Figure_3_Venn_diagrams.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ingenos\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib_venn\n",
    "from matplotlib_venn import venn3_unweighted, venn3_circles\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### set base directories for each data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "amp_base = \"/afs/crc.nd.edu/group/BesanskyNGS/data05/comp_karyo/data/amplicon_sequencing\"\n",
    "oa_base = \"/afs/crc.nd.edu/group/BesanskyNGS/data05/comp_karyo/data/open_array\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### read in lists of SNPs, as saved during the analysis of each data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>assay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>2L_20674783_a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>2L_20726658_a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a</td>\n",
       "      <td>2L_20813877_a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a</td>\n",
       "      <td>2L_20995428_a</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label          assay\n",
       "0   NaN            NaN\n",
       "1     a  2L_20674783_a\n",
       "2     a  2L_20726658_a\n",
       "3     a  2L_20813877_a\n",
       "4     a  2L_20995428_a"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amp_SNPs = pd.read_csv(amp_base + \"/amp_SNP_list.041320.csv\")\n",
    "\n",
    "amp_SNPs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>assay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>2L_20674783_a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>2L_20726658_a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a</td>\n",
       "      <td>2L_20813877_a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a</td>\n",
       "      <td>2L_20995428_a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>a</td>\n",
       "      <td>2L_21120275_a</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label          assay\n",
       "1     a  2L_20674783_a\n",
       "2     a  2L_20726658_a\n",
       "3     a  2L_20813877_a\n",
       "4     a  2L_20995428_a\n",
       "5     a  2L_21120275_a"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amp_SNPs = amp_SNPs[1:]\n",
    "\n",
    "amp_SNPs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### clean and reformat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>assay</th>\n",
       "      <th>chrom</th>\n",
       "      <th>pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>2L_20674783_a</td>\n",
       "      <td>2L</td>\n",
       "      <td>20674783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>2L_20726658_a</td>\n",
       "      <td>2L</td>\n",
       "      <td>20726658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a</td>\n",
       "      <td>2L_20813877_a</td>\n",
       "      <td>2L</td>\n",
       "      <td>20813877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a</td>\n",
       "      <td>2L_20995428_a</td>\n",
       "      <td>2L</td>\n",
       "      <td>20995428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>a</td>\n",
       "      <td>2L_21120275_a</td>\n",
       "      <td>2L</td>\n",
       "      <td>21120275</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label          assay chrom       pos\n",
       "1     a  2L_20674783_a    2L  20674783\n",
       "2     a  2L_20726658_a    2L  20726658\n",
       "3     a  2L_20813877_a    2L  20813877\n",
       "4     a  2L_20995428_a    2L  20995428\n",
       "5     a  2L_21120275_a    2L  21120275"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amp_SNPs[\"chrom\"] = amp_SNPs[\"assay\"].str.split(\"_\", expand=True)[0]\n",
    "amp_SNPs[\"pos\"] = amp_SNPs[\"assay\"].str.split(\"_\", expand=True)[1]\n",
    "amp_SNPs[\"pos\"] = amp_SNPs[\"pos\"].astype(int)\n",
    "\n",
    "amp_SNPs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>20546192.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>20731296.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a</td>\n",
       "      <td>20816241.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a</td>\n",
       "      <td>21590819.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label         pos\n",
       "0   NaN         NaN\n",
       "1     a  20546192.0\n",
       "2     a  20731296.0\n",
       "3     a  20816241.0\n",
       "4     a  21590819.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oa_SNPs = pd.read_csv(oa_base + \"/oa_SNP_list.041320.csv\")\n",
    "\n",
    "oa_SNPs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>label</th>\n",
       "      <th>pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>20546192</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>20731296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a</td>\n",
       "      <td>20816241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a</td>\n",
       "      <td>21590819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>a</td>\n",
       "      <td>21687929</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label       pos\n",
       "1     a  20546192\n",
       "2     a  20731296\n",
       "3     a  20816241\n",
       "4     a  21590819\n",
       "5     a  21687929"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oa_SNPs = oa_SNPs[1:]\n",
    "oa_SNPs[\"pos\"] = oa_SNPs[\"pos\"].astype(int)\n",
    "\n",
    "oa_SNPs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### also read in the targets for the in silico approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "comp_base = \"/afs/crc.nd.edu/group/BesanskyNGS/data05/comp_karyo/data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "a_top = pd.read_csv(comp_base + \"/results/2La/comp/predictive_SNPs_train_set_0995_110918.tsv\",\n",
    "               sep = \"\\t\", header=None)\n",
    "\n",
    "j_top = pd.read_csv(comp_base + \"/results/2Rj/comp/predictive_SNPs_train_set_08_110918.tsv\",\n",
    "               sep = \"\\t\", header=None)\n",
    "\n",
    "b_top = pd.read_csv(comp_base + \"/results/2Rb/comp/predictive_SNPs_train_set_08_110918.tsv\",\n",
    "               sep = \"\\t\", header=None)\n",
    "\n",
    "c_col_top =\\\n",
    "pd.read_csv(comp_base + \"/results/2Rc/comp/col_predictive_SNPs_train_set_08_031919.tsv\",\n",
    "                       sep = \"\\t\", header=None)\n",
    "\n",
    "c_gam_top =\\\n",
    "pd.read_csv(comp_base + \"/results/2Rc/comp/gam_ss_predictive_SNPs_train_set_08_031919.tsv\",\n",
    "           sep = \"\\t\", header=None)\n",
    "\n",
    "u_top = pd.read_csv(comp_base + \"/results/2Ru/comp/predictive_SNPs_train_set_08_110918.tsv\",\n",
    "               sep = \"\\t\", header=None)\n",
    "\n",
    "d_top = pd.read_csv(comp_base + \"/results/2Rd/comp/predictive_SNPs_train_set_08_052619.tsv\",\n",
    "               sep = \"\\t\", header=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### reformat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>chrom</th>\n",
       "      <th>pos</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2L</td>\n",
       "      <td>20528221</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2L</td>\n",
       "      <td>20542311</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2L</td>\n",
       "      <td>20542372</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2L</td>\n",
       "      <td>20542904</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2L</td>\n",
       "      <td>20546192</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  chrom       pos label\n",
       "0    2L  20528221     a\n",
       "1    2L  20542311     a\n",
       "2    2L  20542372     a\n",
       "3    2L  20542904     a\n",
       "4    2L  20546192     a"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_top[\"chrom\"] = \"2L\"\n",
    "a_top[\"label\"] = \"a\"\n",
    "\n",
    "j_top[\"chrom\"] = \"2R\"\n",
    "j_top[\"label\"] = \"j\"\n",
    "\n",
    "b_top[\"chrom\"] = \"2R\"\n",
    "b_top[\"label\"] = \"b\"\n",
    "\n",
    "c_col_top[\"chrom\"] = \"2R\"\n",
    "c_col_top[\"label\"] = \"c_col\"\n",
    "\n",
    "c_gam_top[\"chrom\"] = \"2R\"\n",
    "c_gam_top[\"label\"] = \"c_gam\"\n",
    "\n",
    "u_top[\"chrom\"] = \"2R\"\n",
    "u_top[\"label\"] = \"u\"\n",
    "\n",
    "d_top[\"chrom\"] = \"2R\"\n",
    "d_top[\"label\"] = \"d\"\n",
    "\n",
    "comp = pd.concat([a_top, j_top, b_top, c_col_top, c_gam_top, u_top, d_top])\n",
    "\n",
    "comp.rename(columns = {0 : \"pos\"}, inplace=True)\n",
    "\n",
    "comp = comp[[\"chrom\",\"pos\",\"label\"]]\n",
    "\n",
    "comp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### check that labels are in same order for all three data sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['a', 'j', 'b', 'c_col', 'c_gam', 'u', 'd'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp[\"label\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True,  True])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp[\"label\"].unique() == amp_SNPs[\"label\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True,  True])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amp_SNPs[\"label\"].unique() == oa_SNPs[\"label\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "inversions_dummy = [\"a\", \"dummy\", \"j\", \"b\", \"c_col\", \"c_gam\", \"d\", \"u\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### create Venn diagrams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1152 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=4, ncols=2, figsize=(8,16))\n",
    "\n",
    "for i, inversion in enumerate(inversions_dummy):\n",
    "    \n",
    "    if inversion == \"dummy\":\n",
    "        \n",
    "        axes.flatten()[i].axis('off')\n",
    "        \n",
    "    else:\n",
    "    \n",
    "        v1 = venn3_unweighted(subsets=(set(amp_SNPs.loc[amp_SNPs[\"label\"] == inversion, \"pos\"]), \n",
    "                        set(oa_SNPs.loc[oa_SNPs[\"label\"] == inversion, \"pos\"]),\n",
    "                       set(comp.loc[comp[\"label\"] == inversion, \"pos\"])), \n",
    "              set_labels = (\"GT-seq\", \"OA\", \"In silico\"), alpha = 0.4, \n",
    "                   ax=axes.flatten()[i])\n",
    "\n",
    "        for label in v1.set_labels: label.set_fontsize(15)\n",
    "        for label in v1.subset_labels: \n",
    "            if label is not None:\n",
    "                label.set_fontsize(15)\n",
    "\n",
    "        axes.flatten()[i].set_title(inversion, fontsize=25, y = 0.95)\n",
    "\n",
    "plt.tight_layout()\n",
    "            \n",
    "outString = ingenos.make_date_stamp(\n",
    "    amp_base + \"/results/three_methods_snp_overlap_a\")\n",
    "\n",
    "#plt.savefig(outString);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}