\n",
       "      <td>24.301869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.90</th>\n",
       "      <td>69.856685</td>\n",
       "      <td>68.021038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.95</th>\n",
       "      <td>133.887396</td>\n",
       "      <td>130.155733</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Control_Average  VitC_Average\n",
       "0.70         4.878563      5.076483\n",
       "0.75        13.284759     12.792898\n",
       "0.80        25.209702     24.301869\n",
       "0.90        69.856685     68.021038\n",
       "0.95       133.887396    130.155733"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_RNA_first_RCPM_averages.quantile([0.7, 0.75, 0.8, 0.9, 0.95])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well, this describe function wasn't as useful as I had hoped it would be. I forgot that the human genome (and apparently the rate genome) has many annotated transcripts that are only coding in one or two cell types and only during some points of development, so almost half of these have an average value of 0 for both control and vitamin C. \n",
    "<br>When I look at quantiles closer to 1 I get something useful. The high percentiles all look quite close, which is a good sign that they aren't completely different like cells from another tissue or a different animal type. The standard deviations are quite close for both too. This looks good, like they aren't too different from eachother.\n",
    "\n",
    "Anyways, the next step is to identify outliers and re-normalize without those ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>VitC_Average</td>   <th>  R-squared:         </th>  <td>   0.885</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.885</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>2.537e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 16 Apr 2019</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>20:49:18</td>     <th>  Log-Likelihood:    </th> <td>-1.8269e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 32883</td>      <th>  AIC:               </th>  <td>3.654e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 32881</td>      <th>  BIC:               </th>  <td>3.654e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>       <td>    1.9878</td> <td>    0.350</td> <td>    5.683</td> <td> 0.000</td> <td>    1.302</td> <td>    2.673</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Control_Average</th> <td>    0.9346</td> <td>    0.002</td> <td>  503.712</td> <td> 0.000</td> <td>    0.931</td> <td>    0.938</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>79956.750</td> <th>  Durbin-Watson:     </th>    <td>   2.002</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>11371128734.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td>24.440</td>   <th>  Prob(JB):          </th>    <td>    0.00</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>2883.442</td>  <th>  Cond. No.          </th>    <td>    191.</td>    \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:           VitC_Average   R-squared:                       0.885\n",
       "Model:                            OLS   Adj. R-squared:                  0.885\n",
       "Method:                 Least Squares   F-statistic:                 2.537e+05\n",
       "Date:                Tue, 16 Apr 2019   Prob (F-statistic):               0.00\n",
       "Time:                        20:49:18   Log-Likelihood:            -1.8269e+05\n",
       "No. Observations:               32883   AIC:                         3.654e+05\n",
       "Df Residuals:                   32881   BIC:                         3.654e+05\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===================================================================================\n",
       "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------\n",
       "Intercept           1.9878      0.350      5.683      0.000       1.302       2.673\n",
       "Control_Average     0.9346      0.002    503.712      0.000       0.931       0.938\n",
       "==============================================================================\n",
       "Omnibus:                    79956.750   Durbin-Watson:                   2.002\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):      11371128734.013\n",
       "Skew:                          24.440   Prob(JB):                         0.00\n",
       "Kurtosis:                    2883.442   Cond. No.                         191.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model_RNA = sm.ols(formula=\"VitC_Average ~ Control_Average\", data=SC_VitC_RNA_first_RCPM_averages).fit()\n",
    "linear_model_RNA.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/david/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/Users/david/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "influence_RNA = linear_model_RNA.get_influence()\n",
    "(cook,p_cook) = influence_RNA.cooks_distance\n",
    "SC_VitC_RNA_first_RCPM_averages[\"Cook_Value\"] = cook\n",
    "SC_VitC_RNA_first_RCPM_averages[\"Cook_P_Value\"] = p_cook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "                    Control_Average  VitC_Average    Cook_Value  Cook_P_Value\n",
       "Ensembl_ID                                                                   \n",
       "ENSRNOG00000000001         0.849481      0.985464  1.283694e-08           1.0\n",
       "ENSRNOG00000000007         0.000000      0.024222  1.536125e-08           1.0\n",
       "ENSRNOG00000000008         3.879691      7.735700  1.782349e-08           1.0\n",
       "ENSRNOG00000000009         0.000000      0.011670  1.555826e-08           1.0\n",
       "ENSRNOG00000000010         0.059199      0.119737  1.473744e-08           1.0"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_RNA_first_RCPM_averages.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_RNA_removing_outliers = SC_VitC_RNA_first_RCPM_averages[SC_VitC_RNA_first_RCPM_averages.Cook_Value <= 1]\n",
    "SC_VitC_RNA_outliers = SC_VitC_RNA_first_RCPM_averages[SC_VitC_RNA_first_RCPM_averages.Cook_Value > 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>9.753595e-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Control_Average  VitC_Average  Cook_Value   Cook_P_Value\n",
       "Ensembl_ID                                                                  \n",
       "ENSRNOG00000003897      4574.687404   9817.760658   73.820231   1.028118e-32\n",
       "ENSRNOG00000005434      5963.014015   6925.652343    7.668706   4.680581e-04\n",
       "ENSRNOG00000010286      2146.591522    484.289162    1.184151   3.060188e-01\n",
       "ENSRNOG00000012840      3766.567018   6317.930932   12.565104   3.508559e-06\n",
       "ENSRNOG00000017206      3284.156573   1711.751343    2.242735   1.061840e-01\n",
       "ENSRNOG00000018087     14353.838049  12475.498538   30.376542   6.604012e-14\n",
       "ENSRNOG00000034234      9366.399047   8314.980552    2.230774   1.074615e-01\n",
       "ENSRNOG00000043866      5809.953384   7751.716021   21.403929   5.133784e-10\n",
       "ENSRNOG00000046005     11934.208236   7801.392922  233.260675  2.559223e-101\n",
       "ENSRNOG00000054493     11506.308482   7497.607273  200.502059   2.816843e-87\n",
       "ENSRNOG00000057569      5431.755526   7931.234175   28.061756   6.657687e-13\n",
       "ENSRNOG00000058470      1552.247776   4418.969375    2.327699   9.753595e-02"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_RNA_outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>VitC_Average</td>   <th>  R-squared:         </th>  <td>   0.918</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.918</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>3.666e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 16 Apr 2019</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>20:49:18</td>     <th>  Log-Likelihood:    </th> <td>-1.6235e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 32871</td>      <th>  AIC:               </th>  <td>3.247e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 32869</td>      <th>  BIC:               </th>  <td>3.247e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>       <td>    0.0797</td> <td>    0.192</td> <td>    0.415</td> <td> 0.678</td> <td>   -0.297</td> <td>    0.456</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Control_Average</th> <td>    0.9952</td> <td>    0.002</td> <td>  605.473</td> <td> 0.000</td> <td>    0.992</td> <td>    0.998</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>54499.864</td> <th>  Durbin-Watson:     </th>   <td>   2.015</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>426831866.563</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td>10.248</td>   <th>  Prob(JB):          </th>   <td>    0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>560.872</td>  <th>  Cond. No.          </th>   <td>    120.</td>   \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:           VitC_Average   R-squared:                       0.918\n",
       "Model:                            OLS   Adj. R-squared:                  0.918\n",
       "Method:                 Least Squares   F-statistic:                 3.666e+05\n",
       "Date:                Tue, 16 Apr 2019   Prob (F-statistic):               0.00\n",
       "Time:                        20:49:18   Log-Likelihood:            -1.6235e+05\n",
       "No. Observations:               32871   AIC:                         3.247e+05\n",
       "Df Residuals:                   32869   BIC:                         3.247e+05\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===================================================================================\n",
       "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------\n",
       "Intercept           0.0797      0.192      0.415      0.678      -0.297       0.456\n",
       "Control_Average     0.9952      0.002    605.473      0.000       0.992       0.998\n",
       "==============================================================================\n",
       "Omnibus:                    54499.864   Durbin-Watson:                   2.015\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):        426831866.563\n",
       "Skew:                          10.248   Prob(JB):                         0.00\n",
       "Kurtosis:                     560.872   Cond. No.                         120.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outliers_removed_model_RNA = sm.ols(formula=\"VitC_Average ~ Control_Average\", data=SC_VitC_RNA_removing_outliers).fit()\n",
    "outliers_removed_model_RNA.summary()\n",
    "# This one has a slightly higher R-squared value. \n",
    "# Now I want to see what it looks like in the graph. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#outliers = ARPE_hMeDIP_outliers[\"VitC_Average\"].values\n",
    "fig, tmp = plt.subplots()\n",
    "ax = SC_VitC_RNA_removing_outliers.plot.scatter(y = \"VitC_Average\", x = \"Control_Average\", \n",
    "                                                ax=tmp, color = 'k', label = \"Not Outliers\", s = 10)\n",
    "ax.scatter(y = SC_VitC_RNA_outliers[\"VitC_Average\"], x = SC_VitC_RNA_outliers[\"Control_Average\"], \n",
    "           color = 'r', label=\"Outliers\", s = 10)\n",
    "\n",
    "x_straight = sc.linspace(0, 25000, 10)\n",
    "y_straight = x_straight # I want this line to be a straight line with a slope of 1\n",
    "\n",
    "plt.title(\"Linear Regression RNA Control vs Vit C\", color='black')\n",
    "plt.ylabel(\"Vitamin C\", color='black')\n",
    "plt.xlabel(\"Control\", color='black')\n",
    "ax.set_facecolor('white')\n",
    "ax.tick_params(color='black', labelcolor='black')\n",
    "ax.spines['bottom'].set_color('black')\n",
    "ax.spines['left'].set_color('black')\n",
    "ax.grid(color='lightslategray', linestyle='--', linewidth=0.5, alpha=0.4)\n",
    "plt.tick_params(axis = 'both', which = 'major', labelsize='10')\n",
    "\n",
    "parameters = linear_model_RNA.params\n",
    "x = sc.linspace(0, 25000, 10)\n",
    "y = parameters[0] + parameters[1]*x\n",
    "\n",
    "\n",
    "outlier_parameters = outliers_removed_model_RNA.params\n",
    "#x = sc.linspace(0, 350, 10)\n",
    "outlier_y = outlier_parameters[0] + outlier_parameters[1]*x\n",
    "\n",
    "\n",
    "plt.plot(x_straight,y_straight,label=\"Slope = 1\", linewidth=2, color='k')\n",
    "plt.plot(x,y,label=\"Regression Line\", linewidth=2, color='r')\n",
    "plt.plot(x,outlier_y,label=\"Removing Outliers Regression Line\", linewidth=2, color='g')\n",
    "plt.legend(loc='upper left')\n",
    "plt.savefig(Output_Path+\"SC_VitC_RNA_Finding_Outliers.png\", dpi=300) # size above specified at 10x6\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I was surprised to find that removing the outliers shifted the slope almost exactly to a slope of 1. The R2 values were extremely high (91.8% in the outliers removed model). It looks like it normalized well. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32871\n",
      "32883\n",
      "12\n"
     ]
    }
   ],
   "source": [
    "print(len(SC_VitC_RNA_removing_outliers))\n",
    "print(len(SC_VitC_RNA_first_RCPM_averages))\n",
    "print(len(SC_VitC_RNA_outliers))\n",
    "\n",
    "# Difference of 12, which is the number of outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "remove_outliers_RNA = SC_VitC_Raw_RNA_data.drop(SC_VitC_RNA_outliers.index, axis = 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_counts_removing_outliers_RNA = remove_outliers_RNA.sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SC_Control_01           2.468742e+07\n",
       "SC_Control_02           3.051495e+07\n",
       "SC_Control_03           2.153423e+07\n",
       "SC_VitC_01              2.432794e+07\n",
       "SC_VitC_02              2.579432e+07\n",
       "SC_VitC_03              2.631612e+07\n",
       "Control_1_first_RCPM    9.200837e+05\n",
       "Control_2_first_RCPM    9.198652e+05\n",
       "Control_3_first_RCPM    9.209819e+05\n",
       "VitC_1_first_RCPM       9.160873e+05\n",
       "VitC_2_first_RCPM       9.182628e+05\n",
       "VitC_3_first_RCPM       9.213036e+05\n",
       "Control_Average         9.203103e+05\n",
       "VitC_Average            9.185512e+05\n",
       "dtype: float64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_counts_removing_outliers_RNA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>SC_Control_01</th>\n",
       "      <th>SC_Control_02</th>\n",
       "      <th>SC_Control_03</th>\n",
       "      <th>SC_VitC_01</th>\n",
       "      <th>SC_VitC_02</th>\n",
       "      <th>SC_VitC_03</th>\n",
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       "      <th>Control_3_first_RCPM</th>\n",
       "      <th>VitC_1_first_RCPM</th>\n",
       "      <th>VitC_2_first_RCPM</th>\n",
       "      <th>VitC_3_first_RCPM</th>\n",
       "      <th>Control_Average</th>\n",
       "      <th>VitC_Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ensembl_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>ENSRNOG00000000001</th>\n",
       "      <td>25</td>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "      <td>25</td>\n",
       "      <td>33</td>\n",
       "      <td>24</td>\n",
       "      <td>0.931733</td>\n",
       "      <td>0.633040</td>\n",
       "      <td>0.983670</td>\n",
       "      <td>0.941394</td>\n",
       "      <td>1.174781</td>\n",
       "      <td>0.840218</td>\n",
       "      <td>0.849481</td>\n",
       "      <td>0.985464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000007</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.037656</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035009</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.024222</td>\n",
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       "    <tr>\n",
       "      <th>ENSRNOG00000000008</th>\n",
       "      <td>104</td>\n",
       "      <td>127</td>\n",
       "      <td>92</td>\n",
       "      <td>203</td>\n",
       "      <td>210</td>\n",
       "      <td>231</td>\n",
       "      <td>3.876010</td>\n",
       "      <td>3.828382</td>\n",
       "      <td>3.934682</td>\n",
       "      <td>7.644121</td>\n",
       "      <td>7.475877</td>\n",
       "      <td>8.087101</td>\n",
       "      <td>3.879691</td>\n",
       "      <td>7.735700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000009</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035009</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000010</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.074539</td>\n",
       "      <td>0.060289</td>\n",
       "      <td>0.042768</td>\n",
       "      <td>0.112967</td>\n",
       "      <td>0.071199</td>\n",
       "      <td>0.175045</td>\n",
       "      <td>0.059199</td>\n",
       "      <td>0.119737</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    SC_Control_01  SC_Control_02  SC_Control_03  SC_VitC_01  \\\n",
       "Ensembl_ID                                                                    \n",
       "ENSRNOG00000000001             25             21             23          25   \n",
       "ENSRNOG00000000007              0              0              0           1   \n",
       "ENSRNOG00000000008            104            127             92         203   \n",
       "ENSRNOG00000000009              0              0              0           0   \n",
       "ENSRNOG00000000010              2              2              1           3   \n",
       "\n",
       "                    SC_VitC_02  SC_VitC_03  Control_1_first_RCPM  \\\n",
       "Ensembl_ID                                                         \n",
       "ENSRNOG00000000001          33          24              0.931733   \n",
       "ENSRNOG00000000007           0           1              0.000000   \n",
       "ENSRNOG00000000008         210         231              3.876010   \n",
       "ENSRNOG00000000009           0           1              0.000000   \n",
       "ENSRNOG00000000010           2           5              0.074539   \n",
       "\n",
       "                    Control_2_first_RCPM  Control_3_first_RCPM  \\\n",
       "Ensembl_ID                                                       \n",
       "ENSRNOG00000000001              0.633040              0.983670   \n",
       "ENSRNOG00000000007              0.000000              0.000000   \n",
       "ENSRNOG00000000008              3.828382              3.934682   \n",
       "ENSRNOG00000000009              0.000000              0.000000   \n",
       "ENSRNOG00000000010              0.060289              0.042768   \n",
       "\n",
       "                    VitC_1_first_RCPM  VitC_2_first_RCPM  VitC_3_first_RCPM  \\\n",
       "Ensembl_ID                                                                    \n",
       "ENSRNOG00000000001           0.941394           1.174781           0.840218   \n",
       "ENSRNOG00000000007           0.037656           0.000000           0.035009   \n",
       "ENSRNOG00000000008           7.644121           7.475877           8.087101   \n",
       "ENSRNOG00000000009           0.000000           0.000000           0.035009   \n",
       "ENSRNOG00000000010           0.112967           0.071199           0.175045   \n",
       "\n",
       "                    Control_Average  VitC_Average  \n",
       "Ensembl_ID                                         \n",
       "ENSRNOG00000000001         0.849481      0.985464  \n",
       "ENSRNOG00000000007         0.000000      0.024222  \n",
       "ENSRNOG00000000008         3.879691      7.735700  \n",
       "ENSRNOG00000000009         0.000000      0.011670  \n",
       "ENSRNOG00000000010         0.059199      0.119737  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_Raw_RNA_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_Raw_RNA_data[\"SC_Control_1_RCPM\"] = SC_VitC_Raw_RNA_data[\"SC_Control_01\"]*1000000/total_counts_removing_outliers_RNA[0]\n",
    "SC_VitC_Raw_RNA_data[\"SC_Control_2_RCPM\"] = SC_VitC_Raw_RNA_data[\"SC_Control_02\"]*1000000/total_counts_removing_outliers_RNA[1]\n",
    "SC_VitC_Raw_RNA_data[\"SC_Control_3_RCPM\"] = SC_VitC_Raw_RNA_data[\"SC_Control_03\"]*1000000/total_counts_removing_outliers_RNA[2]\n",
    "SC_VitC_Raw_RNA_data[\"SC_VitC_1_RCPM\"] = SC_VitC_Raw_RNA_data[\"SC_VitC_01\"]*1000000/total_counts_removing_outliers_RNA[3]\n",
    "SC_VitC_Raw_RNA_data[\"SC_VitC_2_RCPM\"] = SC_VitC_Raw_RNA_data[\"SC_VitC_02\"]*1000000/total_counts_removing_outliers_RNA[4]\n",
    "SC_VitC_Raw_RNA_data[\"SC_VitC_3_RCPM\"] = SC_VitC_Raw_RNA_data[\"SC_VitC_03\"]*1000000/total_counts_removing_outliers_RNA[5]\n",
    "SC_VitC_RNA_Corrected_RCPM = SC_VitC_Raw_RNA_data[[\"SC_Control_1_RCPM\", \"SC_Control_2_RCPM\", \"SC_Control_3_RCPM\", \"SC_VitC_1_RCPM\", \"SC_VitC_2_RCPM\", \"SC_VitC_3_RCPM\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SC_Control_1_RCPM</th>\n",
       "      <th>SC_Control_2_RCPM</th>\n",
       "      <th>SC_Control_3_RCPM</th>\n",
       "      <th>SC_VitC_1_RCPM</th>\n",
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       "      <th>SC_VitC_3_RCPM</th>\n",
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       "    <tr>\n",
       "      <th>Ensembl_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000001</th>\n",
       "      <td>1.012661</td>\n",
       "      <td>0.688187</td>\n",
       "      <td>1.068067</td>\n",
       "      <td>1.027625</td>\n",
       "      <td>1.279351</td>\n",
       "      <td>0.911988</td>\n",
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       "      <th>ENSRNOG00000000007</th>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.041105</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.038000</td>\n",
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       "    <tr>\n",
       "      <th>ENSRNOG00000000008</th>\n",
       "      <td>4.212671</td>\n",
       "      <td>4.161895</td>\n",
       "      <td>4.272268</td>\n",
       "      <td>8.344315</td>\n",
       "      <td>8.141327</td>\n",
       "      <td>8.777889</td>\n",
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       "    <tr>\n",
       "      <th>ENSRNOG00000000009</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.038000</td>\n",
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       "      <th>ENSRNOG00000000010</th>\n",
       "      <td>0.081013</td>\n",
       "      <td>0.065542</td>\n",
       "      <td>0.046438</td>\n",
       "      <td>0.123315</td>\n",
       "      <td>0.077536</td>\n",
       "      <td>0.189998</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    SC_Control_1_RCPM  SC_Control_2_RCPM  SC_Control_3_RCPM  \\\n",
       "Ensembl_ID                                                                    \n",
       "ENSRNOG00000000001           1.012661           0.688187           1.068067   \n",
       "ENSRNOG00000000007           0.000000           0.000000           0.000000   \n",
       "ENSRNOG00000000008           4.212671           4.161895           4.272268   \n",
       "ENSRNOG00000000009           0.000000           0.000000           0.000000   \n",
       "ENSRNOG00000000010           0.081013           0.065542           0.046438   \n",
       "\n",
       "                    SC_VitC_1_RCPM  SC_VitC_2_RCPM  SC_VitC_3_RCPM  \n",
       "Ensembl_ID                                                          \n",
       "ENSRNOG00000000001        1.027625        1.279351        0.911988  \n",
       "ENSRNOG00000000007        0.041105        0.000000        0.038000  \n",
       "ENSRNOG00000000008        8.344315        8.141327        8.777889  \n",
       "ENSRNOG00000000009        0.000000        0.000000        0.038000  \n",
       "ENSRNOG00000000010        0.123315        0.077536        0.189998  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_RNA_Corrected_RCPM.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_RNA_Corrected_RCPM.to_csv(Output_Path+\"SC_VitC_RNA_RCPM_table.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "Rat_Genomic_Info_File = pd.read_csv(Input_genomic_info_file, sep = \",\", index_col = 0 )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gene_Name             object\n",
       "Location              object\n",
       "Sense                 object\n",
       "Gene_Biotype          object\n",
       "transcript_id         object\n",
       "Chr                   object\n",
       "Start                  int64\n",
       "Stop                   int64\n",
       "Length                 int64\n",
       "Exon Length            int64\n",
       "Intron Length          int64\n",
       "Gene_ID               object\n",
       "gene_version           int64\n",
       "transcript_version     int64\n",
       "gene_name             object\n",
       "gene_source           object\n",
       "gene_biotype          object\n",
       "transcript_name       object\n",
       "transcript_source     object\n",
       "transcript_biotype    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Rat_Genomic_Info_File.dtypes # It looks like I don't need to specify the types because the lengths and positions went in as int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Length</th>\n",
       "      <th>Exon Length</th>\n",
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       "      <th>Gene_ID</th>\n",
       "      <th>gene_version</th>\n",
       "      <th>transcript_version</th>\n",
       "      <th>gene_name</th>\n",
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       "      <th>gene_biotype</th>\n",
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       "      <th>transcript_source</th>\n",
       "      <th>transcript_biotype</th>\n",
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       "      <th>Ensembl_ID</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th>ENSRNOG00000000001</th>\n",
       "      <td>AABR07013255.1</td>\n",
       "      <td>chr2:230660664-230662084</td>\n",
       "      <td>+</td>\n",
       "      <td>pseudogene</td>\n",
       "      <td>ENSRNOT00000055633</td>\n",
       "      <td>2</td>\n",
       "      <td>230660664</td>\n",
       "      <td>230662084</td>\n",
       "      <td>1421</td>\n",
       "      <td>1416</td>\n",
       "      <td>5</td>\n",
       "      <td>ENSRNOG00000000001</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>AABR07013255.1</td>\n",
       "      <td>ensembl</td>\n",
       "      <td>pseudogene</td>\n",
       "      <td>AABR07013255.1-201</td>\n",
       "      <td>ensembl</td>\n",
       "      <td>pseudogene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000007</th>\n",
       "      <td>Gad1</td>\n",
       "      <td>chr3:56861396-56902157</td>\n",
       "      <td>+</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>ENSRNOT00000087712</td>\n",
       "      <td>3</td>\n",
       "      <td>56862691</td>\n",
       "      <td>56902137</td>\n",
       "      <td>39447</td>\n",
       "      <td>3381</td>\n",
       "      <td>36066</td>\n",
       "      <td>ENSRNOG00000000007</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>Gad1</td>\n",
       "      <td>ensembl</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>Gad1-204</td>\n",
       "      <td>ensembl</td>\n",
       "      <td>protein_coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000008</th>\n",
       "      <td>Alx4</td>\n",
       "      <td>chr3:82548959-82585531</td>\n",
       "      <td>+</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>ENSRNOT00000000009</td>\n",
       "      <td>3</td>\n",
       "      <td>82548959</td>\n",
       "      <td>82585531</td>\n",
       "      <td>36573</td>\n",
       "      <td>1747</td>\n",
       "      <td>34826</td>\n",
       "      <td>ENSRNOG00000000008</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>Alx4</td>\n",
       "      <td>ensembl</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>Alx4-201</td>\n",
       "      <td>ensembl</td>\n",
       "      <td>protein_coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000009</th>\n",
       "      <td>Tmco5b</td>\n",
       "      <td>chr3:104749051-104765436</td>\n",
       "      <td>+</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>ENSRNOT00000000010</td>\n",
       "      <td>3</td>\n",
       "      <td>104749051</td>\n",
       "      <td>104765436</td>\n",
       "      <td>16386</td>\n",
       "      <td>1361</td>\n",
       "      <td>15025</td>\n",
       "      <td>ENSRNOG00000000009</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>Tmco5b</td>\n",
       "      <td>ensembl</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>Tmco5b-201</td>\n",
       "      <td>ensembl</td>\n",
       "      <td>protein_coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000010</th>\n",
       "      <td>Cbln1</td>\n",
       "      <td>chr19:20607507-20611316</td>\n",
       "      <td>+</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>ENSRNOT00000000011</td>\n",
       "      <td>19</td>\n",
       "      <td>20607507</td>\n",
       "      <td>20611316</td>\n",
       "      <td>3810</td>\n",
       "      <td>2444</td>\n",
       "      <td>1366</td>\n",
       "      <td>ENSRNOG00000000010</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>Cbln1</td>\n",
       "      <td>ensembl</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>Cbln1-201</td>\n",
       "      <td>ensembl</td>\n",
       "      <td>protein_coding</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Gene_Name                  Location Sense  \\\n",
       "Ensembl_ID                                                           \n",
       "ENSRNOG00000000001  AABR07013255.1  chr2:230660664-230662084     +   \n",
       "ENSRNOG00000000007            Gad1    chr3:56861396-56902157     +   \n",
       "ENSRNOG00000000008            Alx4    chr3:82548959-82585531     +   \n",
       "ENSRNOG00000000009          Tmco5b  chr3:104749051-104765436     +   \n",
       "ENSRNOG00000000010           Cbln1   chr19:20607507-20611316     +   \n",
       "\n",
       "                      Gene_Biotype       transcript_id Chr      Start  \\\n",
       "Ensembl_ID                                                              \n",
       "ENSRNOG00000000001      pseudogene  ENSRNOT00000055633   2  230660664   \n",
       "ENSRNOG00000000007  protein_coding  ENSRNOT00000087712   3   56862691   \n",
       "ENSRNOG00000000008  protein_coding  ENSRNOT00000000009   3   82548959   \n",
       "ENSRNOG00000000009  protein_coding  ENSRNOT00000000010   3  104749051   \n",
       "ENSRNOG00000000010  protein_coding  ENSRNOT00000000011  19   20607507   \n",
       "\n",
       "                         Stop  Length  Exon Length  Intron Length  \\\n",
       "Ensembl_ID                                                          \n",
       "ENSRNOG00000000001  230662084    1421         1416              5   \n",
       "ENSRNOG00000000007   56902137   39447         3381          36066   \n",
       "ENSRNOG00000000008   82585531   36573         1747          34826   \n",
       "ENSRNOG00000000009  104765436   16386         1361          15025   \n",
       "ENSRNOG00000000010   20611316    3810         2444           1366   \n",
       "\n",
       "                               Gene_ID  gene_version  transcript_version  \\\n",
       "Ensembl_ID                                                                 \n",
       "ENSRNOG00000000001  ENSRNOG00000000001             5                   2   \n",
       "ENSRNOG00000000007  ENSRNOG00000000007             7                   1   \n",
       "ENSRNOG00000000008  ENSRNOG00000000008             7                   5   \n",
       "ENSRNOG00000000009  ENSRNOG00000000009             5                   5   \n",
       "ENSRNOG00000000010  ENSRNOG00000000010             5                   5   \n",
       "\n",
       "                         gene_name gene_source    gene_biotype  \\\n",
       "Ensembl_ID                                                       \n",
       "ENSRNOG00000000001  AABR07013255.1     ensembl      pseudogene   \n",
       "ENSRNOG00000000007            Gad1     ensembl  protein_coding   \n",
       "ENSRNOG00000000008            Alx4     ensembl  protein_coding   \n",
       "ENSRNOG00000000009          Tmco5b     ensembl  protein_coding   \n",
       "ENSRNOG00000000010           Cbln1     ensembl  protein_coding   \n",
       "\n",
       "                       transcript_name transcript_source transcript_biotype  \n",
       "Ensembl_ID                                                                   \n",
       "ENSRNOG00000000001  AABR07013255.1-201           ensembl         pseudogene  \n",
       "ENSRNOG00000000007            Gad1-204           ensembl     protein_coding  \n",
       "ENSRNOG00000000008            Alx4-201           ensembl     protein_coding  \n",
       "ENSRNOG00000000009          Tmco5b-201           ensembl     protein_coding  \n",
       "ENSRNOG00000000010           Cbln1-201           ensembl     protein_coding  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Rat_Genomic_Info_File.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_RNA_Corrected_RCPM = SC_VitC_RNA_Corrected_RCPM.join(Rat_Genomic_Info_File)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SC_Control_1_RCPM</th>\n",
       "      <th>SC_Control_2_RCPM</th>\n",
       "      <th>SC_Control_3_RCPM</th>\n",
       "      <th>SC_VitC_1_RCPM</th>\n",
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       "      <th>Sense</th>\n",
       "      <th>Gene_Biotype</th>\n",
       "      <th>...</th>\n",
       "      <th>Intron Length</th>\n",
       "      <th>Gene_ID</th>\n",
       "      <th>gene_version</th>\n",
       "      <th>transcript_version</th>\n",
       "      <th>gene_name</th>\n",
       "      <th>gene_source</th>\n",
       "      <th>gene_biotype</th>\n",
       "      <th>transcript_name</th>\n",
       "      <th>transcript_source</th>\n",
       "      <th>transcript_biotype</th>\n",
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       "    <tr>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000001</th>\n",
       "      <td>1.012661</td>\n",
       "      <td>0.688187</td>\n",
       "      <td>1.068067</td>\n",
       "      <td>1.027625</td>\n",
       "      <td>1.279351</td>\n",
       "      <td>0.911988</td>\n",
       "      <td>AABR07013255.1</td>\n",
       "      <td>chr2:230660664-230662084</td>\n",
       "      <td>+</td>\n",
       "      <td>pseudogene</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>ENSRNOG00000000001</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>AABR07013255.1</td>\n",
       "      <td>ensembl</td>\n",
       "      <td>pseudogene</td>\n",
       "      <td>AABR07013255.1-201</td>\n",
       "      <td>ensembl</td>\n",
       "      <td>pseudogene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000007</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.041105</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.038000</td>\n",
       "      <td>Gad1</td>\n",
       "      <td>chr3:56861396-56902157</td>\n",
       "      <td>+</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>...</td>\n",
       "      <td>36066</td>\n",
       "      <td>ENSRNOG00000000007</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>Gad1</td>\n",
       "      <td>ensembl</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>Gad1-204</td>\n",
       "      <td>ensembl</td>\n",
       "      <td>protein_coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000008</th>\n",
       "      <td>4.212671</td>\n",
       "      <td>4.161895</td>\n",
       "      <td>4.272268</td>\n",
       "      <td>8.344315</td>\n",
       "      <td>8.141327</td>\n",
       "      <td>8.777889</td>\n",
       "      <td>Alx4</td>\n",
       "      <td>chr3:82548959-82585531</td>\n",
       "      <td>+</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>...</td>\n",
       "      <td>34826</td>\n",
       "      <td>ENSRNOG00000000008</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>Alx4</td>\n",
       "      <td>ensembl</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>Alx4-201</td>\n",
       "      <td>ensembl</td>\n",
       "      <td>protein_coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000009</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.038000</td>\n",
       "      <td>Tmco5b</td>\n",
       "      <td>chr3:104749051-104765436</td>\n",
       "      <td>+</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>...</td>\n",
       "      <td>15025</td>\n",
       "      <td>ENSRNOG00000000009</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>Tmco5b</td>\n",
       "      <td>ensembl</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>Tmco5b-201</td>\n",
       "      <td>ensembl</td>\n",
       "      <td>protein_coding</td>\n",
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       "    <tr>\n",
       "      <th>ENSRNOG00000000010</th>\n",
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       "      <td>0.065542</td>\n",
       "      <td>0.046438</td>\n",
       "      <td>0.123315</td>\n",
       "      <td>0.077536</td>\n",
       "      <td>0.189998</td>\n",
       "      <td>Cbln1</td>\n",
       "      <td>chr19:20607507-20611316</td>\n",
       "      <td>+</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>...</td>\n",
       "      <td>1366</td>\n",
       "      <td>ENSRNOG00000000010</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>Cbln1</td>\n",
       "      <td>ensembl</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>Cbln1-201</td>\n",
       "      <td>ensembl</td>\n",
       "      <td>protein_coding</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    SC_Control_1_RCPM  SC_Control_2_RCPM  SC_Control_3_RCPM  \\\n",
       "Ensembl_ID                                                                    \n",
       "ENSRNOG00000000001           1.012661           0.688187           1.068067   \n",
       "ENSRNOG00000000007           0.000000           0.000000           0.000000   \n",
       "ENSRNOG00000000008           4.212671           4.161895           4.272268   \n",
       "ENSRNOG00000000009           0.000000           0.000000           0.000000   \n",
       "ENSRNOG00000000010           0.081013           0.065542           0.046438   \n",
       "\n",
       "                    SC_VitC_1_RCPM  SC_VitC_2_RCPM  SC_VitC_3_RCPM  \\\n",
       "Ensembl_ID                                                           \n",
       "ENSRNOG00000000001        1.027625        1.279351        0.911988   \n",
       "ENSRNOG00000000007        0.041105        0.000000        0.038000   \n",
       "ENSRNOG00000000008        8.344315        8.141327        8.777889   \n",
       "ENSRNOG00000000009        0.000000        0.000000        0.038000   \n",
       "ENSRNOG00000000010        0.123315        0.077536        0.189998   \n",
       "\n",
       "                         Gene_Name                  Location Sense  \\\n",
       "Ensembl_ID                                                           \n",
       "ENSRNOG00000000001  AABR07013255.1  chr2:230660664-230662084     +   \n",
       "ENSRNOG00000000007            Gad1    chr3:56861396-56902157     +   \n",
       "ENSRNOG00000000008            Alx4    chr3:82548959-82585531     +   \n",
       "ENSRNOG00000000009          Tmco5b  chr3:104749051-104765436     +   \n",
       "ENSRNOG00000000010           Cbln1   chr19:20607507-20611316     +   \n",
       "\n",
       "                      Gene_Biotype         ...         Intron Length  \\\n",
       "Ensembl_ID                                 ...                         \n",
       "ENSRNOG00000000001      pseudogene         ...                     5   \n",
       "ENSRNOG00000000007  protein_coding         ...                 36066   \n",
       "ENSRNOG00000000008  protein_coding         ...                 34826   \n",
       "ENSRNOG00000000009  protein_coding         ...                 15025   \n",
       "ENSRNOG00000000010  protein_coding         ...                  1366   \n",
       "\n",
       "                               Gene_ID  gene_version  transcript_version  \\\n",
       "Ensembl_ID                                                                 \n",
       "ENSRNOG00000000001  ENSRNOG00000000001             5                   2   \n",
       "ENSRNOG00000000007  ENSRNOG00000000007             7                   1   \n",
       "ENSRNOG00000000008  ENSRNOG00000000008             7                   5   \n",
       "ENSRNOG00000000009  ENSRNOG00000000009             5                   5   \n",
       "ENSRNOG00000000010  ENSRNOG00000000010             5                   5   \n",
       "\n",
       "                         gene_name  gene_source    gene_biotype  \\\n",
       "Ensembl_ID                                                        \n",
       "ENSRNOG00000000001  AABR07013255.1      ensembl      pseudogene   \n",
       "ENSRNOG00000000007            Gad1      ensembl  protein_coding   \n",
       "ENSRNOG00000000008            Alx4      ensembl  protein_coding   \n",
       "ENSRNOG00000000009          Tmco5b      ensembl  protein_coding   \n",
       "ENSRNOG00000000010           Cbln1      ensembl  protein_coding   \n",
       "\n",
       "                       transcript_name  transcript_source  transcript_biotype  \n",
       "Ensembl_ID                                                                     \n",
       "ENSRNOG00000000001  AABR07013255.1-201            ensembl          pseudogene  \n",
       "ENSRNOG00000000007            Gad1-204            ensembl      protein_coding  \n",
       "ENSRNOG00000000008            Alx4-201            ensembl      protein_coding  \n",
       "ENSRNOG00000000009          Tmco5b-201            ensembl      protein_coding  \n",
       "ENSRNOG00000000010           Cbln1-201            ensembl      protein_coding  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_RNA_Corrected_RCPM.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_RNA_Corrected_RCPM[\"SC_Control_1_FPKM\"]= SC_VitC_RNA_Corrected_RCPM[\"SC_Control_1_RCPM\"]*1000/SC_VitC_RNA_Corrected_RCPM[\"Exon Length\"]\n",
    "SC_VitC_RNA_Corrected_RCPM[\"SC_Control_2_FPKM\"]= SC_VitC_RNA_Corrected_RCPM[\"SC_Control_2_RCPM\"]*1000/SC_VitC_RNA_Corrected_RCPM[\"Exon Length\"]\n",
    "SC_VitC_RNA_Corrected_RCPM[\"SC_Control_3_FPKM\"]= SC_VitC_RNA_Corrected_RCPM[\"SC_Control_3_RCPM\"]*1000/SC_VitC_RNA_Corrected_RCPM[\"Exon Length\"]\n",
    "\n",
    "SC_VitC_RNA_Corrected_RCPM[\"SC_VitC_1_FPKM\"]= SC_VitC_RNA_Corrected_RCPM[\"SC_VitC_1_RCPM\"]*1000/SC_VitC_RNA_Corrected_RCPM[\"Exon Length\"]\n",
    "SC_VitC_RNA_Corrected_RCPM[\"SC_VitC_2_FPKM\"]= SC_VitC_RNA_Corrected_RCPM[\"SC_VitC_2_RCPM\"]*1000/SC_VitC_RNA_Corrected_RCPM[\"Exon Length\"]\n",
    "SC_VitC_RNA_Corrected_RCPM[\"SC_VitC_3_FPKM\"]= SC_VitC_RNA_Corrected_RCPM[\"SC_VitC_3_RCPM\"]*1000/SC_VitC_RNA_Corrected_RCPM[\"Exon Length\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_RNA_FPKM_table = SC_VitC_RNA_Corrected_RCPM[[\"SC_Control_1_FPKM\", \"SC_Control_2_FPKM\", \"SC_Control_3_FPKM\", \"SC_VitC_1_FPKM\", \"SC_VitC_2_FPKM\", \"SC_VitC_3_FPKM\"]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>SC_Control_1_FPKM</th>\n",
       "      <th>SC_Control_2_FPKM</th>\n",
       "      <th>SC_Control_3_FPKM</th>\n",
       "      <th>SC_VitC_1_FPKM</th>\n",
       "      <th>SC_VitC_2_FPKM</th>\n",
       "      <th>SC_VitC_3_FPKM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ensembl_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000001</th>\n",
       "      <td>0.715156</td>\n",
       "      <td>0.486008</td>\n",
       "      <td>0.754285</td>\n",
       "      <td>0.725724</td>\n",
       "      <td>0.903497</td>\n",
       "      <td>0.644060</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.011239</td>\n",
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       "    <tr>\n",
       "      <th>ENSRNOG00000000008</th>\n",
       "      <td>2.411374</td>\n",
       "      <td>2.382310</td>\n",
       "      <td>2.445488</td>\n",
       "      <td>4.776368</td>\n",
       "      <td>4.660176</td>\n",
       "      <td>5.024550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000009</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.027920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000010</th>\n",
       "      <td>0.033148</td>\n",
       "      <td>0.026817</td>\n",
       "      <td>0.019001</td>\n",
       "      <td>0.050456</td>\n",
       "      <td>0.031725</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    SC_Control_1_FPKM  SC_Control_2_FPKM  SC_Control_3_FPKM  \\\n",
       "Ensembl_ID                                                                    \n",
       "ENSRNOG00000000001           0.715156           0.486008           0.754285   \n",
       "ENSRNOG00000000007           0.000000           0.000000           0.000000   \n",
       "ENSRNOG00000000008           2.411374           2.382310           2.445488   \n",
       "ENSRNOG00000000009           0.000000           0.000000           0.000000   \n",
       "ENSRNOG00000000010           0.033148           0.026817           0.019001   \n",
       "\n",
       "                    SC_VitC_1_FPKM  SC_VitC_2_FPKM  SC_VitC_3_FPKM  \n",
       "Ensembl_ID                                                          \n",
       "ENSRNOG00000000001        0.725724        0.903497        0.644060  \n",
       "ENSRNOG00000000007        0.012158        0.000000        0.011239  \n",
       "ENSRNOG00000000008        4.776368        4.660176        5.024550  \n",
       "ENSRNOG00000000009        0.000000        0.000000        0.027920  \n",
       "ENSRNOG00000000010        0.050456        0.031725        0.077740  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_RNA_FPKM_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "#SC_VitC_RNA_FPKM_table.to_csv(Output_Path+\"SC_VitC_RNA_FPKM_table.csv\")\n",
    "#SC_VitC_RNA_FPKM_table.to_pickle(Output_Path+\"SC_VitC_RNA_FPKM_table.pkl\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I have the FPKM table for the RNA. Next I need to read in the statistical files (edgeR and DESeq2) and calculate fold changes to determine which direction each gene goes. When I did this for the hMeDIP, none of them had values of 0 so I didn't need to worry about dividing by infinity. For this one, I needed to make up a lambda function for an if statement if the values were below 0.1 for both VitC and Control."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/david/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/Users/david/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "/Users/david/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/Users/david/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/Users/david/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: RuntimeWarning: divide by zero encountered in log2\n",
      "  \n",
      "/Users/david/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "SC_VitC_RNA_FPKM_table[\"Control_Average_FPKM\"] = SC_VitC_RNA_FPKM_table[[\"SC_Control_1_FPKM\", \"SC_Control_2_FPKM\", \"SC_Control_2_FPKM\"]].mean(axis=1)\n",
    "SC_VitC_RNA_FPKM_table[\"VitC_Average_FPKM\"] = SC_VitC_RNA_FPKM_table[[\"SC_VitC_1_FPKM\", \"SC_VitC_2_FPKM\", \"SC_VitC_3_FPKM\"]].mean(axis=1)\n",
    "SC_VitC_RNA_FPKM_table[\"Fold_Change\"] =  SC_VitC_RNA_FPKM_table.apply(lambda x: 1 if max(x[\"VitC_Average_FPKM\"],x[\"Control_Average_FPKM\"]) < 0.1 else (x[\"VitC_Average_FPKM\"]/x[\"Control_Average_FPKM\"]), axis = 1)\n",
    "\n",
    "\n",
    "SC_VitC_RNA_FPKM_table[\"Log2FC\"] = np.log2(SC_VitC_RNA_FPKM_table[\"Fold_Change\"])\n",
    "# All 4 of these return warnings, if you have run this before because you are replacing the old values with new ones (which are the same as the old)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>Control_Average_FPKM</th>\n",
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       "</div>"
      ],
      "text/plain": [
       "                    SC_Control_1_FPKM  SC_Control_2_FPKM  SC_Control_3_FPKM  \\\n",
       "Ensembl_ID                                                                    \n",
       "ENSRNOG00000000001           0.715156           0.486008           0.754285   \n",
       "ENSRNOG00000000007           0.000000           0.000000           0.000000   \n",
       "ENSRNOG00000000008           2.411374           2.382310           2.445488   \n",
       "ENSRNOG00000000009           0.000000           0.000000           0.000000   \n",
       "ENSRNOG00000000010           0.033148           0.026817           0.019001   \n",
       "\n",
       "                    SC_VitC_1_FPKM  SC_VitC_2_FPKM  SC_VitC_3_FPKM  \\\n",
       "Ensembl_ID                                                           \n",
       "ENSRNOG00000000001        0.725724        0.903497        0.644060   \n",
       "ENSRNOG00000000007        0.012158        0.000000        0.011239   \n",
       "ENSRNOG00000000008        4.776368        4.660176        5.024550   \n",
       "ENSRNOG00000000009        0.000000        0.000000        0.027920   \n",
       "ENSRNOG00000000010        0.050456        0.031725        0.077740   \n",
       "\n",
       "                    Control_Average_FPKM  VitC_Average_FPKM  Fold_Change  \\\n",
       "Ensembl_ID                                                                 \n",
       "ENSRNOG00000000001              0.562391           0.757760     1.347391   \n",
       "ENSRNOG00000000007              0.000000           0.007799     1.000000   \n",
       "ENSRNOG00000000008              2.391998           4.820365     2.015204   \n",
       "ENSRNOG00000000009              0.000000           0.009307     1.000000   \n",
       "ENSRNOG00000000010              0.028927           0.053307     1.000000   \n",
       "\n",
       "                      Log2FC  \n",
       "Ensembl_ID                    \n",
       "ENSRNOG00000000001  0.430168  \n",
       "ENSRNOG00000000007  0.000000  \n",
       "ENSRNOG00000000008  1.010926  \n",
       "ENSRNOG00000000009  0.000000  \n",
       "ENSRNOG00000000010  0.000000  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_RNA_FPKM_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_RNA_edgeR = pd.read_csv(Input_RNA_edgeR, sep = \",\", index_col=0) \n",
    "SC_VitC_RNA_edgeR = SC_VitC_RNA_edgeR.drop(['logFC', 'logCPM', 'LR', 'PValue'], axis = 1)\n",
    "SC_VitC_RNA_edgeR.columns=[\"edgeR\"]\n",
    "\n",
    "SC_VitC_RNA_DESeq2 = pd.read_csv(Input_RNA_DESeq2, sep = \",\", index_col=0)\n",
    "SC_VitC_RNA_DESeq2 = SC_VitC_RNA_DESeq2.drop(['baseMean', 'log2FoldChange', 'lfcSE', 'stat', 'pvalue'], axis = 1)\n",
    "SC_VitC_RNA_DESeq2.columns = [\"DESeq2\"]\n",
    "SC_VitC_RNA_DESeq2 = SC_VitC_RNA_DESeq2.fillna(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Gene_ID</th>\n",
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      "text/plain": [
       "                            edgeR\n",
       "Gene_ID                          \n",
       "ENSRNOG00000008000  6.970000e-199\n",
       "ENSRNOG00000056714  1.350000e-155\n",
       "ENSRNOG00000007159  8.870000e-153\n",
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       "ENSRNOG00000024082  4.760000e-116"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_RNA_edgeR.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "                           DESeq2\n",
       "ENSRNOG00000008000  9.380000e-261\n",
       "ENSRNOG00000056714  1.560000e-232\n",
       "ENSRNOG00000005580  1.500000e-189\n",
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     "execution_count": 118,
     "metadata": {},
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   "source": [
    "SC_VitC_RNA_DESeq2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_RNA_FPKM_table = SC_VitC_RNA_FPKM_table.join([SC_VitC_RNA_edgeR, SC_VitC_RNA_DESeq2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
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       "      <th>SC_Control_1_FPKM</th>\n",
       "      <th>SC_Control_2_FPKM</th>\n",
       "      <th>SC_Control_3_FPKM</th>\n",
       "      <th>SC_VitC_1_FPKM</th>\n",
       "      <th>SC_VitC_2_FPKM</th>\n",
       "      <th>SC_VitC_3_FPKM</th>\n",
       "      <th>Control_Average_FPKM</th>\n",
       "      <th>VitC_Average_FPKM</th>\n",
       "      <th>Fold_Change</th>\n",
       "      <th>Log2FC</th>\n",
       "      <th>edgeR</th>\n",
       "      <th>DESeq2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ensembl_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000001</th>\n",
       "      <td>0.715156</td>\n",
       "      <td>0.486008</td>\n",
       "      <td>0.754285</td>\n",
       "      <td>0.725724</td>\n",
       "      <td>0.903497</td>\n",
       "      <td>0.644060</td>\n",
       "      <td>0.562391</td>\n",
       "      <td>0.757760</td>\n",
       "      <td>1.347391</td>\n",
       "      <td>0.430168</td>\n",
       "      <td>7.507860e-01</td>\n",
       "      <td>6.209275e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000007</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012158</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011239</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007799</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.898456e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000008</th>\n",
       "      <td>2.411374</td>\n",
       "      <td>2.382310</td>\n",
       "      <td>2.445488</td>\n",
       "      <td>4.776368</td>\n",
       "      <td>4.660176</td>\n",
       "      <td>5.024550</td>\n",
       "      <td>2.391998</td>\n",
       "      <td>4.820365</td>\n",
       "      <td>2.015204</td>\n",
       "      <td>1.010926</td>\n",
       "      <td>3.470000e-12</td>\n",
       "      <td>1.440000e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000009</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.027920</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009307</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.624017e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000010</th>\n",
       "      <td>0.033148</td>\n",
       "      <td>0.026817</td>\n",
       "      <td>0.019001</td>\n",
       "      <td>0.050456</td>\n",
       "      <td>0.031725</td>\n",
       "      <td>0.077740</td>\n",
       "      <td>0.028927</td>\n",
       "      <td>0.053307</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.854191e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000012</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.039042</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013014</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.624017e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000017</th>\n",
       "      <td>0.664559</td>\n",
       "      <td>0.844873</td>\n",
       "      <td>0.906986</td>\n",
       "      <td>0.706492</td>\n",
       "      <td>1.635534</td>\n",
       "      <td>1.157798</td>\n",
       "      <td>0.784768</td>\n",
       "      <td>1.166608</td>\n",
       "      <td>1.486564</td>\n",
       "      <td>0.571982</td>\n",
       "      <td>2.525549e-01</td>\n",
       "      <td>2.469673e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000021</th>\n",
       "      <td>12.793288</td>\n",
       "      <td>13.681820</td>\n",
       "      <td>14.473083</td>\n",
       "      <td>11.817687</td>\n",
       "      <td>14.764231</td>\n",
       "      <td>14.123155</td>\n",
       "      <td>13.385642</td>\n",
       "      <td>13.568358</td>\n",
       "      <td>1.013650</td>\n",
       "      <td>0.019560</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>7.674736e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000024</th>\n",
       "      <td>15.943218</td>\n",
       "      <td>15.212822</td>\n",
       "      <td>15.348053</td>\n",
       "      <td>13.004972</td>\n",
       "      <td>14.711482</td>\n",
       "      <td>14.026188</td>\n",
       "      <td>15.456287</td>\n",
       "      <td>13.914214</td>\n",
       "      <td>0.900230</td>\n",
       "      <td>-0.151634</td>\n",
       "      <td>9.658437e-01</td>\n",
       "      <td>6.204969e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000033</th>\n",
       "      <td>5.590132</td>\n",
       "      <td>5.421742</td>\n",
       "      <td>5.336362</td>\n",
       "      <td>7.537592</td>\n",
       "      <td>7.267067</td>\n",
       "      <td>7.587516</td>\n",
       "      <td>5.477872</td>\n",
       "      <td>7.464058</td>\n",
       "      <td>1.362583</td>\n",
       "      <td>0.446345</td>\n",
       "      <td>5.880000e-06</td>\n",
       "      <td>1.600000e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000034</th>\n",
       "      <td>0.140892</td>\n",
       "      <td>0.136783</td>\n",
       "      <td>0.161522</td>\n",
       "      <td>0.400327</td>\n",
       "      <td>0.498930</td>\n",
       "      <td>0.317213</td>\n",
       "      <td>0.138152</td>\n",
       "      <td>0.405490</td>\n",
       "      <td>2.935093</td>\n",
       "      <td>1.553406</td>\n",
       "      <td>1.094600e-04</td>\n",
       "      <td>3.476118e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000035</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000036</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.025699</td>\n",
       "      <td>0.048475</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.024725</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.499697e-02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000040</th>\n",
       "      <td>5.334183</td>\n",
       "      <td>5.711691</td>\n",
       "      <td>4.912436</td>\n",
       "      <td>8.816032</td>\n",
       "      <td>9.347168</td>\n",
       "      <td>8.600714</td>\n",
       "      <td>5.585855</td>\n",
       "      <td>8.921305</td>\n",
       "      <td>1.597124</td>\n",
       "      <td>0.675477</td>\n",
       "      <td>2.160000e-13</td>\n",
       "      <td>4.650000e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000041</th>\n",
       "      <td>0.021830</td>\n",
       "      <td>0.114799</td>\n",
       "      <td>0.025027</td>\n",
       "      <td>0.011077</td>\n",
       "      <td>0.020894</td>\n",
       "      <td>0.051198</td>\n",
       "      <td>0.083810</td>\n",
       "      <td>0.027723</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.624017e-01</td>\n",
       "      <td>5.785241e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    SC_Control_1_FPKM  SC_Control_2_FPKM  SC_Control_3_FPKM  \\\n",
       "Ensembl_ID                                                                    \n",
       "ENSRNOG00000000001           0.715156           0.486008           0.754285   \n",
       "ENSRNOG00000000007           0.000000           0.000000           0.000000   \n",
       "ENSRNOG00000000008           2.411374           2.382310           2.445488   \n",
       "ENSRNOG00000000009           0.000000           0.000000           0.000000   \n",
       "ENSRNOG00000000010           0.033148           0.026817           0.019001   \n",
       "ENSRNOG00000000012           0.000000           0.000000           0.000000   \n",
       "ENSRNOG00000000017           0.664559           0.844873           0.906986   \n",
       "ENSRNOG00000000021          12.793288          13.681820          14.473083   \n",
       "ENSRNOG00000000024          15.943218          15.212822          15.348053   \n",
       "ENSRNOG00000000033           5.590132           5.421742           5.336362   \n",
       "ENSRNOG00000000034           0.140892           0.136783           0.161522   \n",
       "ENSRNOG00000000035           0.000000           0.000000           0.000000   \n",
       "ENSRNOG00000000036           0.000000           0.000000           0.000000   \n",
       "ENSRNOG00000000040           5.334183           5.711691           4.912436   \n",
       "ENSRNOG00000000041           0.021830           0.114799           0.025027   \n",
       "\n",
       "                    SC_VitC_1_FPKM  SC_VitC_2_FPKM  SC_VitC_3_FPKM  \\\n",
       "Ensembl_ID                                                           \n",
       "ENSRNOG00000000001        0.725724        0.903497        0.644060   \n",
       "ENSRNOG00000000007        0.012158        0.000000        0.011239   \n",
       "ENSRNOG00000000008        4.776368        4.660176        5.024550   \n",
       "ENSRNOG00000000009        0.000000        0.000000        0.027920   \n",
       "ENSRNOG00000000010        0.050456        0.031725        0.077740   \n",
       "ENSRNOG00000000012        0.000000        0.039042        0.000000   \n",
       "ENSRNOG00000000017        0.706492        1.635534        1.157798   \n",
       "ENSRNOG00000000021       11.817687       14.764231       14.123155   \n",
       "ENSRNOG00000000024       13.004972       14.711482       14.026188   \n",
       "ENSRNOG00000000033        7.537592        7.267067        7.587516   \n",
       "ENSRNOG00000000034        0.400327        0.498930        0.317213   \n",
       "ENSRNOG00000000035        0.000000        0.000000        0.000000   \n",
       "ENSRNOG00000000036        0.025699        0.048475        0.000000   \n",
       "ENSRNOG00000000040        8.816032        9.347168        8.600714   \n",
       "ENSRNOG00000000041        0.011077        0.020894        0.051198   \n",
       "\n",
       "                    Control_Average_FPKM  VitC_Average_FPKM  Fold_Change  \\\n",
       "Ensembl_ID                                                                 \n",
       "ENSRNOG00000000001              0.562391           0.757760     1.347391   \n",
       "ENSRNOG00000000007              0.000000           0.007799     1.000000   \n",
       "ENSRNOG00000000008              2.391998           4.820365     2.015204   \n",
       "ENSRNOG00000000009              0.000000           0.009307     1.000000   \n",
       "ENSRNOG00000000010              0.028927           0.053307     1.000000   \n",
       "ENSRNOG00000000012              0.000000           0.013014     1.000000   \n",
       "ENSRNOG00000000017              0.784768           1.166608     1.486564   \n",
       "ENSRNOG00000000021             13.385642          13.568358     1.013650   \n",
       "ENSRNOG00000000024             15.456287          13.914214     0.900230   \n",
       "ENSRNOG00000000033              5.477872           7.464058     1.362583   \n",
       "ENSRNOG00000000034              0.138152           0.405490     2.935093   \n",
       "ENSRNOG00000000035              0.000000           0.000000     1.000000   \n",
       "ENSRNOG00000000036              0.000000           0.024725     1.000000   \n",
       "ENSRNOG00000000040              5.585855           8.921305     1.597124   \n",
       "ENSRNOG00000000041              0.083810           0.027723     1.000000   \n",
       "\n",
       "                      Log2FC         edgeR        DESeq2  \n",
       "Ensembl_ID                                                \n",
       "ENSRNOG00000000001  0.430168  7.507860e-01  6.209275e-01  \n",
       "ENSRNOG00000000007  0.000000  3.898456e-01  1.000000e+00  \n",
       "ENSRNOG00000000008  1.010926  3.470000e-12  1.440000e-09  \n",
       "ENSRNOG00000000009  0.000000  6.624017e-01  1.000000e+00  \n",
       "ENSRNOG00000000010  0.000000  5.854191e-01  1.000000e+00  \n",
       "ENSRNOG00000000012  0.000000  6.624017e-01  1.000000e+00  \n",
       "ENSRNOG00000000017  0.571982  2.525549e-01  2.469673e-01  \n",
       "ENSRNOG00000000021  0.019560  1.000000e+00  7.674736e-01  \n",
       "ENSRNOG00000000024 -0.151634  9.658437e-01  6.204969e-01  \n",
       "ENSRNOG00000000033  0.446345  5.880000e-06  1.600000e-06  \n",
       "ENSRNOG00000000034  1.553406  1.094600e-04  3.476118e-03  \n",
       "ENSRNOG00000000035  0.000000  1.000000e+00  1.000000e+00  \n",
       "ENSRNOG00000000036  0.000000  6.499697e-02  1.000000e+00  \n",
       "ENSRNOG00000000040  0.675477  2.160000e-13  4.650000e-14  \n",
       "ENSRNOG00000000041  0.000000  6.624017e-01  5.785241e-01  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_RNA_FPKM_table.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SC_Control_1_FPKM        4.072902e+01\n",
       "SC_Control_2_FPKM        4.275939e+01\n",
       "SC_Control_3_FPKM        4.509479e+01\n",
       "SC_VitC_1_FPKM           4.100990e+00\n",
       "SC_VitC_2_FPKM           3.722108e+00\n",
       "SC_VitC_3_FPKM           4.241705e+00\n",
       "Control_Average_FPKM     4.208260e+01\n",
       "VitC_Average_FPKM        4.021601e+00\n",
       "Fold_Change              9.556447e-02\n",
       "Log2FC                  -3.387382e+00\n",
       "edgeR                   6.970000e-199\n",
       "DESeq2                  9.380000e-261\n",
       "Name: ENSRNOG00000008000, dtype: float64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_RNA_FPKM_table.loc[\"ENSRNOG00000008000\"] # This went in correctly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This definition will mistakenly put \"downregulated\" for ones with good P-values by both but a fold change of 1\n",
    "# These ones will be filtered out in the next step, finding limit of detection\n",
    "def direction(row):\n",
    "    if row[\"edgeR\"] < 0.05 and row[\"DESeq2\"] < 0.05:\n",
    "        if row[\"Log2FC\"] > 0:\n",
    "            return \"Upregulated\"\n",
    "        else:\n",
    "            return \"Downregulated\"\n",
    "    else:\n",
    "        return \"Nondifferential\"\n",
    "\n",
    "SC_VitC_RNA_FPKM_table[\"Change\"] =  SC_VitC_RNA_FPKM_table.apply(direction, axis = 1)\n",
    "# I figured out how to make it faster :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SC_Control_1_FPKM</th>\n",
       "      <th>SC_Control_2_FPKM</th>\n",
       "      <th>SC_Control_3_FPKM</th>\n",
       "      <th>SC_VitC_1_FPKM</th>\n",
       "      <th>SC_VitC_2_FPKM</th>\n",
       "      <th>SC_VitC_3_FPKM</th>\n",
       "      <th>Control_Average_FPKM</th>\n",
       "      <th>VitC_Average_FPKM</th>\n",
       "      <th>Fold_Change</th>\n",
       "      <th>Log2FC</th>\n",
       "      <th>edgeR</th>\n",
       "      <th>DESeq2</th>\n",
       "      <th>Change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ensembl_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000001</th>\n",
       "      <td>0.715156</td>\n",
       "      <td>0.486008</td>\n",
       "      <td>0.754285</td>\n",
       "      <td>0.725724</td>\n",
       "      <td>0.903497</td>\n",
       "      <td>0.644060</td>\n",
       "      <td>0.562391</td>\n",
       "      <td>0.757760</td>\n",
       "      <td>1.347391</td>\n",
       "      <td>0.430168</td>\n",
       "      <td>7.507860e-01</td>\n",
       "      <td>6.209275e-01</td>\n",
       "      <td>Nondifferential</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000007</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012158</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011239</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007799</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.898456e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>Nondifferential</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000008</th>\n",
       "      <td>2.411374</td>\n",
       "      <td>2.382310</td>\n",
       "      <td>2.445488</td>\n",
       "      <td>4.776368</td>\n",
       "      <td>4.660176</td>\n",
       "      <td>5.024550</td>\n",
       "      <td>2.391998</td>\n",
       "      <td>4.820365</td>\n",
       "      <td>2.015204</td>\n",
       "      <td>1.010926</td>\n",
       "      <td>3.470000e-12</td>\n",
       "      <td>1.440000e-09</td>\n",
       "      <td>Upregulated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000009</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.027920</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009307</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.624017e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>Nondifferential</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000010</th>\n",
       "      <td>0.033148</td>\n",
       "      <td>0.026817</td>\n",
       "      <td>0.019001</td>\n",
       "      <td>0.050456</td>\n",
       "      <td>0.031725</td>\n",
       "      <td>0.077740</td>\n",
       "      <td>0.028927</td>\n",
       "      <td>0.053307</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.854191e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>Nondifferential</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    SC_Control_1_FPKM  SC_Control_2_FPKM  SC_Control_3_FPKM  \\\n",
       "Ensembl_ID                                                                    \n",
       "ENSRNOG00000000001           0.715156           0.486008           0.754285   \n",
       "ENSRNOG00000000007           0.000000           0.000000           0.000000   \n",
       "ENSRNOG00000000008           2.411374           2.382310           2.445488   \n",
       "ENSRNOG00000000009           0.000000           0.000000           0.000000   \n",
       "ENSRNOG00000000010           0.033148           0.026817           0.019001   \n",
       "\n",
       "                    SC_VitC_1_FPKM  SC_VitC_2_FPKM  SC_VitC_3_FPKM  \\\n",
       "Ensembl_ID                                                           \n",
       "ENSRNOG00000000001        0.725724        0.903497        0.644060   \n",
       "ENSRNOG00000000007        0.012158        0.000000        0.011239   \n",
       "ENSRNOG00000000008        4.776368        4.660176        5.024550   \n",
       "ENSRNOG00000000009        0.000000        0.000000        0.027920   \n",
       "ENSRNOG00000000010        0.050456        0.031725        0.077740   \n",
       "\n",
       "                    Control_Average_FPKM  VitC_Average_FPKM  Fold_Change  \\\n",
       "Ensembl_ID                                                                 \n",
       "ENSRNOG00000000001              0.562391           0.757760     1.347391   \n",
       "ENSRNOG00000000007              0.000000           0.007799     1.000000   \n",
       "ENSRNOG00000000008              2.391998           4.820365     2.015204   \n",
       "ENSRNOG00000000009              0.000000           0.009307     1.000000   \n",
       "ENSRNOG00000000010              0.028927           0.053307     1.000000   \n",
       "\n",
       "                      Log2FC         edgeR        DESeq2           Change  \n",
       "Ensembl_ID                                                                 \n",
       "ENSRNOG00000000001  0.430168  7.507860e-01  6.209275e-01  Nondifferential  \n",
       "ENSRNOG00000000007  0.000000  3.898456e-01  1.000000e+00  Nondifferential  \n",
       "ENSRNOG00000000008  1.010926  3.470000e-12  1.440000e-09      Upregulated  \n",
       "ENSRNOG00000000009  0.000000  6.624017e-01  1.000000e+00  Nondifferential  \n",
       "ENSRNOG00000000010  0.000000  5.854191e-01  1.000000e+00  Nondifferential  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_RNA_FPKM_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_RNA_FPKM_table.to_csv(Output_Path+\"SC_VitC_RNA_FPKM_table.csv\")\n",
    "SC_VitC_RNA_FPKM_table.to_pickle(Output_Path+\"SC_VitC_RNA_FPKM_table.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I have the FPKM table calculated out and I have determined which ones go up and down, but I have not filtered out genes that are really lowly expressed. The next step is to take those out because they will not add information to our analysis, but just add noise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I am going to calculate the FPKM values for the genomic input samples. I am going to see if I can use these to determine a cutoff LOD for the RNA. Luckily, I don't need to worry about outliers for this one because it is genomic input and I want to include those ones if there is something funky because it might transfer across the other sequencing files too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_genomic_input_raw = pd.read_csv(Input_Genomic_Input_Counts_File, sep = \"\\t\", index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SC_Control_1_IN</th>\n",
       "      <th>SC_Control_2_IN</th>\n",
       "      <th>SC_Control_3_IN</th>\n",
       "      <th>SC_VitC_1_IN</th>\n",
       "      <th>SC_VitC_2_IN</th>\n",
       "      <th>SC_VitC_3_IN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peak_Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merged_peak_92032</th>\n",
       "      <td>29</td>\n",
       "      <td>32</td>\n",
       "      <td>29</td>\n",
       "      <td>25</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merged_peak_92041</th>\n",
       "      <td>17</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100020</th>\n",
       "      <td>24</td>\n",
       "      <td>26</td>\n",
       "      <td>29</td>\n",
       "      <td>21</td>\n",
       "      <td>33</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100028</th>\n",
       "      <td>21</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_10003</th>\n",
       "      <td>28</td>\n",
       "      <td>17</td>\n",
       "      <td>27</td>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    SC_Control_1_IN  \\\n",
       "Peak_Name                                                             \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...               29   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...               17   \n",
       "SC_Control_IP_Merged_peak_100020                                 24   \n",
       "SC_Control_IP_Merged_peak_100028                                 21   \n",
       "SC_Control_IP_Merged_peak_10003                                  28   \n",
       "\n",
       "                                                    SC_Control_2_IN  \\\n",
       "Peak_Name                                                             \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...               32   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...               23   \n",
       "SC_Control_IP_Merged_peak_100020                                 26   \n",
       "SC_Control_IP_Merged_peak_100028                                 12   \n",
       "SC_Control_IP_Merged_peak_10003                                  17   \n",
       "\n",
       "                                                    SC_Control_3_IN  \\\n",
       "Peak_Name                                                             \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...               29   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...               30   \n",
       "SC_Control_IP_Merged_peak_100020                                 29   \n",
       "SC_Control_IP_Merged_peak_100028                                 20   \n",
       "SC_Control_IP_Merged_peak_10003                                  27   \n",
       "\n",
       "                                                    SC_VitC_1_IN  \\\n",
       "Peak_Name                                                          \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...            25   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...            18   \n",
       "SC_Control_IP_Merged_peak_100020                              21   \n",
       "SC_Control_IP_Merged_peak_100028                              15   \n",
       "SC_Control_IP_Merged_peak_10003                               25   \n",
       "\n",
       "                                                    SC_VitC_2_IN  SC_VitC_3_IN  \n",
       "Peak_Name                                                                       \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...            35            35  \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...            21            28  \n",
       "SC_Control_IP_Merged_peak_100020                              33            24  \n",
       "SC_Control_IP_Merged_peak_100028                              17            15  \n",
       "SC_Control_IP_Merged_peak_10003                               30            25  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_genomic_input_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_counts_SC_VitC_genomic_input = SC_VitC_genomic_input_raw.sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SC_Control_1_IN    100915341\n",
       "SC_Control_2_IN     83233328\n",
       "SC_Control_3_IN    111336681\n",
       "SC_VitC_1_IN        86173506\n",
       "SC_VitC_2_IN       103924317\n",
       "SC_VitC_3_IN       102885634\n",
       "dtype: int64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_counts_SC_VitC_genomic_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_genomic_input_raw[\"SC_Control_1_IN_RCPM\"] = SC_VitC_genomic_input_raw[\"SC_Control_1_IN\"]*1000000/total_counts_SC_VitC_genomic_input[0]\n",
    "SC_VitC_genomic_input_raw[\"SC_Control_2_IN_RCPM\"] = SC_VitC_genomic_input_raw[\"SC_Control_2_IN\"]*1000000/total_counts_SC_VitC_genomic_input[1]\n",
    "SC_VitC_genomic_input_raw[\"SC_Control_3_IN_RCPM\"] = SC_VitC_genomic_input_raw[\"SC_Control_3_IN\"]*1000000/total_counts_SC_VitC_genomic_input[2]\n",
    "\n",
    "\n",
    "SC_VitC_genomic_input_raw[\"SC_VitC_1_IN_RCPM\"] = SC_VitC_genomic_input_raw[\"SC_VitC_1_IN\"]*1000000/total_counts_SC_VitC_genomic_input[3]\n",
    "SC_VitC_genomic_input_raw[\"SC_VitC_2_IN_RCPM\"] = SC_VitC_genomic_input_raw[\"SC_VitC_2_IN\"]*1000000/total_counts_SC_VitC_genomic_input[4]\n",
    "SC_VitC_genomic_input_raw[\"SC_VitC_3_IN_RCPM\"] = SC_VitC_genomic_input_raw[\"SC_VitC_3_IN\"]*1000000/total_counts_SC_VitC_genomic_input[5]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_genomic_input_rcpm = SC_VitC_genomic_input_raw[[\"SC_Control_1_IN_RCPM\", \"SC_Control_2_IN_RCPM\", \"SC_Control_3_IN_RCPM\", \"SC_VitC_1_IN_RCPM\", \"SC_VitC_2_IN_RCPM\", \"SC_VitC_3_IN_RCPM\"]].drop(columns_added_by_htseq, axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>SC_Control_2_IN_RCPM</th>\n",
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       "      <th>Peak_Name</th>\n",
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       "      <td>0.449088</td>\n",
       "      <td>0.185672</td>\n",
       "      <td>0.346406</td>\n",
       "      <td>0.281866</td>\n",
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       "      <td>0.228274</td>\n",
       "      <td>0.233526</td>\n",
       "      <td>0.243694</td>\n",
       "      <td>0.153958</td>\n",
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       "                                                    SC_Control_1_IN_RCPM  \\\n",
       "Peak_Name                                                                  \n",
       "SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merge...              0.822472   \n",
       "SC_LAA_IP_Merged_peak_99990                                     0.297279   \n",
       "SC_LAA_IP_Merged_peak_99994                                     0.208095   \n",
       "SC_LAA_IP_Merged_peak_99995                                     0.267551   \n",
       "SC_LAA_IP_Merged_peak_99999                                     0.188277   \n",
       "\n",
       "                                                    SC_Control_2_IN_RCPM  \\\n",
       "Peak_Name                                                                  \n",
       "SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merge...              0.901081   \n",
       "SC_LAA_IP_Merged_peak_99990                                     0.276332   \n",
       "SC_LAA_IP_Merged_peak_99994                                     0.192231   \n",
       "SC_LAA_IP_Merged_peak_99995                                     0.384461   \n",
       "SC_LAA_IP_Merged_peak_99999                                     0.228274   \n",
       "\n",
       "                                                    SC_Control_3_IN_RCPM  \\\n",
       "Peak_Name                                                                  \n",
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       "SC_LAA_IP_Merged_peak_99990                                     0.278435   \n",
       "SC_LAA_IP_Merged_peak_99994                                     0.287417   \n",
       "SC_LAA_IP_Merged_peak_99995                                     0.449088   \n",
       "SC_LAA_IP_Merged_peak_99999                                     0.233526   \n",
       "\n",
       "                                                    SC_VitC_1_IN_RCPM  \\\n",
       "Peak_Name                                                               \n",
       "SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merge...           0.997987   \n",
       "SC_LAA_IP_Merged_peak_99990                                  0.197276   \n",
       "SC_LAA_IP_Merged_peak_99994                                  0.220485   \n",
       "SC_LAA_IP_Merged_peak_99995                                  0.185672   \n",
       "SC_LAA_IP_Merged_peak_99999                                  0.243694   \n",
       "\n",
       "                                                    SC_VitC_2_IN_RCPM  \\\n",
       "Peak_Name                                                               \n",
       "SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merge...           0.740924   \n",
       "SC_LAA_IP_Merged_peak_99990                                  0.240560   \n",
       "SC_LAA_IP_Merged_peak_99994                                  0.211693   \n",
       "SC_LAA_IP_Merged_peak_99995                                  0.346406   \n",
       "SC_LAA_IP_Merged_peak_99999                                  0.153958   \n",
       "\n",
       "                                                    SC_VitC_3_IN_RCPM  \n",
       "Peak_Name                                                              \n",
       "SC_LAA_IP_Merged_peak_99980,SC_Control_IP_Merge...           0.923355  \n",
       "SC_LAA_IP_Merged_peak_99990                                  0.184671  \n",
       "SC_LAA_IP_Merged_peak_99994                                  0.242988  \n",
       "SC_LAA_IP_Merged_peak_99995                                  0.281866  \n",
       "SC_LAA_IP_Merged_peak_99999                                  0.291586  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_genomic_input_rcpm.tail()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_genomic_input_rcpm.to_csv(Output_Path+\"SC_VitC_Genomic_Input_RCPM_table.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_hMeDIP_bed = pd.read_csv(Input_bed_file, sep = \"\\t\", index_col = 3) \n",
    "SC_VitC_hMeDIP_bed[\"Width\"] = (SC_VitC_hMeDIP_bed[\"Stop\"] - SC_VitC_hMeDIP_bed[\"Start\"] + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Chr</th>\n",
       "      <th>Start</th>\n",
       "      <th>Stop</th>\n",
       "      <th>Width</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peak_Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_3</th>\n",
       "      <td>1</td>\n",
       "      <td>50022</td>\n",
       "      <td>50495</td>\n",
       "      <td>474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_4</th>\n",
       "      <td>1</td>\n",
       "      <td>110730</td>\n",
       "      <td>111377</td>\n",
       "      <td>648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_17</th>\n",
       "      <td>1</td>\n",
       "      <td>562156</td>\n",
       "      <td>562456</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_23</th>\n",
       "      <td>1</td>\n",
       "      <td>759781</td>\n",
       "      <td>760211</td>\n",
       "      <td>431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_33</th>\n",
       "      <td>1</td>\n",
       "      <td>1227704</td>\n",
       "      <td>1228255</td>\n",
       "      <td>552</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Chr    Start     Stop  Width\n",
       "Peak_Name                                                \n",
       "SC_Control_IP_Merged_peak_3    1    50022    50495    474\n",
       "SC_Control_IP_Merged_peak_4    1   110730   111377    648\n",
       "SC_Control_IP_Merged_peak_17   1   562156   562456    301\n",
       "SC_Control_IP_Merged_peak_23   1   759781   760211    431\n",
       "SC_Control_IP_Merged_peak_33   1  1227704  1228255    552"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_hMeDIP_bed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_genomic_input_rcpm = SC_VitC_genomic_input_rcpm.join(SC_VitC_hMeDIP_bed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_genomic_input_rcpm[\"SC_Control_1_IN_FPKM\"]= SC_VitC_genomic_input_rcpm[\"SC_Control_1_IN_RCPM\"]*1000/SC_VitC_genomic_input_rcpm[\"Width\"]\n",
    "SC_VitC_genomic_input_rcpm[\"SC_Control_2_IN_FPKM\"]= SC_VitC_genomic_input_rcpm[\"SC_Control_2_IN_RCPM\"]*1000/SC_VitC_genomic_input_rcpm[\"Width\"]\n",
    "SC_VitC_genomic_input_rcpm[\"SC_Control_3_IN_FPKM\"]= SC_VitC_genomic_input_rcpm[\"SC_Control_3_IN_RCPM\"]*1000/SC_VitC_genomic_input_rcpm[\"Width\"]\n",
    "\n",
    "SC_VitC_genomic_input_rcpm[\"SC_VitC_1_IN_FPKM\"]= SC_VitC_genomic_input_rcpm[\"SC_VitC_1_IN_RCPM\"]*1000/SC_VitC_genomic_input_rcpm[\"Width\"]\n",
    "SC_VitC_genomic_input_rcpm[\"SC_VitC_2_IN_FPKM\"]= SC_VitC_genomic_input_rcpm[\"SC_VitC_2_IN_RCPM\"]*1000/SC_VitC_genomic_input_rcpm[\"Width\"]\n",
    "SC_VitC_genomic_input_rcpm[\"SC_VitC_3_IN_FPKM\"]= SC_VitC_genomic_input_rcpm[\"SC_VitC_3_IN_RCPM\"]*1000/SC_VitC_genomic_input_rcpm[\"Width\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <th>SC_Control_1_IN_RCPM</th>\n",
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       "      <th>Stop</th>\n",
       "      <th>Width</th>\n",
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       "    <tr>\n",
       "      <th>Peak_Name</th>\n",
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       "      <th>SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merged_peak_92032</th>\n",
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       "      <td>0.290112</td>\n",
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       "      <td>0.366767</td>\n",
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       "      <td>0.269453</td>\n",
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       "      <td>0.202070</td>\n",
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       "      <td>0.563942</td>\n",
       "      <td>0.549904</td>\n",
       "      <td>0.426288</td>\n",
       "      <td>0.412388</td>\n",
       "      <td>0.555402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100020</th>\n",
       "      <td>0.237823</td>\n",
       "      <td>0.312375</td>\n",
       "      <td>0.260471</td>\n",
       "      <td>0.243694</td>\n",
       "      <td>0.317539</td>\n",
       "      <td>0.233269</td>\n",
       "      <td>11</td>\n",
       "      <td>58509719</td>\n",
       "      <td>58510097</td>\n",
       "      <td>379</td>\n",
       "      <td>0.627502</td>\n",
       "      <td>0.824208</td>\n",
       "      <td>0.687259</td>\n",
       "      <td>0.642993</td>\n",
       "      <td>0.837833</td>\n",
       "      <td>0.615485</td>\n",
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       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_100028</th>\n",
       "      <td>0.208095</td>\n",
       "      <td>0.144173</td>\n",
       "      <td>0.179635</td>\n",
       "      <td>0.174067</td>\n",
       "      <td>0.163581</td>\n",
       "      <td>0.145793</td>\n",
       "      <td>11</td>\n",
       "      <td>58518547</td>\n",
       "      <td>58518878</td>\n",
       "      <td>332</td>\n",
       "      <td>0.626793</td>\n",
       "      <td>0.434256</td>\n",
       "      <td>0.541070</td>\n",
       "      <td>0.524299</td>\n",
       "      <td>0.492713</td>\n",
       "      <td>0.439135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_Control_IP_Merged_peak_10003</th>\n",
       "      <td>0.277460</td>\n",
       "      <td>0.204245</td>\n",
       "      <td>0.242508</td>\n",
       "      <td>0.290112</td>\n",
       "      <td>0.288672</td>\n",
       "      <td>0.242988</td>\n",
       "      <td>1</td>\n",
       "      <td>48456350</td>\n",
       "      <td>48456761</td>\n",
       "      <td>412</td>\n",
       "      <td>0.673447</td>\n",
       "      <td>0.495741</td>\n",
       "      <td>0.588611</td>\n",
       "      <td>0.704156</td>\n",
       "      <td>0.700659</td>\n",
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      "text/plain": [
       "                                                    SC_Control_1_IN_RCPM  \\\n",
       "Peak_Name                                                                  \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...              0.287370   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...              0.168458   \n",
       "SC_Control_IP_Merged_peak_100020                                0.237823   \n",
       "SC_Control_IP_Merged_peak_100028                                0.208095   \n",
       "SC_Control_IP_Merged_peak_10003                                 0.277460   \n",
       "\n",
       "                                                    SC_Control_2_IN_RCPM  \\\n",
       "Peak_Name                                                                  \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...              0.384461   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...              0.276332   \n",
       "SC_Control_IP_Merged_peak_100020                                0.312375   \n",
       "SC_Control_IP_Merged_peak_100028                                0.144173   \n",
       "SC_Control_IP_Merged_peak_10003                                 0.204245   \n",
       "\n",
       "                                                    SC_Control_3_IN_RCPM  \\\n",
       "Peak_Name                                                                  \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...              0.260471   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...              0.269453   \n",
       "SC_Control_IP_Merged_peak_100020                                0.260471   \n",
       "SC_Control_IP_Merged_peak_100028                                0.179635   \n",
       "SC_Control_IP_Merged_peak_10003                                 0.242508   \n",
       "\n",
       "                                                    SC_VitC_1_IN_RCPM  \\\n",
       "Peak_Name                                                               \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...           0.290112   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...           0.208881   \n",
       "SC_Control_IP_Merged_peak_100020                             0.243694   \n",
       "SC_Control_IP_Merged_peak_100028                             0.174067   \n",
       "SC_Control_IP_Merged_peak_10003                              0.290112   \n",
       "\n",
       "                                                    SC_VitC_2_IN_RCPM  \\\n",
       "Peak_Name                                                               \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...           0.336784   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...           0.202070   \n",
       "SC_Control_IP_Merged_peak_100020                             0.317539   \n",
       "SC_Control_IP_Merged_peak_100028                             0.163581   \n",
       "SC_Control_IP_Merged_peak_10003                              0.288672   \n",
       "\n",
       "                                                    SC_VitC_3_IN_RCPM Chr  \\\n",
       "Peak_Name                                                                   \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...           0.340184  11   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...           0.272147  11   \n",
       "SC_Control_IP_Merged_peak_100020                             0.233269  11   \n",
       "SC_Control_IP_Merged_peak_100028                             0.145793  11   \n",
       "SC_Control_IP_Merged_peak_10003                              0.242988   1   \n",
       "\n",
       "                                                       Start      Stop  Width  \\\n",
       "Peak_Name                                                                       \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...  58427350  58428140    791   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...  58467708  58468197    490   \n",
       "SC_Control_IP_Merged_peak_100020                    58509719  58510097    379   \n",
       "SC_Control_IP_Merged_peak_100028                    58518547  58518878    332   \n",
       "SC_Control_IP_Merged_peak_10003                     48456350  48456761    412   \n",
       "\n",
       "                                                    SC_Control_1_IN_FPKM  \\\n",
       "Peak_Name                                                                  \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...              0.363299   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...              0.343792   \n",
       "SC_Control_IP_Merged_peak_100020                                0.627502   \n",
       "SC_Control_IP_Merged_peak_100028                                0.626793   \n",
       "SC_Control_IP_Merged_peak_10003                                 0.673447   \n",
       "\n",
       "                                                    SC_Control_2_IN_FPKM  \\\n",
       "Peak_Name                                                                  \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...              0.486045   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...              0.563942   \n",
       "SC_Control_IP_Merged_peak_100020                                0.824208   \n",
       "SC_Control_IP_Merged_peak_100028                                0.434256   \n",
       "SC_Control_IP_Merged_peak_10003                                 0.495741   \n",
       "\n",
       "                                                    SC_Control_3_IN_FPKM  \\\n",
       "Peak_Name                                                                  \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...              0.329294   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...              0.549904   \n",
       "SC_Control_IP_Merged_peak_100020                                0.687259   \n",
       "SC_Control_IP_Merged_peak_100028                                0.541070   \n",
       "SC_Control_IP_Merged_peak_10003                                 0.588611   \n",
       "\n",
       "                                                    SC_VitC_1_IN_FPKM  \\\n",
       "Peak_Name                                                               \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...           0.366767   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...           0.426288   \n",
       "SC_Control_IP_Merged_peak_100020                             0.642993   \n",
       "SC_Control_IP_Merged_peak_100028                             0.524299   \n",
       "SC_Control_IP_Merged_peak_10003                              0.704156   \n",
       "\n",
       "                                                    SC_VitC_2_IN_FPKM  \\\n",
       "Peak_Name                                                               \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...           0.425769   \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...           0.412388   \n",
       "SC_Control_IP_Merged_peak_100020                             0.837833   \n",
       "SC_Control_IP_Merged_peak_100028                             0.492713   \n",
       "SC_Control_IP_Merged_peak_10003                              0.700659   \n",
       "\n",
       "                                                    SC_VitC_3_IN_FPKM  \n",
       "Peak_Name                                                              \n",
       "SC_Control_IP_Merged_peak_100000,SC_LAA_IP_Merg...           0.430068  \n",
       "SC_Control_IP_Merged_peak_100011,SC_LAA_IP_Merg...           0.555402  \n",
       "SC_Control_IP_Merged_peak_100020                             0.615485  \n",
       "SC_Control_IP_Merged_peak_100028                             0.439135  \n",
       "SC_Control_IP_Merged_peak_10003                              0.589777  "
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_genomic_input_rcpm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SC_Control_1_IN_RCPM</th>\n",
       "      <th>SC_Control_2_IN_RCPM</th>\n",
       "      <th>SC_Control_3_IN_RCPM</th>\n",
       "      <th>SC_VitC_1_IN_RCPM</th>\n",
       "      <th>SC_VitC_2_IN_RCPM</th>\n",
       "      <th>SC_VitC_3_IN_RCPM</th>\n",
       "      <th>Start</th>\n",
       "      <th>Stop</th>\n",
       "      <th>Width</th>\n",
       "      <th>SC_Control_1_IN_FPKM</th>\n",
       "      <th>SC_Control_2_IN_FPKM</th>\n",
       "      <th>SC_Control_3_IN_FPKM</th>\n",
       "      <th>SC_VitC_1_IN_FPKM</th>\n",
       "      <th>SC_VitC_2_IN_FPKM</th>\n",
       "      <th>SC_VitC_3_IN_FPKM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>215951.000000</td>\n",
       "      <td>215951.000000</td>\n",
       "      <td>215951.000000</td>\n",
       "      <td>215951.000000</td>\n",
       "      <td>215951.000000</td>\n",
       "      <td>215951.000000</td>\n",
       "      <td>2.159510e+05</td>\n",
       "      <td>2.159510e+05</td>\n",
       "      <td>215951.000000</td>\n",
       "      <td>215951.000000</td>\n",
       "      <td>215951.000000</td>\n",
       "      <td>215951.000000</td>\n",
       "      <td>215951.000000</td>\n",
       "      <td>215951.000000</td>\n",
       "      <td>215951.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.393217</td>\n",
       "      <td>0.400663</td>\n",
       "      <td>0.395996</td>\n",
       "      <td>0.397272</td>\n",
       "      <td>0.397458</td>\n",
       "      <td>0.390218</td>\n",
       "      <td>8.314439e+07</td>\n",
       "      <td>8.314520e+07</td>\n",
       "      <td>819.269418</td>\n",
       "      <td>0.495449</td>\n",
       "      <td>0.504540</td>\n",
       "      <td>0.500739</td>\n",
       "      <td>0.503088</td>\n",
       "      <td>0.503577</td>\n",
       "      <td>0.494569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.264691</td>\n",
       "      <td>0.271959</td>\n",
       "      <td>0.264080</td>\n",
       "      <td>0.266468</td>\n",
       "      <td>0.264857</td>\n",
       "      <td>0.258963</td>\n",
       "      <td>6.182687e+07</td>\n",
       "      <td>6.182687e+07</td>\n",
       "      <td>578.368034</td>\n",
       "      <td>0.120438</td>\n",
       "      <td>0.130183</td>\n",
       "      <td>0.123059</td>\n",
       "      <td>0.135358</td>\n",
       "      <td>0.128446</td>\n",
       "      <td>0.125916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.009909</td>\n",
       "      <td>0.012014</td>\n",
       "      <td>0.017964</td>\n",
       "      <td>0.011604</td>\n",
       "      <td>0.028867</td>\n",
       "      <td>0.009720</td>\n",
       "      <td>5.500000e+01</td>\n",
       "      <td>5.080000e+02</td>\n",
       "      <td>257.000000</td>\n",
       "      <td>0.034648</td>\n",
       "      <td>0.038632</td>\n",
       "      <td>0.057027</td>\n",
       "      <td>0.031279</td>\n",
       "      <td>0.077600</td>\n",
       "      <td>0.028503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.227914</td>\n",
       "      <td>0.228274</td>\n",
       "      <td>0.233526</td>\n",
       "      <td>0.232090</td>\n",
       "      <td>0.230937</td>\n",
       "      <td>0.233269</td>\n",
       "      <td>3.536651e+07</td>\n",
       "      <td>3.536715e+07</td>\n",
       "      <td>455.000000</td>\n",
       "      <td>0.417234</td>\n",
       "      <td>0.420257</td>\n",
       "      <td>0.420473</td>\n",
       "      <td>0.415039</td>\n",
       "      <td>0.419769</td>\n",
       "      <td>0.412598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.317097</td>\n",
       "      <td>0.324389</td>\n",
       "      <td>0.323344</td>\n",
       "      <td>0.324926</td>\n",
       "      <td>0.327161</td>\n",
       "      <td>0.320744</td>\n",
       "      <td>7.017918e+07</td>\n",
       "      <td>7.017961e+07</td>\n",
       "      <td>640.000000</td>\n",
       "      <td>0.479482</td>\n",
       "      <td>0.487071</td>\n",
       "      <td>0.482167</td>\n",
       "      <td>0.482607</td>\n",
       "      <td>0.483316</td>\n",
       "      <td>0.475515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.475646</td>\n",
       "      <td>0.480577</td>\n",
       "      <td>0.476034</td>\n",
       "      <td>0.475784</td>\n",
       "      <td>0.471497</td>\n",
       "      <td>0.466537</td>\n",
       "      <td>1.178669e+08</td>\n",
       "      <td>1.178676e+08</td>\n",
       "      <td>977.000000</td>\n",
       "      <td>0.556702</td>\n",
       "      <td>0.570685</td>\n",
       "      <td>0.561360</td>\n",
       "      <td>0.569090</td>\n",
       "      <td>0.566023</td>\n",
       "      <td>0.555771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.986893</td>\n",
       "      <td>7.821386</td>\n",
       "      <td>7.859045</td>\n",
       "      <td>7.798220</td>\n",
       "      <td>7.823001</td>\n",
       "      <td>8.222722</td>\n",
       "      <td>2.825860e+08</td>\n",
       "      <td>2.825864e+08</td>\n",
       "      <td>18486.000000</td>\n",
       "      <td>4.886308</td>\n",
       "      <td>5.945065</td>\n",
       "      <td>4.769627</td>\n",
       "      <td>5.722216</td>\n",
       "      <td>4.993687</td>\n",
       "      <td>5.127890</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SC_Control_1_IN_RCPM  SC_Control_2_IN_RCPM  SC_Control_3_IN_RCPM  \\\n",
       "count         215951.000000         215951.000000         215951.000000   \n",
       "mean               0.393217              0.400663              0.395996   \n",
       "std                0.264691              0.271959              0.264080   \n",
       "min                0.009909              0.012014              0.017964   \n",
       "25%                0.227914              0.228274              0.233526   \n",
       "50%                0.317097              0.324389              0.323344   \n",
       "75%                0.475646              0.480577              0.476034   \n",
       "max                7.986893              7.821386              7.859045   \n",
       "\n",
       "       SC_VitC_1_IN_RCPM  SC_VitC_2_IN_RCPM  SC_VitC_3_IN_RCPM         Start  \\\n",
       "count      215951.000000      215951.000000      215951.000000  2.159510e+05   \n",
       "mean            0.397272           0.397458           0.390218  8.314439e+07   \n",
       "std             0.266468           0.264857           0.258963  6.182687e+07   \n",
       "min             0.011604           0.028867           0.009720  5.500000e+01   \n",
       "25%             0.232090           0.230937           0.233269  3.536651e+07   \n",
       "50%             0.324926           0.327161           0.320744  7.017918e+07   \n",
       "75%             0.475784           0.471497           0.466537  1.178669e+08   \n",
       "max             7.798220           7.823001           8.222722  2.825860e+08   \n",
       "\n",
       "               Stop          Width  SC_Control_1_IN_FPKM  \\\n",
       "count  2.159510e+05  215951.000000         215951.000000   \n",
       "mean   8.314520e+07     819.269418              0.495449   \n",
       "std    6.182687e+07     578.368034              0.120438   \n",
       "min    5.080000e+02     257.000000              0.034648   \n",
       "25%    3.536715e+07     455.000000              0.417234   \n",
       "50%    7.017961e+07     640.000000              0.479482   \n",
       "75%    1.178676e+08     977.000000              0.556702   \n",
       "max    2.825864e+08   18486.000000              4.886308   \n",
       "\n",
       "       SC_Control_2_IN_FPKM  SC_Control_3_IN_FPKM  SC_VitC_1_IN_FPKM  \\\n",
       "count         215951.000000         215951.000000      215951.000000   \n",
       "mean               0.504540              0.500739           0.503088   \n",
       "std                0.130183              0.123059           0.135358   \n",
       "min                0.038632              0.057027           0.031279   \n",
       "25%                0.420257              0.420473           0.415039   \n",
       "50%                0.487071              0.482167           0.482607   \n",
       "75%                0.570685              0.561360           0.569090   \n",
       "max                5.945065              4.769627           5.722216   \n",
       "\n",
       "       SC_VitC_2_IN_FPKM  SC_VitC_3_IN_FPKM  \n",
       "count      215951.000000      215951.000000  \n",
       "mean            0.503577           0.494569  \n",
       "std             0.128446           0.125916  \n",
       "min             0.077600           0.028503  \n",
       "25%             0.419769           0.412598  \n",
       "50%             0.483316           0.475515  \n",
       "75%             0.566023           0.555771  \n",
       "max             4.993687           5.127890  "
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_genomic_input_rcpm.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_Genomic_Input_FPKM_table = SC_VitC_genomic_input_rcpm[[\"SC_Control_1_IN_FPKM\", \"SC_Control_2_IN_FPKM\", \"SC_Control_3_IN_FPKM\", \n",
    "                                                               \"SC_VitC_1_IN_FPKM\", \"SC_VitC_2_IN_FPKM\", \"SC_VitC_3_IN_FPKM\"]]\n",
    "SC_VitC_Genomic_Input_FPKM_table.to_csv(Output_Path+\"SC_VitC_Genomic_Input_FPKM_table.csv\")\n",
    "#I am not going to make a pickle file for this one because I don't think we are likely to use it in future analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well, calculating the FPKM for the genomic input without removing outliers was much less painful. Now I can move on to a density plot with genomic input (should have no bias to any region) and RNA. \n",
    "\n",
    "The genomic input is just sheared DNA with no form of pull-down. This is meant to show us what kind of coverage you get in each genomic region just by chance of the DNA-sequence. These were generated for calling peaks in the hMeDIP, but can be used her to show what we get if we are just sequencing noise. \n",
    "\n",
    "I am going to look at a density plot of the genomic input samples. These are just noise, so if we use a cutoff for limit of detection below the top expressed of these, those ones would essentially be \"false positives\" as to limit of detection.\n",
    "\n",
    "When I look at the denisty plot for the RNA, those are annotated genes. In other words, those should be annotated as \"above limit of detection\", so if I use a cutoff above the most lowly expressed gene, this gives \"false negatives\" as to the limit of detection. \n",
    "\n",
    "I am going to chart both the genomic input samples and the RNA samples on the same graph. Where the two overlap, there will be a minimum between the \"false positives\" and the \"false negatives\". This value gives us our limit of detection. (See Trapnell et al. 2012 Nature Protocols)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First I need to take the log10 of each value. It makes it easier to visualize. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "\n",
    "SC_VitC_Genomic_Input_FPKM_table = pd.read_csv(Output_Path+\"SC_VitC_Genomic_Input_FPKM_table.csv\", sep = \",\", index_col = 0)\n",
    "SC_VitC_RNA_FPKM_table = pd.read_pickle(Output_Path+\"SC_VitC_RNA_FPKM_table.pkl\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SC_Control_1_FPKM</th>\n",
       "      <th>SC_Control_2_FPKM</th>\n",
       "      <th>SC_Control_3_FPKM</th>\n",
       "      <th>SC_VitC_1_FPKM</th>\n",
       "      <th>SC_VitC_2_FPKM</th>\n",
       "      <th>SC_VitC_3_FPKM</th>\n",
       "      <th>Control_Average_FPKM</th>\n",
       "      <th>VitC_Average_FPKM</th>\n",
       "      <th>Fold_Change</th>\n",
       "      <th>Log2FC</th>\n",
       "      <th>edgeR</th>\n",
       "      <th>DESeq2</th>\n",
       "      <th>Change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ensembl_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000001</th>\n",
       "      <td>0.715156</td>\n",
       "      <td>0.486008</td>\n",
       "      <td>0.754285</td>\n",
       "      <td>0.725724</td>\n",
       "      <td>0.903497</td>\n",
       "      <td>0.644060</td>\n",
       "      <td>0.562391</td>\n",
       "      <td>0.757760</td>\n",
       "      <td>1.347391</td>\n",
       "      <td>0.430168</td>\n",
       "      <td>7.507860e-01</td>\n",
       "      <td>6.209275e-01</td>\n",
       "      <td>Nondifferential</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000007</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012158</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011239</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007799</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.898456e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>Nondifferential</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000008</th>\n",
       "      <td>2.411374</td>\n",
       "      <td>2.382310</td>\n",
       "      <td>2.445488</td>\n",
       "      <td>4.776368</td>\n",
       "      <td>4.660176</td>\n",
       "      <td>5.024550</td>\n",
       "      <td>2.391998</td>\n",
       "      <td>4.820365</td>\n",
       "      <td>2.015204</td>\n",
       "      <td>1.010926</td>\n",
       "      <td>3.470000e-12</td>\n",
       "      <td>1.440000e-09</td>\n",
       "      <td>Upregulated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000009</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.027920</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009307</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.624017e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>Nondifferential</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000010</th>\n",
       "      <td>0.033148</td>\n",
       "      <td>0.026817</td>\n",
       "      <td>0.019001</td>\n",
       "      <td>0.050456</td>\n",
       "      <td>0.031725</td>\n",
       "      <td>0.077740</td>\n",
       "      <td>0.028927</td>\n",
       "      <td>0.053307</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.854191e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>Nondifferential</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    SC_Control_1_FPKM  SC_Control_2_FPKM  SC_Control_3_FPKM  \\\n",
       "Ensembl_ID                                                                    \n",
       "ENSRNOG00000000001           0.715156           0.486008           0.754285   \n",
       "ENSRNOG00000000007           0.000000           0.000000           0.000000   \n",
       "ENSRNOG00000000008           2.411374           2.382310           2.445488   \n",
       "ENSRNOG00000000009           0.000000           0.000000           0.000000   \n",
       "ENSRNOG00000000010           0.033148           0.026817           0.019001   \n",
       "\n",
       "                    SC_VitC_1_FPKM  SC_VitC_2_FPKM  SC_VitC_3_FPKM  \\\n",
       "Ensembl_ID                                                           \n",
       "ENSRNOG00000000001        0.725724        0.903497        0.644060   \n",
       "ENSRNOG00000000007        0.012158        0.000000        0.011239   \n",
       "ENSRNOG00000000008        4.776368        4.660176        5.024550   \n",
       "ENSRNOG00000000009        0.000000        0.000000        0.027920   \n",
       "ENSRNOG00000000010        0.050456        0.031725        0.077740   \n",
       "\n",
       "                    Control_Average_FPKM  VitC_Average_FPKM  Fold_Change  \\\n",
       "Ensembl_ID                                                                 \n",
       "ENSRNOG00000000001              0.562391           0.757760     1.347391   \n",
       "ENSRNOG00000000007              0.000000           0.007799     1.000000   \n",
       "ENSRNOG00000000008              2.391998           4.820365     2.015204   \n",
       "ENSRNOG00000000009              0.000000           0.009307     1.000000   \n",
       "ENSRNOG00000000010              0.028927           0.053307     1.000000   \n",
       "\n",
       "                      Log2FC         edgeR        DESeq2           Change  \n",
       "Ensembl_ID                                                                 \n",
       "ENSRNOG00000000001  0.430168  7.507860e-01  6.209275e-01  Nondifferential  \n",
       "ENSRNOG00000000007  0.000000  3.898456e-01  1.000000e+00  Nondifferential  \n",
       "ENSRNOG00000000008  1.010926  3.470000e-12  1.440000e-09      Upregulated  \n",
       "ENSRNOG00000000009  0.000000  6.624017e-01  1.000000e+00  Nondifferential  \n",
       "ENSRNOG00000000010  0.000000  5.854191e-01  1.000000e+00  Nondifferential  "
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_RNA_FPKM_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "control_1_RNA = []\n",
    "for i in SC_VitC_RNA_FPKM_table[\"SC_Control_1_FPKM\"].values:\n",
    "    if i > 0.001:\n",
    "        control_1_RNA.append(np.log10(i))\n",
    "\n",
    "control_2_RNA = []\n",
    "for i in SC_VitC_RNA_FPKM_table[\"SC_Control_2_FPKM\"].values:\n",
    "    if i > 0.001:\n",
    "        control_2_RNA.append(np.log10(i))\n",
    "\n",
    "control_3_RNA = []\n",
    "for i in SC_VitC_RNA_FPKM_table[\"SC_Control_3_FPKM\"].values:\n",
    "    if i > 0.001:\n",
    "        control_3_RNA.append(np.log10(i))\n",
    "\n",
    "vitc_1_RNA = []\n",
    "for i in SC_VitC_RNA_FPKM_table[\"SC_VitC_1_FPKM\"].values:\n",
    "    if i > 0.001:\n",
    "        vitc_1_RNA.append(np.log10(i))\n",
    "\n",
    "vitc_2_RNA = []\n",
    "for i in SC_VitC_RNA_FPKM_table[\"SC_VitC_2_FPKM\"].values:\n",
    "    if i > 0.001:\n",
    "        vitc_2_RNA.append(np.log10(i))\n",
    "\n",
    "vitc_3_RNA = []\n",
    "for i in SC_VitC_RNA_FPKM_table[\"SC_VitC_3_FPKM\"].values:\n",
    "    if i > 0.001:\n",
    "        vitc_3_RNA.append(np.log10(i))\n",
    "        \n",
    "\n",
    "\n",
    "control_1_INPUT = []\n",
    "for i in SC_VitC_Genomic_Input_FPKM_table[\"SC_Control_1_IN_FPKM\"].values:\n",
    "    if i > 0.001:\n",
    "        control_1_INPUT.append(np.log10(i))\n",
    "        \n",
    "control_2_INPUT = []\n",
    "for i in SC_VitC_Genomic_Input_FPKM_table[\"SC_Control_2_IN_FPKM\"].values:\n",
    "    if i > 0.001:\n",
    "        control_2_INPUT.append(np.log10(i))\n",
    "        \n",
    "control_3_INPUT = []\n",
    "for i in SC_VitC_Genomic_Input_FPKM_table[\"SC_Control_3_IN_FPKM\"].values:\n",
    "    if i > 0.001:\n",
    "        control_3_INPUT.append(np.log10(i))\n",
    "        \n",
    "        \n",
    "vitc_1_INPUT = []\n",
    "for i in SC_VitC_Genomic_Input_FPKM_table[\"SC_VitC_1_IN_FPKM\"].values:\n",
    "    if i > 0.001:\n",
    "        vitc_1_INPUT.append(np.log10(i))\n",
    "        \n",
    "vitc_2_INPUT = []\n",
    "for i in SC_VitC_Genomic_Input_FPKM_table[\"SC_VitC_2_IN_FPKM\"].values:\n",
    "    if i > 0.001:\n",
    "        vitc_2_INPUT.append(np.log10(i))\n",
    "\n",
    "vitc_3_INPUT = []\n",
    "for i in SC_VitC_Genomic_Input_FPKM_table[\"SC_VitC_3_IN_FPKM\"].values:\n",
    "    if i > 0.001:\n",
    "        vitc_3_INPUT.append(np.log10(i))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16724\n",
      "16993\n",
      "16259\n",
      "17229\n",
      "17040\n",
      "17347\n",
      "215951\n",
      "215951\n",
      "215951\n",
      "215951\n",
      "215951\n",
      "215951\n"
     ]
    }
   ],
   "source": [
    "print(len(control_1_RNA))\n",
    "print(len(control_2_RNA))\n",
    "print(len(control_3_RNA))\n",
    "print(len(vitc_1_RNA))\n",
    "print(len(vitc_2_RNA))\n",
    "print(len(vitc_3_RNA))\n",
    "print(len(control_1_INPUT))\n",
    "print(len(control_2_INPUT))\n",
    "print(len(control_3_INPUT))\n",
    "print(len(vitc_1_INPUT))\n",
    "print(len(vitc_2_INPUT))\n",
    "print(len(vitc_3_INPUT))\n",
    "# The lengths are not identical for any of them indicating I didn't accidentally copy over any of them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_tables_list = [control_1_RNA, control_2_RNA, control_3_RNA, vitc_1_RNA, vitc_2_RNA, vitc_3_RNA,\n",
    "                   control_1_INPUT, control_2_INPUT, control_3_INPUT, vitc_1_INPUT, vitc_2_INPUT, vitc_3_INPUT]\n",
    "table_of_logs = pd.DataFrame((_ for _ in itertools.zip_longest(*log_tables_list)), columns=[\"control_1_RNA\", \"control_2_RNA\", \"control_3_RNA\", \n",
    "                                                                                                \"vitc_1_RNA\", \"vitc_2_RNA\", \"vitc_3_RNA\", \n",
    "                                                                                                \"control_1_INPUT\", \"control_2_INPUT\", \"control_3_INPUT\",\n",
    "                                                                                            \"vitc_1_INPUT\", \"vitc_2_INPUT\", \"vitc_3_INPUT\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>control_1_RNA</th>\n",
       "      <th>control_2_RNA</th>\n",
       "      <th>control_3_RNA</th>\n",
       "      <th>vitc_1_RNA</th>\n",
       "      <th>vitc_2_RNA</th>\n",
       "      <th>vitc_3_RNA</th>\n",
       "      <th>control_1_INPUT</th>\n",
       "      <th>control_2_INPUT</th>\n",
       "      <th>control_3_INPUT</th>\n",
       "      <th>vitc_1_INPUT</th>\n",
       "      <th>vitc_2_INPUT</th>\n",
       "      <th>vitc_3_INPUT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.145599</td>\n",
       "      <td>-0.313357</td>\n",
       "      <td>-0.122465</td>\n",
       "      <td>-0.139229</td>\n",
       "      <td>-0.044073</td>\n",
       "      <td>-0.191074</td>\n",
       "      <td>-0.439736</td>\n",
       "      <td>-0.313324</td>\n",
       "      <td>-0.482417</td>\n",
       "      <td>-0.435610</td>\n",
       "      <td>-0.370826</td>\n",
       "      <td>-0.366463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.382265</td>\n",
       "      <td>0.376998</td>\n",
       "      <td>0.388366</td>\n",
       "      <td>-1.915151</td>\n",
       "      <td>0.668402</td>\n",
       "      <td>-1.949267</td>\n",
       "      <td>-0.463704</td>\n",
       "      <td>-0.248766</td>\n",
       "      <td>-0.259713</td>\n",
       "      <td>-0.370297</td>\n",
       "      <td>-0.384694</td>\n",
       "      <td>-0.255393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.479547</td>\n",
       "      <td>-1.571584</td>\n",
       "      <td>-1.721231</td>\n",
       "      <td>0.679098</td>\n",
       "      <td>-1.498595</td>\n",
       "      <td>0.701097</td>\n",
       "      <td>-0.202385</td>\n",
       "      <td>-0.083963</td>\n",
       "      <td>-0.162879</td>\n",
       "      <td>-0.191794</td>\n",
       "      <td>-0.076842</td>\n",
       "      <td>-0.210783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.177466</td>\n",
       "      <td>-0.073209</td>\n",
       "      <td>-0.042399</td>\n",
       "      <td>-1.297085</td>\n",
       "      <td>-1.408473</td>\n",
       "      <td>-1.554080</td>\n",
       "      <td>-0.202876</td>\n",
       "      <td>-0.362254</td>\n",
       "      <td>-0.266746</td>\n",
       "      <td>-0.280421</td>\n",
       "      <td>-0.307406</td>\n",
       "      <td>-0.357402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.106982</td>\n",
       "      <td>1.136144</td>\n",
       "      <td>1.160561</td>\n",
       "      <td>-0.150893</td>\n",
       "      <td>0.213660</td>\n",
       "      <td>-1.109353</td>\n",
       "      <td>-0.171696</td>\n",
       "      <td>-0.304746</td>\n",
       "      <td>-0.230172</td>\n",
       "      <td>-0.152331</td>\n",
       "      <td>-0.154493</td>\n",
       "      <td>-0.229312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   control_1_RNA  control_2_RNA  control_3_RNA  vitc_1_RNA  vitc_2_RNA  \\\n",
       "0      -0.145599      -0.313357      -0.122465   -0.139229   -0.044073   \n",
       "1       0.382265       0.376998       0.388366   -1.915151    0.668402   \n",
       "2      -1.479547      -1.571584      -1.721231    0.679098   -1.498595   \n",
       "3      -0.177466      -0.073209      -0.042399   -1.297085   -1.408473   \n",
       "4       1.106982       1.136144       1.160561   -0.150893    0.213660   \n",
       "\n",
       "   vitc_3_RNA  control_1_INPUT  control_2_INPUT  control_3_INPUT  \\\n",
       "0   -0.191074        -0.439736        -0.313324        -0.482417   \n",
       "1   -1.949267        -0.463704        -0.248766        -0.259713   \n",
       "2    0.701097        -0.202385        -0.083963        -0.162879   \n",
       "3   -1.554080        -0.202876        -0.362254        -0.266746   \n",
       "4   -1.109353        -0.171696        -0.304746        -0.230172   \n",
       "\n",
       "   vitc_1_INPUT  vitc_2_INPUT  vitc_3_INPUT  \n",
       "0     -0.435610     -0.370826     -0.366463  \n",
       "1     -0.370297     -0.384694     -0.255393  \n",
       "2     -0.191794     -0.076842     -0.210783  \n",
       "3     -0.280421     -0.307406     -0.357402  \n",
       "4     -0.152331     -0.154493     -0.229312  "
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_of_logs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/david/anaconda3/lib/python3.7/site-packages/statsmodels/nonparametric/kde.py:448: RuntimeWarning: invalid value encountered in greater\n",
      "  X = X[np.logical_and(X > clip[0], X < clip[1])] # won't work for two columns.\n",
      "/Users/david/anaconda3/lib/python3.7/site-packages/statsmodels/nonparametric/kde.py:448: RuntimeWarning: invalid value encountered in less\n",
      "  X = X[np.logical_and(X > clip[0], X < clip[1])] # won't work for two columns.\n",
      "/Users/david/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_names = [\"control_1_RNA\", \"control_2_RNA\", \"control_3_RNA\", \"vitc_1_RNA\", \"vitc_2_RNA\", \"vitc_3_RNA\", \"control_1_INPUT\", \"control_2_INPUT\", \"control_3_INPUT\", \"vitc_1_INPUT\", \"vitc_2_INPUT\", \"vitc_3_INPUT\"]\n",
    "fig, ax = plt.subplots()##\n",
    "plt.title(\"Density Plot with RNA and Genomic Input Samples\", color='black', size = 10)\n",
    "plt.ylabel(\"Density\", color='black')\n",
    "plt.xlabel(\"Log10 FPKM\", color='black')\n",
    "ax.set_facecolor('white')\n",
    "ax.set_facecolor('white')\n",
    "ax.tick_params(color='black', labelcolor='black')\n",
    "ax.spines['bottom'].set_color('black')\n",
    "ax.spines['left'].set_color('black')\n",
    "ax.grid(color='lightslategray', linestyle='--', linewidth=0.5, alpha=0.4)\n",
    "plt.tick_params(axis = 'both', which = 'major', labelsize='10')\n",
    "\n",
    "for sample in sample_names:\n",
    "    # Subset to the airline\n",
    "    subset = table_of_logs[[sample]].values\n",
    "    \n",
    "    # Draw the density plot\n",
    "    sns.distplot(subset, hist = False, kde = True, \n",
    "                 kde_kws = {'linewidth': 1}, label = sample)\n",
    "\n",
    "\n",
    "plt.legend(prop={'size': 8}, title = 'Sample')\n",
    "#plt.title('Density Plot with RNA and Genomic Input Samples')\n",
    "#plt.xlabel('Log10 FPKM')\n",
    "#plt.ylabel('Density')\n",
    "plt.savefig(Output_Path+\"SC_VitC_RNA_LOD_Chart.png\", dpi=300) # size above specified at 10x6\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_names = [\"control_1_RNA\", \"control_2_RNA\", \"control_3_RNA\", \"vitc_1_RNA\", \"vitc_2_RNA\", \"vitc_3_RNA\", \"control_1_INPUT\", \"control_2_INPUT\", \"control_3_INPUT\", \"vitc_1_INPUT\", \"vitc_2_INPUT\", \"vitc_3_INPUT\"]\n",
    "fig, ax = plt.subplots()##\n",
    "plt.title(\"Density Plot with RNA and Genomic Input Samples\", color='black', size = 10)\n",
    "plt.ylabel(\"Density\", color='black')\n",
    "plt.xlabel(\"Log10 FPKM\", color='black')\n",
    "ax.set_facecolor('white')\n",
    "ax.set_facecolor('white')\n",
    "ax.tick_params(color='black', labelcolor='black')\n",
    "ax.spines['bottom'].set_color('black')\n",
    "ax.spines['left'].set_color('black')\n",
    "ax.grid(color='lightslategray', linestyle='--', linewidth=0.5, alpha=0.4)\n",
    "ax.set_xlim(-0.75,0.25)\n",
    "plt.tick_params(axis = 'both', which = 'major', labelsize='10')\n",
    "plt.axvline(x=-0.05)\n",
    "\n",
    "for sample in sample_names:\n",
    "    # Subset to the airline\n",
    "    subset = table_of_logs[[sample]].values\n",
    "    \n",
    "    # Draw the density plot\n",
    "    sns.distplot(subset, hist = False, kde = True, \n",
    "                 kde_kws = {'linewidth': 1}, label = sample)\n",
    "\n",
    "\n",
    "plt.legend(prop={'size': 8}, title = 'Sample')\n",
    "#plt.title('Density Plot with RNA and Genomic Input Samples')\n",
    "#plt.xlabel('Log10 FPKM')\n",
    "#plt.ylabel('Density')\n",
    "plt.savefig(Output_Path+\"SC_VitC_RNA_LOD_Chart_Zoomed.png\", dpi=300) # size above specified at 10x6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the graphs above, we can see that they cross at -0.05. This is log base 10, so this converts to 0.89125094 FPKM. I am going to use 0.89 as my limit of detection for simplicity. Moving forward, I am going to keep genes that have one or both sample types averages above 0.89 FPKM. These are the ones that will be used for further analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SC_Control_1_FPKM</th>\n",
       "      <th>SC_Control_2_FPKM</th>\n",
       "      <th>SC_Control_3_FPKM</th>\n",
       "      <th>SC_VitC_1_FPKM</th>\n",
       "      <th>SC_VitC_2_FPKM</th>\n",
       "      <th>SC_VitC_3_FPKM</th>\n",
       "      <th>Control_Average_FPKM</th>\n",
       "      <th>VitC_Average_FPKM</th>\n",
       "      <th>Fold_Change</th>\n",
       "      <th>Log2FC</th>\n",
       "      <th>edgeR</th>\n",
       "      <th>DESeq2</th>\n",
       "      <th>Change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ensembl_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000001</th>\n",
       "      <td>0.715156</td>\n",
       "      <td>0.486008</td>\n",
       "      <td>0.754285</td>\n",
       "      <td>0.725724</td>\n",
       "      <td>0.903497</td>\n",
       "      <td>0.644060</td>\n",
       "      <td>0.562391</td>\n",
       "      <td>0.757760</td>\n",
       "      <td>1.347391</td>\n",
       "      <td>0.430168</td>\n",
       "      <td>7.507860e-01</td>\n",
       "      <td>6.209275e-01</td>\n",
       "      <td>Nondifferential</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000007</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012158</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011239</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007799</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.898456e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>Nondifferential</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000008</th>\n",
       "      <td>2.411374</td>\n",
       "      <td>2.382310</td>\n",
       "      <td>2.445488</td>\n",
       "      <td>4.776368</td>\n",
       "      <td>4.660176</td>\n",
       "      <td>5.024550</td>\n",
       "      <td>2.391998</td>\n",
       "      <td>4.820365</td>\n",
       "      <td>2.015204</td>\n",
       "      <td>1.010926</td>\n",
       "      <td>3.470000e-12</td>\n",
       "      <td>1.440000e-09</td>\n",
       "      <td>Upregulated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000009</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.027920</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009307</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.624017e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>Nondifferential</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000010</th>\n",
       "      <td>0.033148</td>\n",
       "      <td>0.026817</td>\n",
       "      <td>0.019001</td>\n",
       "      <td>0.050456</td>\n",
       "      <td>0.031725</td>\n",
       "      <td>0.077740</td>\n",
       "      <td>0.028927</td>\n",
       "      <td>0.053307</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.854191e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>Nondifferential</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    SC_Control_1_FPKM  SC_Control_2_FPKM  SC_Control_3_FPKM  \\\n",
       "Ensembl_ID                                                                    \n",
       "ENSRNOG00000000001           0.715156           0.486008           0.754285   \n",
       "ENSRNOG00000000007           0.000000           0.000000           0.000000   \n",
       "ENSRNOG00000000008           2.411374           2.382310           2.445488   \n",
       "ENSRNOG00000000009           0.000000           0.000000           0.000000   \n",
       "ENSRNOG00000000010           0.033148           0.026817           0.019001   \n",
       "\n",
       "                    SC_VitC_1_FPKM  SC_VitC_2_FPKM  SC_VitC_3_FPKM  \\\n",
       "Ensembl_ID                                                           \n",
       "ENSRNOG00000000001        0.725724        0.903497        0.644060   \n",
       "ENSRNOG00000000007        0.012158        0.000000        0.011239   \n",
       "ENSRNOG00000000008        4.776368        4.660176        5.024550   \n",
       "ENSRNOG00000000009        0.000000        0.000000        0.027920   \n",
       "ENSRNOG00000000010        0.050456        0.031725        0.077740   \n",
       "\n",
       "                    Control_Average_FPKM  VitC_Average_FPKM  Fold_Change  \\\n",
       "Ensembl_ID                                                                 \n",
       "ENSRNOG00000000001              0.562391           0.757760     1.347391   \n",
       "ENSRNOG00000000007              0.000000           0.007799     1.000000   \n",
       "ENSRNOG00000000008              2.391998           4.820365     2.015204   \n",
       "ENSRNOG00000000009              0.000000           0.009307     1.000000   \n",
       "ENSRNOG00000000010              0.028927           0.053307     1.000000   \n",
       "\n",
       "                      Log2FC         edgeR        DESeq2           Change  \n",
       "Ensembl_ID                                                                 \n",
       "ENSRNOG00000000001  0.430168  7.507860e-01  6.209275e-01  Nondifferential  \n",
       "ENSRNOG00000000007  0.000000  3.898456e-01  1.000000e+00  Nondifferential  \n",
       "ENSRNOG00000000008  1.010926  3.470000e-12  1.440000e-09      Upregulated  \n",
       "ENSRNOG00000000009  0.000000  6.624017e-01  1.000000e+00  Nondifferential  \n",
       "ENSRNOG00000000010  0.000000  5.854191e-01  1.000000e+00  Nondifferential  "
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_RNA_FPKM_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_RNA_FPKM_table[\"Expressed\"] = SC_VitC_RNA_FPKM_table.apply(lambda x: (\"Expressed\") if (x[\"VitC_Average_FPKM\"] > 0.89 or x[\"Control_Average_FPKM\"] > 0.89) else \"Below_LOD\", axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SC_Control_1_FPKM</th>\n",
       "      <th>SC_Control_2_FPKM</th>\n",
       "      <th>SC_Control_3_FPKM</th>\n",
       "      <th>SC_VitC_1_FPKM</th>\n",
       "      <th>SC_VitC_2_FPKM</th>\n",
       "      <th>SC_VitC_3_FPKM</th>\n",
       "      <th>Control_Average_FPKM</th>\n",
       "      <th>VitC_Average_FPKM</th>\n",
       "      <th>Fold_Change</th>\n",
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       "      <th>edgeR</th>\n",
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       "      <th>Expressed</th>\n",
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       "    <tr>\n",
       "      <th>Ensembl_ID</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000001</th>\n",
       "      <td>0.715156</td>\n",
       "      <td>0.486008</td>\n",
       "      <td>0.754285</td>\n",
       "      <td>0.725724</td>\n",
       "      <td>0.903497</td>\n",
       "      <td>0.644060</td>\n",
       "      <td>0.562391</td>\n",
       "      <td>0.757760</td>\n",
       "      <td>1.347391</td>\n",
       "      <td>0.430168</td>\n",
       "      <td>7.507860e-01</td>\n",
       "      <td>6.209275e-01</td>\n",
       "      <td>Nondifferential</td>\n",
       "      <td>Below_LOD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000007</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012158</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011239</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007799</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.898456e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>Nondifferential</td>\n",
       "      <td>Below_LOD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000008</th>\n",
       "      <td>2.411374</td>\n",
       "      <td>2.382310</td>\n",
       "      <td>2.445488</td>\n",
       "      <td>4.776368</td>\n",
       "      <td>4.660176</td>\n",
       "      <td>5.024550</td>\n",
       "      <td>2.391998</td>\n",
       "      <td>4.820365</td>\n",
       "      <td>2.015204</td>\n",
       "      <td>1.010926</td>\n",
       "      <td>3.470000e-12</td>\n",
       "      <td>1.440000e-09</td>\n",
       "      <td>Upregulated</td>\n",
       "      <td>Expressed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000009</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.027920</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009307</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.624017e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>Nondifferential</td>\n",
       "      <td>Below_LOD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000010</th>\n",
       "      <td>0.033148</td>\n",
       "      <td>0.026817</td>\n",
       "      <td>0.019001</td>\n",
       "      <td>0.050456</td>\n",
       "      <td>0.031725</td>\n",
       "      <td>0.077740</td>\n",
       "      <td>0.028927</td>\n",
       "      <td>0.053307</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.854191e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>Nondifferential</td>\n",
       "      <td>Below_LOD</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    SC_Control_1_FPKM  SC_Control_2_FPKM  SC_Control_3_FPKM  \\\n",
       "Ensembl_ID                                                                    \n",
       "ENSRNOG00000000001           0.715156           0.486008           0.754285   \n",
       "ENSRNOG00000000007           0.000000           0.000000           0.000000   \n",
       "ENSRNOG00000000008           2.411374           2.382310           2.445488   \n",
       "ENSRNOG00000000009           0.000000           0.000000           0.000000   \n",
       "ENSRNOG00000000010           0.033148           0.026817           0.019001   \n",
       "\n",
       "                    SC_VitC_1_FPKM  SC_VitC_2_FPKM  SC_VitC_3_FPKM  \\\n",
       "Ensembl_ID                                                           \n",
       "ENSRNOG00000000001        0.725724        0.903497        0.644060   \n",
       "ENSRNOG00000000007        0.012158        0.000000        0.011239   \n",
       "ENSRNOG00000000008        4.776368        4.660176        5.024550   \n",
       "ENSRNOG00000000009        0.000000        0.000000        0.027920   \n",
       "ENSRNOG00000000010        0.050456        0.031725        0.077740   \n",
       "\n",
       "                    Control_Average_FPKM  VitC_Average_FPKM  Fold_Change  \\\n",
       "Ensembl_ID                                                                 \n",
       "ENSRNOG00000000001              0.562391           0.757760     1.347391   \n",
       "ENSRNOG00000000007              0.000000           0.007799     1.000000   \n",
       "ENSRNOG00000000008              2.391998           4.820365     2.015204   \n",
       "ENSRNOG00000000009              0.000000           0.009307     1.000000   \n",
       "ENSRNOG00000000010              0.028927           0.053307     1.000000   \n",
       "\n",
       "                      Log2FC         edgeR        DESeq2           Change  \\\n",
       "Ensembl_ID                                                                  \n",
       "ENSRNOG00000000001  0.430168  7.507860e-01  6.209275e-01  Nondifferential   \n",
       "ENSRNOG00000000007  0.000000  3.898456e-01  1.000000e+00  Nondifferential   \n",
       "ENSRNOG00000000008  1.010926  3.470000e-12  1.440000e-09      Upregulated   \n",
       "ENSRNOG00000000009  0.000000  6.624017e-01  1.000000e+00  Nondifferential   \n",
       "ENSRNOG00000000010  0.000000  5.854191e-01  1.000000e+00  Nondifferential   \n",
       "\n",
       "                    Expressed  \n",
       "Ensembl_ID                     \n",
       "ENSRNOG00000000001  Below_LOD  \n",
       "ENSRNOG00000000007  Below_LOD  \n",
       "ENSRNOG00000000008  Expressed  \n",
       "ENSRNOG00000000009  Below_LOD  \n",
       "ENSRNOG00000000010  Below_LOD  "
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_RNA_FPKM_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_Expressed_Genes = SC_VitC_RNA_FPKM_table[SC_VitC_RNA_FPKM_table[\"Expressed\"] == \"Expressed\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11848"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(SC_VitC_Expressed_Genes)\n",
    "# We have 11,848 genes left that are expressed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I am a little bummed to see that we only have 11,848 genes left. I think we have more that are really detectable, so I am going to go with the old fashioned way of taking the minimum point between the two humps of only the RNA.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_names = [\"control_1_RNA\", \"control_2_RNA\", \"control_3_RNA\", \"vitc_1_RNA\", \"vitc_2_RNA\", \"vitc_3_RNA\"]\n",
    "fig, ax = plt.subplots()##\n",
    "plt.title(\"Density Plot with RNA and Genomic Input Samples\", color='black', size = 10)\n",
    "plt.ylabel(\"Density\", color='black')\n",
    "plt.xlabel(\"Log10 FPKM\", color='black')\n",
    "ax.set_facecolor('white')\n",
    "ax.set_facecolor('white')\n",
    "ax.tick_params(color='black', labelcolor='black')\n",
    "ax.spines['bottom'].set_color('black')\n",
    "ax.spines['left'].set_color('black')\n",
    "ax.grid(color='lightslategray', linestyle='--', linewidth=0.5, alpha=0.4)\n",
    "#ax.set_xlim(-0.75,0.25)\n",
    "plt.tick_params(axis = 'both', which = 'major', labelsize='10')\n",
    "plt.axvline(x=-0.15)\n",
    "\n",
    "for sample in sample_names:\n",
    "    # Subset to the airline\n",
    "    subset = table_of_logs[[sample]].values\n",
    "    \n",
    "    # Draw the density plot\n",
    "    sns.distplot(subset, hist = False, kde = True, \n",
    "                 kde_kws = {'linewidth': 1}, label = sample)\n",
    "\n",
    "\n",
    "plt.legend(prop={'size': 8}, title = 'Sample')\n",
    "#plt.title('Density Plot with RNA and Genomic Input Samples')\n",
    "#plt.xlabel('Log10 FPKM')\n",
    "#plt.ylabel('Density')\n",
    "plt.savefig(Output_Path+\"SC_VitC_RNA_LOD_Chart_Zoomed.png\", dpi=300) # size above specified at 10x6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If I change the line to -0.15, it changes the LOD to only 0.71, which I think is more reasonable. I am going to try that. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12152"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_VitC_RNA_FPKM_table[\"Expressed\"] = SC_VitC_RNA_FPKM_table.apply(lambda x: (\"Expressed\") if (x[\"VitC_Average_FPKM\"] > 0.71 or x[\"Control_Average_FPKM\"] > 0.71) else \"Below_LOD\", axis = 1)\n",
    "SC_VitC_Expressed_Genes = SC_VitC_RNA_FPKM_table[SC_VitC_RNA_FPKM_table[\"Expressed\"] == \"Expressed\"]\n",
    "len(SC_VitC_Expressed_Genes)\n",
    "# We have 12,152 genes left that are expressed\n",
    "# I guess that is a few more to look at. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downregulated Genes: 1798\n",
      "Upregulated Genes: 1984\n",
      "Nondifferential Genes: 8370\n"
     ]
    }
   ],
   "source": [
    "print(\"Downregulated Genes:\", len(SC_VitC_Expressed_Genes[SC_VitC_Expressed_Genes[\"Change\"] == \"Downregulated\"]))\n",
    "print(\"Upregulated Genes:\",len(SC_VitC_Expressed_Genes[SC_VitC_Expressed_Genes[\"Change\"] == \"Upregulated\"]))\n",
    "print(\"Nondifferential Genes:\",len(SC_VitC_Expressed_Genes[SC_VitC_Expressed_Genes[\"Change\"] == \"Nondifferential\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_Expressed_Genes.to_csv(Output_Path+\"SC_VitC_Expressed_RNA.csv\")\n",
    "SC_VitC_Expressed_Genes.to_pickle(Output_Path+\"SC_VitC_Expressed_RNA.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that we have 1,984 genes that go up and 1,798 genes that go down after treatement with vitamin C. This is much lower than the 8,370 genes that do not change after treatment. This matches the assumption that most genes will not change, and indicates that our treatment was not too extreme."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I have a table of the expressed genes, as well as the information about how they react to vitamin C treatment.\n",
    "The next step is going to be to integrate this with the information that we have generated about the hMeDIP peaks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_Expressed_Genes = pd.read_pickle(Output_Path+\"SC_VitC_Expressed_RNA.pkl\")\n",
    "Rat_Genomic_Info_File = pd.read_csv(Input_genomic_info_file, sep = \",\", index_col = 0 )\n",
    "summed_counts = pd.read_pickle(Output_Path+\"SC_VitC_Counts_of_peaks_per_region.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gene_Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Sense</th>\n",
       "      <th>Gene_Biotype</th>\n",
       "      <th>transcript_id</th>\n",
       "      <th>Chr</th>\n",
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       "      <th>transcript_source</th>\n",
       "      <th>transcript_biotype</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ensembl_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000001</th>\n",
       "      <td>AABR07013255.1</td>\n",
       "      <td>chr2:230660664-230662084</td>\n",
       "      <td>+</td>\n",
       "      <td>pseudogene</td>\n",
       "      <td>ENSRNOT00000055633</td>\n",
       "      <td>2</td>\n",
       "      <td>230660664</td>\n",
       "      <td>230662084</td>\n",
       "      <td>1421</td>\n",
       "      <td>1416</td>\n",
       "      <td>5</td>\n",
       "      <td>ENSRNOG00000000001</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>AABR07013255.1</td>\n",
       "      <td>ensembl</td>\n",
       "      <td>pseudogene</td>\n",
       "      <td>AABR07013255.1-201</td>\n",
       "      <td>ensembl</td>\n",
       "      <td>pseudogene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000007</th>\n",
       "      <td>Gad1</td>\n",
       "      <td>chr3:56861396-56902157</td>\n",
       "      <td>+</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>ENSRNOT00000087712</td>\n",
       "      <td>3</td>\n",
       "      <td>56862691</td>\n",
       "      <td>56902137</td>\n",
       "      <td>39447</td>\n",
       "      <td>3381</td>\n",
       "      <td>36066</td>\n",
       "      <td>ENSRNOG00000000007</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>Gad1</td>\n",
       "      <td>ensembl</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>Gad1-204</td>\n",
       "      <td>ensembl</td>\n",
       "      <td>protein_coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000008</th>\n",
       "      <td>Alx4</td>\n",
       "      <td>chr3:82548959-82585531</td>\n",
       "      <td>+</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>ENSRNOT00000000009</td>\n",
       "      <td>3</td>\n",
       "      <td>82548959</td>\n",
       "      <td>82585531</td>\n",
       "      <td>36573</td>\n",
       "      <td>1747</td>\n",
       "      <td>34826</td>\n",
       "      <td>ENSRNOG00000000008</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>Alx4</td>\n",
       "      <td>ensembl</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>Alx4-201</td>\n",
       "      <td>ensembl</td>\n",
       "      <td>protein_coding</td>\n",
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       "    <tr>\n",
       "      <th>ENSRNOG00000000009</th>\n",
       "      <td>Tmco5b</td>\n",
       "      <td>chr3:104749051-104765436</td>\n",
       "      <td>+</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>ENSRNOT00000000010</td>\n",
       "      <td>3</td>\n",
       "      <td>104749051</td>\n",
       "      <td>104765436</td>\n",
       "      <td>16386</td>\n",
       "      <td>1361</td>\n",
       "      <td>15025</td>\n",
       "      <td>ENSRNOG00000000009</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>Tmco5b</td>\n",
       "      <td>ensembl</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>Tmco5b-201</td>\n",
       "      <td>ensembl</td>\n",
       "      <td>protein_coding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000010</th>\n",
       "      <td>Cbln1</td>\n",
       "      <td>chr19:20607507-20611316</td>\n",
       "      <td>+</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>ENSRNOT00000000011</td>\n",
       "      <td>19</td>\n",
       "      <td>20607507</td>\n",
       "      <td>20611316</td>\n",
       "      <td>3810</td>\n",
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       "      <td>protein_coding</td>\n",
       "      <td>Cbln1-201</td>\n",
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       "      <td>protein_coding</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                         Gene_Name                  Location Sense  \\\n",
       "Ensembl_ID                                                           \n",
       "ENSRNOG00000000001  AABR07013255.1  chr2:230660664-230662084     +   \n",
       "ENSRNOG00000000007            Gad1    chr3:56861396-56902157     +   \n",
       "ENSRNOG00000000008            Alx4    chr3:82548959-82585531     +   \n",
       "ENSRNOG00000000009          Tmco5b  chr3:104749051-104765436     +   \n",
       "ENSRNOG00000000010           Cbln1   chr19:20607507-20611316     +   \n",
       "\n",
       "                      Gene_Biotype       transcript_id Chr      Start  \\\n",
       "Ensembl_ID                                                              \n",
       "ENSRNOG00000000001      pseudogene  ENSRNOT00000055633   2  230660664   \n",
       "ENSRNOG00000000007  protein_coding  ENSRNOT00000087712   3   56862691   \n",
       "ENSRNOG00000000008  protein_coding  ENSRNOT00000000009   3   82548959   \n",
       "ENSRNOG00000000009  protein_coding  ENSRNOT00000000010   3  104749051   \n",
       "ENSRNOG00000000010  protein_coding  ENSRNOT00000000011  19   20607507   \n",
       "\n",
       "                         Stop  Length  Exon Length  Intron Length  \\\n",
       "Ensembl_ID                                                          \n",
       "ENSRNOG00000000001  230662084    1421         1416              5   \n",
       "ENSRNOG00000000007   56902137   39447         3381          36066   \n",
       "ENSRNOG00000000008   82585531   36573         1747          34826   \n",
       "ENSRNOG00000000009  104765436   16386         1361          15025   \n",
       "ENSRNOG00000000010   20611316    3810         2444           1366   \n",
       "\n",
       "                               Gene_ID  gene_version  transcript_version  \\\n",
       "Ensembl_ID                                                                 \n",
       "ENSRNOG00000000001  ENSRNOG00000000001             5                   2   \n",
       "ENSRNOG00000000007  ENSRNOG00000000007             7                   1   \n",
       "ENSRNOG00000000008  ENSRNOG00000000008             7                   5   \n",
       "ENSRNOG00000000009  ENSRNOG00000000009             5                   5   \n",
       "ENSRNOG00000000010  ENSRNOG00000000010             5                   5   \n",
       "\n",
       "                         gene_name gene_source    gene_biotype  \\\n",
       "Ensembl_ID                                                       \n",
       "ENSRNOG00000000001  AABR07013255.1     ensembl      pseudogene   \n",
       "ENSRNOG00000000007            Gad1     ensembl  protein_coding   \n",
       "ENSRNOG00000000008            Alx4     ensembl  protein_coding   \n",
       "ENSRNOG00000000009          Tmco5b     ensembl  protein_coding   \n",
       "ENSRNOG00000000010           Cbln1     ensembl  protein_coding   \n",
       "\n",
       "                       transcript_name transcript_source transcript_biotype  \n",
       "Ensembl_ID                                                                   \n",
       "ENSRNOG00000000001  AABR07013255.1-201           ensembl         pseudogene  \n",
       "ENSRNOG00000000007            Gad1-204           ensembl     protein_coding  \n",
       "ENSRNOG00000000008            Alx4-201           ensembl     protein_coding  \n",
       "ENSRNOG00000000009          Tmco5b-201           ensembl     protein_coding  \n",
       "ENSRNOG00000000010           Cbln1-201           ensembl     protein_coding  "
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Rat_Genomic_Info_File.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "labels = SC_VitC_Expressed_Genes[[\"Change\"]]\n",
    "relevant_genomic_info = Rat_Genomic_Info_File[[\"Gene_Name\", \"Gene_Biotype\", \"Chr\", \"Length\", \"Exon Length\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Change</th>\n",
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       "    <tr>\n",
       "      <th>Ensembl_ID</th>\n",
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       "      <th>ENSRNOG00000000001</th>\n",
       "      <td>Nondifferential</td>\n",
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       "      <th>ENSRNOG00000000008</th>\n",
       "      <td>Upregulated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000017</th>\n",
       "      <td>Nondifferential</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000021</th>\n",
       "      <td>Nondifferential</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000024</th>\n",
       "      <td>Nondifferential</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Change\n",
       "Ensembl_ID                         \n",
       "ENSRNOG00000000001  Nondifferential\n",
       "ENSRNOG00000000008      Upregulated\n",
       "ENSRNOG00000000017  Nondifferential\n",
       "ENSRNOG00000000021  Nondifferential\n",
       "ENSRNOG00000000024  Nondifferential"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gene_Name</th>\n",
       "      <th>Gene_Biotype</th>\n",
       "      <th>Chr</th>\n",
       "      <th>Length</th>\n",
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       "      <th>Ensembl_ID</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000001</th>\n",
       "      <td>AABR07013255.1</td>\n",
       "      <td>pseudogene</td>\n",
       "      <td>2</td>\n",
       "      <td>1421</td>\n",
       "      <td>1416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000007</th>\n",
       "      <td>Gad1</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>3</td>\n",
       "      <td>39447</td>\n",
       "      <td>3381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000008</th>\n",
       "      <td>Alx4</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>3</td>\n",
       "      <td>36573</td>\n",
       "      <td>1747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000009</th>\n",
       "      <td>Tmco5b</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>3</td>\n",
       "      <td>16386</td>\n",
       "      <td>1361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000010</th>\n",
       "      <td>Cbln1</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>19</td>\n",
       "      <td>3810</td>\n",
       "      <td>2444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Gene_Name    Gene_Biotype Chr  Length  Exon Length\n",
       "Ensembl_ID                                                                 \n",
       "ENSRNOG00000000001  AABR07013255.1      pseudogene   2    1421         1416\n",
       "ENSRNOG00000000007            Gad1  protein_coding   3   39447         3381\n",
       "ENSRNOG00000000008            Alx4  protein_coding   3   36573         1747\n",
       "ENSRNOG00000000009          Tmco5b  protein_coding   3   16386         1361\n",
       "ENSRNOG00000000010           Cbln1  protein_coding  19    3810         2444"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "relevant_genomic_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "expressed_genes_info = labels.join(relevant_genomic_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gene_Biotype\n",
       "0                                      44\n",
       "Mt_rRNA                                 2\n",
       "Mt_tRNA                                10\n",
       "TEC                                     3\n",
       "antisense_RNA                          11\n",
       "lincRNA                               208\n",
       "miRNA                                 106\n",
       "misc_RNA                               33\n",
       "processed_pseudogene                  138\n",
       "processed_transcript                   33\n",
       "protein_coding                      11218\n",
       "pseudogene                            169\n",
       "rRNA                                    2\n",
       "ribozyme                                4\n",
       "scaRNA                                  7\n",
       "snRNA                                  77\n",
       "snoRNA                                 77\n",
       "transcribed_processed_pseudogene        2\n",
       "unprocessed_pseudogene                  8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expressed_genes_info.groupby([\"Gene_Biotype\"], as_index = False).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is not surprising that the majority of the expressed genes (11,218 of the 12,152) are protein coding genes. It looks like the next most common is lincRNA (208) and pseudogenes (169). I may join all of the noncoding types together for further anaylsis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "expressed_genes_info = expressed_genes_info.join(summed_counts)\n",
    "expressed_genes_info = expressed_genes_info.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Change</th>\n",
       "      <th>Gene_Name</th>\n",
       "      <th>Gene_Biotype</th>\n",
       "      <th>Chr</th>\n",
       "      <th>Length</th>\n",
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       "      <th>Gene_Body_Upregulated</th>\n",
       "      <th>TSS_Upregulated</th>\n",
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       "    <tr>\n",
       "      <th>Ensembl_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000001</th>\n",
       "      <td>Nondifferential</td>\n",
       "      <td>AABR07013255.1</td>\n",
       "      <td>pseudogene</td>\n",
       "      <td>2</td>\n",
       "      <td>1421</td>\n",
       "      <td>1416</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>ENSRNOG00000000008</th>\n",
       "      <td>Upregulated</td>\n",
       "      <td>Alx4</td>\n",
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       "      <td>3</td>\n",
       "      <td>36573</td>\n",
       "      <td>1747</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>4.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000017</th>\n",
       "      <td>Nondifferential</td>\n",
       "      <td>Steap1</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>4</td>\n",
       "      <td>10589</td>\n",
       "      <td>1280</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>ENSRNOG00000000021</th>\n",
       "      <td>Nondifferential</td>\n",
       "      <td>AABR07061902.1</td>\n",
       "      <td>pseudogene</td>\n",
       "      <td>4</td>\n",
       "      <td>1202</td>\n",
       "      <td>1200</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>ENSRNOG00000000024</th>\n",
       "      <td>Nondifferential</td>\n",
       "      <td>Hebp1</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>4</td>\n",
       "      <td>29515</td>\n",
       "      <td>1062</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Change       Gene_Name    Gene_Biotype Chr  \\\n",
       "Ensembl_ID                                                                \n",
       "ENSRNOG00000000001  Nondifferential  AABR07013255.1      pseudogene   2   \n",
       "ENSRNOG00000000008      Upregulated            Alx4  protein_coding   3   \n",
       "ENSRNOG00000000017  Nondifferential          Steap1  protein_coding   4   \n",
       "ENSRNOG00000000021  Nondifferential  AABR07061902.1      pseudogene   4   \n",
       "ENSRNOG00000000024  Nondifferential           Hebp1  protein_coding   4   \n",
       "\n",
       "                    Length  Exon Length  Downstream_Promoter_Downregulated  \\\n",
       "Ensembl_ID                                                                   \n",
       "ENSRNOG00000000001    1421         1416                                0.0   \n",
       "ENSRNOG00000000008   36573         1747                                0.0   \n",
       "ENSRNOG00000000017   10589         1280                                0.0   \n",
       "ENSRNOG00000000021    1202         1200                                0.0   \n",
       "ENSRNOG00000000024   29515         1062                                0.0   \n",
       "\n",
       "                    Upstream_Promoter_Downregulated  Gene_Body_Downregulated  \\\n",
       "Ensembl_ID                                                                     \n",
       "ENSRNOG00000000001                              1.0                      0.0   \n",
       "ENSRNOG00000000008                              0.0                      0.0   \n",
       "ENSRNOG00000000017                              1.0                      0.0   \n",
       "ENSRNOG00000000021                              0.0                      0.0   \n",
       "ENSRNOG00000000024                              0.0                      1.0   \n",
       "\n",
       "                    TSS_Downregulated  Downstream_Promoter_Nondifferential  \\\n",
       "Ensembl_ID                                                                   \n",
       "ENSRNOG00000000001                0.0                                  1.0   \n",
       "ENSRNOG00000000008                0.0                                  0.0   \n",
       "ENSRNOG00000000017                0.0                                  0.0   \n",
       "ENSRNOG00000000021                0.0                                  0.0   \n",
       "ENSRNOG00000000024                0.0                                  0.0   \n",
       "\n",
       "                    Upstream_Promoter_Nondifferential  \\\n",
       "Ensembl_ID                                              \n",
       "ENSRNOG00000000001                                0.0   \n",
       "ENSRNOG00000000008                                0.0   \n",
       "ENSRNOG00000000017                                0.0   \n",
       "ENSRNOG00000000021                                0.0   \n",
       "ENSRNOG00000000024                                0.0   \n",
       "\n",
       "                    Gene_Body_Nondifferential  TSS_Nondifferential  \\\n",
       "Ensembl_ID                                                           \n",
       "ENSRNOG00000000001                        0.0                  0.0   \n",
       "ENSRNOG00000000008                        4.0                  0.0   \n",
       "ENSRNOG00000000017                        0.0                  0.0   \n",
       "ENSRNOG00000000021                        0.0                  0.0   \n",
       "ENSRNOG00000000024                        5.0                  0.0   \n",
       "\n",
       "                    Downstream_Promoter_Upregulated  \\\n",
       "Ensembl_ID                                            \n",
       "ENSRNOG00000000001                              0.0   \n",
       "ENSRNOG00000000008                              1.0   \n",
       "ENSRNOG00000000017                              0.0   \n",
       "ENSRNOG00000000021                              0.0   \n",
       "ENSRNOG00000000024                              0.0   \n",
       "\n",
       "                    Upstream_Promoter_Upregulated  Gene_Body_Upregulated  \\\n",
       "Ensembl_ID                                                                 \n",
       "ENSRNOG00000000001                            0.0                    0.0   \n",
       "ENSRNOG00000000008                            1.0                    4.0   \n",
       "ENSRNOG00000000017                            0.0                    0.0   \n",
       "ENSRNOG00000000021                            0.0                    0.0   \n",
       "ENSRNOG00000000024                            0.0                    2.0   \n",
       "\n",
       "                    TSS_Upregulated  \n",
       "Ensembl_ID                           \n",
       "ENSRNOG00000000001              0.0  \n",
       "ENSRNOG00000000008              0.0  \n",
       "ENSRNOG00000000017              0.0  \n",
       "ENSRNOG00000000021              0.0  \n",
       "ENSRNOG00000000024              0.0  "
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expressed_genes_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Length</th>\n",
       "      <th>Exon Length</th>\n",
       "      <th>Downstream_Promoter_Downregulated</th>\n",
       "      <th>Upstream_Promoter_Downregulated</th>\n",
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       "      <th>TSS_Upregulated</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.215200e+04</td>\n",
       "      <td>12152.000000</td>\n",
       "      <td>12152.000000</td>\n",
       "      <td>12152.000000</td>\n",
       "      <td>12152.000000</td>\n",
       "      <td>12152.000000</td>\n",
       "      <td>12152.000000</td>\n",
       "      <td>12152.000000</td>\n",
       "      <td>12152.000000</td>\n",
       "      <td>12152.000000</td>\n",
       "      <td>12152.000000</td>\n",
       "      <td>12152.000000</td>\n",
       "      <td>12152.000000</td>\n",
       "      <td>12152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.200248e+04</td>\n",
       "      <td>2590.660303</td>\n",
       "      <td>0.023700</td>\n",
       "      <td>0.028061</td>\n",
       "      <td>0.388660</td>\n",
       "      <td>0.002222</td>\n",
       "      <td>0.135369</td>\n",
       "      <td>0.137837</td>\n",
       "      <td>2.314269</td>\n",
       "      <td>0.015800</td>\n",
       "      <td>0.262755</td>\n",
       "      <td>0.184414</td>\n",
       "      <td>3.384052</td>\n",
       "      <td>0.027403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.477106e+04</td>\n",
       "      <td>1940.736936</td>\n",
       "      <td>0.154267</td>\n",
       "      <td>0.168118</td>\n",
       "      <td>1.120189</td>\n",
       "      <td>0.047086</td>\n",
       "      <td>0.374075</td>\n",
       "      <td>0.377340</td>\n",
       "      <td>5.006940</td>\n",
       "      <td>0.126019</td>\n",
       "      <td>0.522557</td>\n",
       "      <td>0.453929</td>\n",
       "      <td>7.755123</td>\n",
       "      <td>0.166752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.900000e+01</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.241000e+03</td>\n",
       "      <td>1336.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.847350e+04</td>\n",
       "      <td>2148.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.660600e+04</td>\n",
       "      <td>3387.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.232523e+06</td>\n",
       "      <td>25658.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Length   Exon Length  Downstream_Promoter_Downregulated  \\\n",
       "count  1.215200e+04  12152.000000                       12152.000000   \n",
       "mean   4.200248e+04   2590.660303                           0.023700   \n",
       "std    7.477106e+04   1940.736936                           0.154267   \n",
       "min    4.900000e+01     49.000000                           0.000000   \n",
       "25%    6.241000e+03   1336.000000                           0.000000   \n",
       "50%    1.847350e+04   2148.000000                           0.000000   \n",
       "75%    4.660600e+04   3387.000000                           0.000000   \n",
       "max    2.232523e+06  25658.000000                           2.000000   \n",
       "\n",
       "       Upstream_Promoter_Downregulated  Gene_Body_Downregulated  \\\n",
       "count                     12152.000000             12152.000000   \n",
       "mean                          0.028061                 0.388660   \n",
       "std                           0.168118                 1.120189   \n",
       "min                           0.000000                 0.000000   \n",
       "25%                           0.000000                 0.000000   \n",
       "50%                           0.000000                 0.000000   \n",
       "75%                           0.000000                 0.000000   \n",
       "max                           2.000000                30.000000   \n",
       "\n",
       "       TSS_Downregulated  Downstream_Promoter_Nondifferential  \\\n",
       "count       12152.000000                         12152.000000   \n",
       "mean            0.002222                             0.135369   \n",
       "std             0.047086                             0.374075   \n",
       "min             0.000000                             0.000000   \n",
       "25%             0.000000                             0.000000   \n",
       "50%             0.000000                             0.000000   \n",
       "75%             0.000000                             0.000000   \n",
       "max             1.000000                             5.000000   \n",
       "\n",
       "       Upstream_Promoter_Nondifferential  Gene_Body_Nondifferential  \\\n",
       "count                       12152.000000               12152.000000   \n",
       "mean                            0.137837                   2.314269   \n",
       "std                             0.377340                   5.006940   \n",
       "min                             0.000000                   0.000000   \n",
       "25%                             0.000000                   0.000000   \n",
       "50%                             0.000000                   0.000000   \n",
       "75%                             0.000000                   2.000000   \n",
       "max                             4.000000                 107.000000   \n",
       "\n",
       "       TSS_Nondifferential  Downstream_Promoter_Upregulated  \\\n",
       "count         12152.000000                     12152.000000   \n",
       "mean              0.015800                         0.262755   \n",
       "std               0.126019                         0.522557   \n",
       "min               0.000000                         0.000000   \n",
       "25%               0.000000                         0.000000   \n",
       "50%               0.000000                         0.000000   \n",
       "75%               0.000000                         0.000000   \n",
       "max               2.000000                         4.000000   \n",
       "\n",
       "       Upstream_Promoter_Upregulated  Gene_Body_Upregulated  TSS_Upregulated  \n",
       "count                   12152.000000           12152.000000     12152.000000  \n",
       "mean                        0.184414               3.384052         0.027403  \n",
       "std                         0.453929               7.755123         0.166752  \n",
       "min                         0.000000               0.000000         0.000000  \n",
       "25%                         0.000000               0.000000         0.000000  \n",
       "50%                         0.000000               1.000000         0.000000  \n",
       "75%                         0.000000               3.000000         0.000000  \n",
       "max                         5.000000             148.000000         2.000000  "
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expressed_genes_info.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "expressed_genes_info.to_csv(\"Output_Tables_and_Results/SC_VitC_Combined_Cleaned_Data.csv\")\n",
    "expressed_genes_info.to_pickle(\"Output_Tables_and_Results/SC_VitC_Combined_Cleaned_Data.pkl\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the finally cleaned data. I realize that I did not need to have all of the steps for finding outliers to get the table this way, but it was needed to accurately determine the fold change, which might end up being used in this project and will very likely be used in other projects. Additionally, the corrected FPKM values are needed for heatmaps and were needed to determine the expression threshold. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exploration of the data has shown that there are slightly more upregulated genes than downregulated genes in the SC Vitamin C dataset, but this is still only about 1/3 of the expressed genes (12,152). About 90% of the expressed genes are protein coding genes (11,218), even though they make up a relatively small portion of the annotated genome. This is consistent with other RNA-seq studies in Rat, and there are probably more noncoding genes, but they aren't as well annotated in rats. It looks like the highest average peak counts are genebody upregulated (3.38) followed by genebody nondifferential (2.3). We now have many dimensions and this should be helpful for determining if we can classify genes based on the genomic info and hMeDIP peak information (data about DNA methylation). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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