2_plot_isoquant.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5f1b6c1a-fea4-4f3c-b51b-596f02e63294",
   "metadata": {},
   "outputs": [],
   "source": [
    "#plotly libraries\n",
    "import plotly.express as px\n",
    "import plotly.colors as pc\n",
    "import plotly.graph_objects as go\n",
    "import plotly.io as pio\n",
    "\n",
    "\n",
    "#default renderer (svg means very small file size, visibility on github, high quality, but requires sometimes setting height and width)\n",
    "pio.renderers.default = \"svg\"\n",
    "\n",
    "colors = [\"#363b3d\", \"#727b76\", \"#31a240\", \"#f93939\", \"#f79118\", \"#de7b06\", \"#9b308f\", \"#dc759b\"]\n",
    "#additional defaults\n",
    "px.defaults.color_discrete_sequence = [\"rgb(100,100,100)\"] + pc.qualitative.D3\n",
    "px.defaults.width=800\n",
    "px.defaults.height=600\n",
    "\n",
    "#set default template as \"simple_white\" (no background, no grid lines)\n",
    "pio.templates[\"simple_white\"].layout[\"yaxis\"][\"showgrid\"] = True\n",
    "pio.templates.default = \"simple_white\"\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import sklearn.metrics as metrics\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "import pandas as pd\n",
    "from plotly.subplots import make_subplots\n",
    "import plotly.figure_factory as ff\n",
    "\n",
    "colorscale = pc.sequential.Plasma\n",
    "colorscale = [colorscale[0]] + colorscale[3:8]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eaa1008-c430-4b70-8aed-96d2f24947fe",
   "metadata": {},
   "source": [
    "# Plot isoforms per sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9cf4b8c0-a4eb-4708-ab7e-36d7e87155f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "figure_dir = \"figures/isoform_expression\"\n",
    "os.makedirs(figure_dir, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d8427a01-239c-412e-8b60-32d898bb2328",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "isoforms = [\n",
    "    'RT1_unspliced1', \n",
    "    'RT2_unspliced2', \n",
    "    'RT2_Vif2_RT3_Vif1',\n",
    "    'RT2_Vif3_RT3_Vif6', \n",
    "    'RT2_Vif4_RT3_Vif9', \n",
    "    'RT2_Vif5_RT3_Vif8',\n",
    "    'RT2_Vif7', \n",
    "    \n",
    "    'RT2_Vpr3_RT3_Vpr1', \n",
    "    'RT2_Vpr4_RT3_Vpr2',\n",
    "       \n",
    "    'RT2_Env1_RT3_Nef2', \n",
    "    'RT2_Env9_RT3_Nef4', \n",
    "    'RT2_Env2_RT3_Rev1',\n",
    "    'RT2_Env3_RT3_Rev2', \n",
    "    'RT2_Env17_RT3_Nef9', \n",
    "    'RT2_Env5_RT3_Nef3',\n",
    "    'RT2_Env13_RT3_Nef5', \n",
    "    'RT2_Env18_RT3_Rev13', \n",
    "    'RT2_Env10_RT3_Rev7',\n",
    "    'RT2_Env19_RT3_Nef12', \n",
    "    'RT2_Env11_RT3_Rev8', \n",
    "    'RT2_Env14_RT3_Rev10',\n",
    "    'RT2_Env15_RT3_Rev11', \n",
    "    'RT2_Env12_RT3_Rev9', \n",
    "    'RT2_Env6_RT3_Rev4',\n",
    "    'RT2_Env7_RT3_Rev5', \n",
    "    'RT2_Env4_RT3_Rev3', \n",
    "    'RT2_Env8_RT3_Rev6',\n",
    "    'RT2_Env20_RT3_Nef11', \n",
    "    \n",
    "    'RT2_Tat5_RT3_Tat1', \n",
    "    'RT2_Tat6_RT3_Tat2',\n",
    "    'RT2_Tat7_RT3_Tat3', \n",
    "    'RT2_Tat8_RT3_Tat4', \n",
    "    'RT2_Tat12_RT3_Tat9',\n",
    "    'RT3_Tat10', \n",
    "    'RT3_Tat11', \n",
    "    'RT3_Tat13', \n",
    "    'RT3_Tat15', \n",
    "    'RT3_Tat16',\n",
    "    \n",
    "    'RT3_Rev12', \n",
    "    \n",
    "    'RT3_Nef16', \n",
    "    'RT3_Nef15', \n",
    "    'RT3_Nef19', \n",
    "    'RT3_Nef20',\n",
    "    'RT3_Nef22', \n",
    "    'RT3_Nef23', \n",
    "    \n",
    "    'RT3_sORF1', \n",
    "    'RT3_sORF3']\n",
    "\n",
    "\n",
    "isoform_colors_RT2 = {\n",
    "    \" \"  : \"white\",\n",
    "    \"unspliced\" : \"grey\",\n",
    " 'RT1_unspliced1': \"black\",\n",
    " 'RT2_unspliced2': \"black\",\n",
    "\n",
    " 'RT2_Env1_RT3_Nef2': \"rgb(26,82,118)\",\n",
    " 'RT2_Env2_RT3_Rev1': \"rgb(31,97,141)\",\n",
    " 'RT2_Env3_RT3_Rev2': \"rgb(36,113,163)\",\n",
    " 'RT2_Env4_RT3_Rev3': \"rgb(41,128,185)\", \n",
    " 'RT2_Env5_RT3_Nef3': \"rgb(84,153,199)\",\n",
    " 'RT2_Env6_RT3_Rev4': \"rgb(127,179,213)\",\n",
    " 'RT2_Env7_RT3_Rev5': \"rgb(169,204,227)\", \n",
    " 'RT2_Env8_RT3_Rev6': \"rgb(27,79,114)\",\n",
    " 'RT2_Env9_RT3_Nef4': \"rgb(33,97,140)\",\n",
    " 'RT2_Env10_RT3_Rev7': \"rgb(40,116,166)\",\n",
    " 'RT2_Env11_RT3_Rev8': \"rgb(46,134,163)\",\n",
    " 'RT2_Env12_RT3_Rev9': \"rgb(52,152,219)\", \n",
    " 'RT2_Env13_RT3_Nef5': \"rgb(93,173,226)\",\n",
    " 'RT2_Env14_RT3_Rev10': \"rgb(133,193,233)\",\n",
    " 'RT2_Env15_RT3_Rev11': \"rgb(174,214,241)\",\n",
    " 'RT2_Env17_RT3_Nef9': \"rgb(84,153,199)\",\n",
    " 'RT2_Env18_RT3_Rev13': \"rgb(127,179,213)\",\n",
    " 'RT2_Env19_RT3_Nef12': \"rgb(169,204,224)\",\n",
    " 'RT2_Env20_RT3_Nef11': \"rgb(174,214,241)\", \n",
    "    \n",
    " 'RT3_Nef16': \"grey\",\n",
    "    'RT3_Nef15': \"grey\", \n",
    "    'RT3_Nef19': \"grey\", \n",
    "    'RT3_Nef20': \"grey\",\n",
    "    'RT3_Nef22': \"grey\", \n",
    "    'RT3_Nef23': \"grey\", \n",
    "\n",
    "    'RT3_Rev12':\"grey\", \n",
    "\n",
    " 'RT2_Tat5_RT3_Tat1': \"rgb(207,87,161)\",\n",
    " 'RT2_Tat6_RT3_Tat2': \"rgb(217,144,188)\",\n",
    " 'RT2_Tat7_RT3_Tat3': \"rgb(237,206,228)\",\n",
    " 'RT2_Tat8_RT3_Tat4': \"rgb(221,168,200)\",\n",
    " 'RT2_Tat12_RT3_Tat9': \"rgb(229,186,216)\",\n",
    "\n",
    "    'RT3_Tat10': \"grey\", \n",
    "    'RT3_Tat11': \"grey\", \n",
    "    'RT3_Tat13': \"grey\", \n",
    "    'RT3_Tat15': \"grey\", \n",
    "    'RT3_Tat16': \"grey\",\n",
    "    \n",
    " 'RT2_Vif2_RT3_Vif1': \"rgb(96,22,135)\",\n",
    " 'RT2_Vif3_RT3_Vif6': \"rgb(141,74,176)\",\n",
    " 'RT2_Vif4_RT3_Vif9': \"rgb(170,89,212)\",\n",
    " 'RT2_Vif5_RT3_Vif8': \"rgb(204,108,255)\",\n",
    "    'RT2_Vif7':\"rgb(142,68,173)\",\n",
    "    \n",
    " 'RT2_Vpr3_RT3_Vpr1': \"rgb(168,30,45)\",\n",
    " 'RT2_Vpr4_RT3_Vpr2': \"rgb(192,98,106)\",\n",
    "\n",
    " 'RT3_sORF1': \"rgb(177,153,109)\",\n",
    " 'RT3_sORF3': \"rgb(157,138,109)\",\n",
    "}\n",
    "\n",
    "\n",
    "isoform_colors_RT3 = {\n",
    " 'RT1_unspliced1': \"black\",\n",
    " 'RT2_unspliced2': \"black\",\n",
    "\n",
    "    \"Nef\" : \"green\",\n",
    " 'RT2_Env1_RT3_Nef2': \"rgb(17,120,100)\",\n",
    " 'RT2_Env5_RT3_Nef3': \"rgb(20,143,119)\",\n",
    " 'RT2_Env9_RT3_Nef4': \"rgb(23,165,137)\",\n",
    " 'RT2_Env13_RT3_Nef5': \"rgb(26,188,156)\",\n",
    " 'RT2_Env17_RT3_Nef9': \"rgb(72,201,176)\",\n",
    " 'RT2_Env20_RT3_Nef11': \"rgb(118,215,196)\", \n",
    " 'RT2_Env19_RT3_Nef12': \"rgb(163,228,215)\",\n",
    " 'RT3_Nef15': \"rgb(29,131,72)\", \n",
    " 'RT3_Nef16': \"rgb(35,155,86)\",\n",
    " 'RT3_Nef19': \"rgb(40,180,99)\", \n",
    " 'RT3_Nef20': \"rgb(46,204,113)\",\n",
    " 'RT3_Nef22': \"rgb(88,214,141)\", \n",
    " 'RT3_Nef23': \"rgb(130,224,170)\", \n",
    "\n",
    " 'RT2_Env2_RT3_Rev1': \"rgb(147,81,22)\",\n",
    " 'RT2_Env3_RT3_Rev2': \"rgb(175,96,26)\",\n",
    " 'RT2_Env4_RT3_Rev3': \"rgb(202,111,30)\",\n",
    " 'RT2_Env6_RT3_Rev4': \"rgb(230,126,34)\",\n",
    " 'RT2_Env7_RT3_Rev5': \"rgb(235,152,78)\",\n",
    " 'RT2_Env8_RT3_Rev6': \"rgb(240,178,122)\",\n",
    " 'RT2_Env10_RT3_Rev7': \"rgb(185,119,14)\",\n",
    " 'RT2_Env11_RT3_Rev8': \"rgb(214,137,16)\",\n",
    " 'RT2_Env12_RT3_Rev9': \"rgb(243,156,18)\",\n",
    " 'RT2_Env14_RT3_Rev10': \"rgb(245,176,65)\",\n",
    " 'RT2_Env15_RT3_Rev11': \"rgb(248,180,113)\",\n",
    " 'RT3_Rev12': \"rgb(246,221,204)\", \n",
    " 'RT2_Env18_RT3_Rev13': \"rgb(250,215,160)\",\n",
    " \n",
    "    \n",
    " 'RT2_Tat5_RT3_Tat1': \"rgb(207,87,161)\",\n",
    " 'RT2_Tat6_RT3_Tat2': \"rgb(217,144,188)\",\n",
    " 'RT2_Tat7_RT3_Tat3': \"rgb(237,206,228)\",\n",
    " 'RT2_Tat8_RT3_Tat4': \"rgb(221,168,200)\",\n",
    " 'RT2_Tat12_RT3_Tat9': \"rgb(229,186,216)\",\n",
    "\n",
    " 'RT3_Tat10': \"rgb(240,98,146)\", \n",
    " 'RT3_Tat11': \"rgb(244,143,177)\", \n",
    " 'RT3_Tat13': \"rgb(248,187,208)\", \n",
    " 'RT3_Tat15': \"rgb(252,200,215)\", \n",
    " 'RT3_Tat16': \"rgb(252,228,236)\",\n",
    "    \n",
    " 'RT2_Vif2_RT3_Vif1': \"rgb(96,22,135)\",\n",
    " 'RT2_Vif3_RT3_Vif6': \"rgb(141,74,176)\",\n",
    " 'RT2_Vif4_RT3_Vif9': \"rgb(170,89,212)\",\n",
    " 'RT2_Vif5_RT3_Vif8': \"rgb(204,108,255)\",\n",
    " 'RT2_Vif7' : \"grey\",\n",
    " 'RT2_Vpr3_RT3_Vpr1': \"rgb(168,30,45)\",\n",
    " 'RT2_Vpr4_RT3_Vpr2': \"rgb(192,98,106)\",\n",
    "\n",
    " 'RT3_sORF1': \"rgb(177,153,109)\",\n",
    " 'RT3_sORF3': \"rgb(157,138,109)\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ea5cf602-346a-46fb-bf2e-95a7ce21b31e",
   "metadata": {},
   "outputs": [],
   "source": [
    "order_dict = {\n",
    "    \"DMS_conc\" : [f\"{i}mM\" for i in [0,8,17,34,57,85]],\n",
    "    \"isoform\" : isoforms\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ca8db971-b30a-46fb-a7e3-c426cedd4fb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_folder = f\"{os.getcwd()}/data\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36b6238e-e4b7-42d8-aa90-6a1faba2b2cd",
   "metadata": {},
   "source": [
    "# Get sample names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "25f7f4bb-e2a5-480b-8a31-1f2086046d45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Nanopore_HIV1_Rep1_RT-US_0mM_cell',\n",
       "       'Nanopore_HIV1_Rep1_RT-US_57mM_cell',\n",
       "       'Nanopore_HIV1_Rep1_RT-US_85mM_cell',\n",
       "       'Nanopore_HIV1_Rep2_RT-US_0mM_cell',\n",
       "       'Nanopore_HIV1_Rep2_RT-US_57mM_cell',\n",
       "       'Nanopore_HIV1_Rep2_RT-US_85mM_cell',\n",
       "       'Nanopore_HIV1_Rep1_RT-FS_0mM_cell',\n",
       "       'Nanopore_HIV1_Rep1_RT-FS_57mM_cell',\n",
       "       'Nanopore_HIV1_Rep1_RT-FS_85mM_cell',\n",
       "       'Nanopore_HIV1_Rep2_RT-FS_0mM_cell',\n",
       "       'Nanopore_HIV1_Rep2_RT-FS_57mM_cell',\n",
       "       'Nanopore_HIV1_Rep2_RT-FS_85mM_cell'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "samplesheet = pd.read_excel(\"Samplesheet_example.xlsx\", engine=\"openpyxl\")\n",
    "samples = samplesheet[\"sample\"].values\n",
    "samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "24aed74a-719b-4d1c-815e-10db780fe6dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# adjust this function to your naming scheme. Splitting the name into its separate parts allows for straightforward plotting of comparisons\n",
    "\n",
    "def split_unique_name(df, unique_name_column = \"unique_name\"):\n",
    "    df[\"replicate\"] = df[unique_name_column].apply(lambda x: x.split(\"_\")[2])\n",
    "    df[\"RT\"] = df[unique_name_column].apply(lambda x: x.split(\"_\")[3])\n",
    "    df[\"DMS_conc\"] = df[unique_name_column].apply(lambda x: x.split(\"_\")[4])\n",
    "    df[\"rep_and_DMS\"] = df[\"replicate\"] + \"_\" + df[\"DMS_conc\"]\n",
    "    df[\"RT_and_DMS\"] = df[\"RT\"] + \"_\" + df[\"DMS_conc\"]\n",
    "    df[\"DMS_conc\"] = df[\"DMS_conc\"].astype(\"category\")\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a634440-86e1-410d-ab99-d0a9c830d395",
   "metadata": {},
   "source": [
    "# Read in isoquant output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5bc0c1a1-d55a-42a1-b916-810418fe4401",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading in from isoquant output\n",
    "tmp_data = []\n",
    "for sample in samples:\n",
    "    tmp_df = pd.read_csv(f\"{data_folder}/isoquant/{sample}/{sample}.read_assignments.tsv\", sep=\"\\t\", skiprows=2)\n",
    "    tmp_df[\"sample\"] = sample\n",
    "    tmp_data.append(tmp_df)\n",
    "read_assignment_df = pd.concat(tmp_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e50967af-6117-4761-b579-39bb0021807c",
   "metadata": {},
   "outputs": [],
   "source": [
    "read_assignment_df = read_assignment_df.rename(columns={\"#read_id\" :\"read_id\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "45b88083-a25b-4e5c-91c4-9e6c8636a84f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To decrease time it takes to load data you can store the dataframe on disk\n",
    "\n",
    "#this is the dataframe that is used in notebook 1\n",
    "read_assignment_df.to_pickle(f\"{data_folder}/isoquant/read_assignments.pickle.gz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f9b24816-9d9a-4e47-82b8-bc3a10e5496c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# if already stored on disk\n",
    "read_assignment_df = pd.read_pickle(f\"{data_folder}/isoquant/read_assignments.pickle.gz\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd82cbb5-e4e4-4811-9340-29630b9a8abb",
   "metadata": {},
   "source": [
    "# Take per read information to calculate assignment types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "a534e1b7-b86d-4ec2-b6f9-80f700c8d1ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "order = {\"assignment_type\" : [\"unique\", \"unique_minor_difference\", \"ambiguous\", \"inconsistent\", \"noninformative\"],\n",
    "         \"DMS_conc\" : [f\"{i}mM\" for i in [0,8,17,34,57,85]]}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42dc7524-20da-4977-a13e-73f33d03060b",
   "metadata": {},
   "source": [
    "## Plot individual read assignments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "afa1281d-1608-4bbc-9e1a-55d72ce67c78",
   "metadata": {},
   "outputs": [],
   "source": [
    "# it is important here to drop duplicates of read_id + assignment_type, because ambigously assigned reads appear multiple times\n",
    "\n",
    "assignment_counts = read_assignment_df[[\"read_id\", \"assignment_type\", \"sample\"]].drop_duplicates().groupby(\"sample\")[\"assignment_type\"].value_counts().reset_index(name=\"count\")\n",
    "assignment_counts = split_unique_name(assignment_counts, unique_name_column = \"sample\")\n",
    "total_count_dict = assignment_counts.groupby(\"sample\")[\"count\"].sum().to_dict()\n",
    "\n",
    "assignment_counts[\"percent\"] = assignment_counts.apply(lambda x: 100*x[\"count\"]/(total_count_dict[x[\"sample\"]]), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "628b2f95-37fc-41d7-b8a5-81ea589eb4c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs(\"figures/isoform_expression\", exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "8ccb614a-5c58-440c-a7ff-43eaf5c13aa0",
   "metadata": {},
   "outputs": [
    {
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.bar(assignment_counts, x=\"RT_and_DMS\", y=\"count\", facet_col=\"replicate\", color=\"assignment_type\", barmode=\"stack\", category_orders=order)\n",
    "fig.update_yaxes(dtick=50000, showgrid=True)\n",
    "fig.update_traces(width=0.8, marker_line_width=1, marker_line_color=\"black\")\n",
    "fig.update_layout(width=600, height=400)\n",
    "fig.write_image(f\"{figure_dir}/isoquant_classification_absolute.svg\")\n",
    "fig.show(renderer=\"svg\", width=600,height=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "6327cc10-9076-4989-a88f-6469732b0eb2",
   "metadata": {},
   "outputs": [
    {
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.bar(assignment_counts, x=\"RT_and_DMS\", y=\"percent\", facet_col=\"replicate\", color=\"assignment_type\", barmode=\"stack\", category_orders=order)\n",
    "fig.update_yaxes(dtick=20, showgrid=True)\n",
    "fig.update_layout(width=600, height=400)\n",
    "fig.update_traces(width=0.8, marker_line_width=0.5, marker_line_color=\"black\")\n",
    "fig.write_image(f\"{figure_dir}/isoquant_classification_percent.svg\")\n",
    "fig.show(renderer=\"svg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f70fd922-dad5-4da6-9f5a-19574826c20f",
   "metadata": {},
   "source": [
    "# Relative isoform expression for uniquely assigned isoforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "c6e46cfa-d41b-47bc-8a57-f806ba60ea16",
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_assignment_df = read_assignment_df[read_assignment_df[\"assignment_type\"].isin([\"unique\", \"unique_minor_difference\"])]\n",
    "isoform_count_df = unique_assignment_df.groupby(\"sample\")[\"isoform_id\"].value_counts().reset_index(name=\"count\")\n",
    "isoform_count_df = split_unique_name(isoform_count_df, unique_name_column = \"sample\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "2bdc11c5-a259-4f5b-8941-beb5770665b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This export is critical for use in the workflow because it is used to identify isoforms that have a minimum amount of reads assigned. \n",
    "\n",
    "isoform_count_df.to_pickle(f\"{data_folder}/isoquant/read_counts.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "f13f34f7-aa64-4cc5-8318-3778aa846aee",
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "order_dict[\"isoform_id\"] = list(isoform_colors_RT3.keys())\n",
    "fig = px.bar(isoform_count_df, x=\"RT_and_DMS\",facet_col=\"replicate\",  y=\"count\", color=\"isoform_id\", category_orders=order_dict, color_discrete_map = isoform_colors_RT3)\n",
    "fig.update_layout(width=600, height=400)\n",
    "fig.update_traces(width=0.8, marker_line_width=0.5, marker_line_color=\"black\")\n",
    "fig.write_image(f\"{figure_dir}/isoquant_isoform_count.svg\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "e12a2bf8-3036-4372-a253-7a26b3fbe170",
   "metadata": {},
   "outputs": [
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        },
        "width": 600,
        "xaxis": {
         "anchor": "y",
         "autorange": true,
         "domain": [
          0,
          0.49
         ],
         "range": [
          -0.5,
          5.5
         ],
         "title": {
          "text": "RT_and_DMS"
         },
         "type": "category"
        },
        "xaxis2": {
         "anchor": "y2",
         "autorange": true,
         "domain": [
          0.51,
          1
         ],
         "matches": "x",
         "range": [
          -0.5,
          5.5
         ],
         "title": {
          "text": "RT_and_DMS"
         },
         "type": "category"
        },
        "yaxis": {
         "anchor": "x",
         "autorange": true,
         "domain": [
          0,
          1
         ],
         "range": [
          0,
          105.26315789473686
         ],
         "title": {
          "text": "sum of count (normalized as percent)"
         },
         "type": "linear"
        },
        "yaxis2": {
         "anchor": "x2",
         "autorange": true,
         "domain": [
          0,
          1
         ],
         "matches": "y",
         "range": [
          0,
          105.26315789473686
         ],
         "showticklabels": false,
         "type": "linear"
        }
       }
      },
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.histogram(isoform_count_df, x=\"RT_and_DMS\", facet_col=\"replicate\", y=\"count\", color=\"isoform_id\", barnorm=\"percent\", category_orders = order_dict, color_discrete_map = isoform_colors_RT3)\n",
    "fig.update_layout(width=600, height=400)\n",
    "fig.update_traces(marker_line_width=0.5, marker_line_color=\"black\")\n",
    "fig.write_image(f\"{figure_dir}/isoquant_isoform_percent.svg\")\n",
    "fig.show(renderer=\"jupyterlab\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98ef4dcd-6154-420c-9c88-a368adc66902",
   "metadata": {},
   "source": [
    "# Isoform expression correlation between replicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "8da8503a-c20d-4ba3-9279-394a31265e63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/vol/projects/pbohn/AnSo_DMS_MaP/_for_publication/_protocol_example_run'"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "import numpy as np\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "2841c2af-18cc-4aef-997e-d95c80af487b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Nanopore_HIV1_Rep1_RT-FS_0mM_cell': 122604,\n",
       " 'Nanopore_HIV1_Rep1_RT-FS_57mM_cell': 167638,\n",
       " 'Nanopore_HIV1_Rep1_RT-FS_85mM_cell': 182156,\n",
       " 'Nanopore_HIV1_Rep1_RT-US_0mM_cell': 34346,\n",
       " 'Nanopore_HIV1_Rep1_RT-US_57mM_cell': 46426,\n",
       " 'Nanopore_HIV1_Rep1_RT-US_85mM_cell': 71399,\n",
       " 'Nanopore_HIV1_Rep2_RT-FS_0mM_cell': 93618,\n",
       " 'Nanopore_HIV1_Rep2_RT-FS_57mM_cell': 164837,\n",
       " 'Nanopore_HIV1_Rep2_RT-FS_85mM_cell': 192813,\n",
       " 'Nanopore_HIV1_Rep2_RT-US_0mM_cell': 93885,\n",
       " 'Nanopore_HIV1_Rep2_RT-US_57mM_cell': 63779,\n",
       " 'Nanopore_HIV1_Rep2_RT-US_85mM_cell': 88001}"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "per_sample_counts = isoform_count_df.groupby(\"sample\").agg({\"count\":\"sum\"}).to_dict()[\"count\"]\n",
    "\n",
    "isoform_count_df[\"percent\"] = 100* isoform_count_df.apply(lambda x: x[\"count\"] / per_sample_counts[x[\"sample\"]], axis=1)\n",
    "\n",
    "pivot_df = pd.pivot(isoform_count_df, index = [\"RT_and_DMS\", \"isoform_id\"], columns = \"replicate\", values = \"percent\").reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "8c91d571-cda4-4b33-a1c9-2df09da1efc9",
   "metadata": {},
   "outputs": [
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.scatter(pivot_df, x=\"Rep1\", y=\"Rep2\", trendline=\"ols\")\n",
    "fig.update_layout(width=500, height=400)\n",
    "fig.update_xaxes(dtick=10)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "8d035484-daa7-4e34-80a7-4b3788556ec0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9966137165677839\n"
     ]
    }
   ],
   "source": [
    "results = px.get_trendline_results(fig)\n",
    "\n",
    "print(results.iloc[0][\"px_fit_results\"].rsquared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "8ace89f6-0481-41f7-beff-a8e9c09c0511",
   "metadata": {},
   "outputs": [
    {
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font-size: 12px; fill: rgb(36, 36, 36); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(0,166.2)\">60</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"71.6\" y=\"4.199999999999999\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(36, 36, 36); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(0,120.52000000000001)\">80</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"71.6\" y=\"4.199999999999999\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(36, 36, 36); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(0,74.83)\">100</text></g></g><g class=\"overaxes-above\"/></g></g><g class=\"polarlayer\"/><g class=\"smithlayer\"/><g class=\"ternarylayer\"/><g class=\"geolayer\"/><g class=\"funnelarealayer\"/><g class=\"pielayer\"/><g class=\"iciclelayer\"/><g class=\"treemaplayer\"/><g class=\"sunburstlayer\"/><g class=\"glimages\"/><defs id=\"topdefs-6feab1\"><g class=\"clips\"/><clipPath id=\"legend6feab1\"><rect width=\"121\" height=\"143\" x=\"0\" y=\"0\"/></clipPath></defs><g class=\"layer-above\"><g class=\"imagelayer\"/><g class=\"shapelayer\"/></g><g class=\"infolayer\"><g class=\"legend\" pointer-events=\"all\" transform=\"translate(366.62,60)\"><rect class=\"bg\" shape-rendering=\"crispEdges\" style=\"stroke: rgb(68, 68, 68); stroke-opacity: 1; fill: rgb(255, 255, 255); fill-opacity: 1; stroke-width: 0px;\" width=\"121\" height=\"143\" x=\"0\" y=\"0\"/><g class=\"scrollbox\" transform=\"\" clip-path=\"url(#legend6feab1)\"><text class=\"legendtitletext\" text-anchor=\"start\" x=\"2\" y=\"18.2\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(36, 36, 36); fill-opacity: 1; white-space: pre;\">RT_and_DMS</text><g class=\"groups\" transform=\"\"><g class=\"traces\" transform=\"translate(0,32.7)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(36, 36, 36); fill-opacity: 1; white-space: pre;\">RT-FS_0mM</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"/><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; 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fill-opacity: 0;\"/></g></g><g class=\"groups\" transform=\"\"><g class=\"traces\" transform=\"translate(0,70.7)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(36, 36, 36); fill-opacity: 1; white-space: pre;\">RT-FS_85mM</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"/><g class=\"legendsymbols\"><g class=\"legendpoints\"><path class=\"scatterpts\" transform=\"translate(20,0)\" d=\"M3,0A3,3 0 1,1 0,-3A3,3 0 0,1 3,0Z\" style=\"opacity: 1; stroke-width: 0px; fill: rgb(255, 127, 14); fill-opacity: 1;\"/></g></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"115.8125\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g></g><g class=\"groups\" transform=\"\"><g class=\"traces\" transform=\"translate(0,89.7)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; 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fill-opacity: 0;\"/></g></g></g><rect class=\"scrollbar\" rx=\"20\" ry=\"3\" width=\"0\" height=\"0\" style=\"fill: rgb(128, 139, 164); fill-opacity: 1;\" x=\"0\" y=\"0\"/></g><g class=\"g-gtitle\"/><g class=\"g-xtitle\"><text class=\"xtitle\" x=\"220.5\" y=\"368.20625\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(36, 36, 36); opacity: 1; font-weight: normal; white-space: pre;\">Rep1</text></g><g class=\"g-ytitle\"><text class=\"ytitle\" transform=\"rotate(-90,26.778125000000003,190)\" x=\"26.778125000000003\" y=\"190\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(36, 36, 36); opacity: 1; font-weight: normal; white-space: pre;\">Rep2</text></g></g></svg>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.scatter(pivot_df, x=\"Rep1\", y=\"Rep2\", color=\"RT_and_DMS\", trendline=\"ols\")\n",
    "fig.update_layout(width=500, height=400)\n",
    "fig.update_xaxes(dtick=10)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "8e54d808-392e-4033-869c-665e83f6100c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RT-FS_0mM\n",
      "0.933714990672154\n",
      "RT-FS_57mM\n",
      "0.9745246537999859\n",
      "RT-FS_85mM\n",
      "0.9618634860276496\n",
      "RT-US_0mM\n",
      "0.9999903427297899\n",
      "RT-US_57mM\n",
      "0.9999876518365786\n",
      "RT-US_85mM\n",
      "0.9999981618659899\n"
     ]
    }
   ],
   "source": [
    "results = px.get_trendline_results(fig)\n",
    "for _, row in results.iterrows():\n",
    "    print(row[\"RT_and_DMS\"])\n",
    "    print(row[\"px_fit_results\"].rsquared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "e787702f-c435-41c0-b22e-51f6293bba93",
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.scatter(pivot_df, x=\"Rep1\", y=\"Rep2\", color=\"RT_and_DMS\", log_x=True, log_y=True)\n",
    "fig.update_xaxes(range=[-3, 2.1], dtick=[-2,2])\n",
    "fig.update_yaxes(range=[-3, 2.1], dtick=[-2,2])\n",
    "\n",
    "fig.update_layout(width=500, height=400)\n",
    "fig.show()"
   ]
  }
 ],
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