plot_enrichments_pairwise_monocyte_corces_microglia.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "rcParams.update({'figure.autolayout': True})\n",
    "rcParams['svg.fonttype'] = 'none'\n",
    "rcParams['font.size']=10\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.patches as mpatches\n",
    "from statsmodels.stats.multitest import multipletests\n",
    "from seaborn import pointplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "inFile = '/projects/pfenninggroup/machineLearningForComputationalBiology/eramamur_stuff/gwas_enrichments/monocyte_corces_microglia/kunkle_enrichments.cell_type_results.txt'\n",
    "kunkle_data = pd.read_csv(inFile, sep=\"\\t\", header=0).sort_values(by=\"Name\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "inFile = '/projects/pfenninggroup/machineLearningForComputationalBiology/eramamur_stuff/gwas_enrichments/monocyte_corces_microglia/jansen_enrichments.cell_type_results.txt'\n",
    "jansen_data = pd.read_csv(inFile, sep=\"\\t\", header=0).sort_values(by=\"Name\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Name</th>\n",
       "      <th>Coefficient</th>\n",
       "      <th>Coefficient_std_error</th>\n",
       "      <th>Coefficient_P_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>corces_microglia</td>\n",
       "      <td>3.248359e-08</td>\n",
       "      <td>3.665399e-08</td>\n",
       "      <td>0.187749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>monocyte_dnase</td>\n",
       "      <td>-2.007625e-09</td>\n",
       "      <td>2.509152e-08</td>\n",
       "      <td>0.531886</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name   Coefficient  Coefficient_std_error  Coefficient_P_value\n",
       "0  corces_microglia  3.248359e-08           3.665399e-08             0.187749\n",
       "1    monocyte_dnase -2.007625e-09           2.509152e-08             0.531886"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jansen_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Name</th>\n",
       "      <th>Coefficient</th>\n",
       "      <th>Coefficient_std_error</th>\n",
       "      <th>Coefficient_P_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>corces_microglia</td>\n",
       "      <td>-7.381441e-08</td>\n",
       "      <td>1.230802e-07</td>\n",
       "      <td>0.725656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>monocyte_dnase</td>\n",
       "      <td>1.446313e-07</td>\n",
       "      <td>1.238242e-07</td>\n",
       "      <td>0.121396</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name   Coefficient  Coefficient_std_error  Coefficient_P_value\n",
       "1  corces_microglia -7.381441e-08           1.230802e-07             0.725656\n",
       "0    monocyte_dnase  1.446313e-07           1.238242e-07             0.121396"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kunkle_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": [
    "dims = (20, 16)\n",
    "plt.errorbar([0,1], y=jansen_data[\"Coefficient\"],\n",
    "                yerr=jansen_data[\"Coefficient_std_error\"],\n",
    "                fmt='o',\n",
    "                capsize=5,\n",
    "                ecolor='red',\n",
    "                markerfacecolor='red',\n",
    "                markeredgecolor='red')\n",
    "\n",
    "plt.axhline(y=0,linestyle='--', c='k')\n",
    "\n",
    "jansen_p_values = list(jansen_data[\"Coefficient_P_value\"])\n",
    "jansen_p_values = [round(val,2) for val in jansen_p_values]\n",
    "\n",
    "\n",
    "plt.text(0,\n",
    "         9e-8,\n",
    "         \"p=\"+str(jansen_p_values[0]),\n",
    "         fontsize=10,\n",
    "         ha='center')\n",
    "plt.text(1,\n",
    "         9e-8,\n",
    "         \"p=\"+str(jansen_p_values[1]),\n",
    "         fontsize=10,\n",
    "         ha='center')\n",
    "\n",
    "plt.xlim([-1,2])\n",
    "plt.ylim([-3e-8,10e-8])\n",
    "\n",
    "plt.ylabel(\"LDSC Coefficient\\n(Jansen GWAS)\", fontsize=10)\n",
    "plt.xlim(-1,2)\n",
    "plt.xticks([0,1], labels=[\"Corces_microglia\", \"ENCODE_monocyte\"])\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"monocyte_corces_microglia_pairwise_jansen.svg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": [
    "dims = (20, 16)\n",
    "plt.errorbar([0,1], y=kunkle_data[\"Coefficient\"],\n",
    "                yerr=kunkle_data[\"Coefficient_std_error\"],\n",
    "                fmt='o',\n",
    "                capsize=5,\n",
    "                ecolor='red',\n",
    "                markerfacecolor='red',\n",
    "                markeredgecolor='red')\n",
    "\n",
    "kunkle_p_values = list(kunkle_data[\"Coefficient_P_value\"])\n",
    "kunkle_p_values = [round(val,2) for val in kunkle_p_values]\n",
    "\n",
    "\n",
    "plt.text(0,\n",
    "         5e-7,\n",
    "         \"p=\"+str(kunkle_p_values[0]),\n",
    "         fontsize=10,\n",
    "         ha='center')\n",
    "plt.text(1,\n",
    "         5e-7,\n",
    "         \"p=\"+str(kunkle_p_values[1]),\n",
    "         fontsize=10,\n",
    "         ha='center')\n",
    "\n",
    "plt.xlim([-1,2])\n",
    "plt.xticks([0,1], labels=[\"Corces_microglia\", \"ENCODE_monocyte\"])\n",
    "plt.ylim([-3e-7,6e-7])\n",
    "plt.axhline(y=0,linestyle='--', c='k')\n",
    "plt.ylabel(\"LDSC Coefficient\\n(Kunkle GWAS)\", fontsize=10)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"monocyte_corces_microglia_pairwise_kunkle.svg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:data_analysis]",
   "language": "python",
   "name": "conda-env-data_analysis-py"
  },
  "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
}