plot_enrichments_pairwise_monocyte_microglia.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "inFile = '/projects/pfenninggroup/machineLearningForComputationalBiology/eramamur_stuff/gwas_enrichments/monocyte_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": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "inFile = '/projects/pfenninggroup/machineLearningForComputationalBiology/eramamur_stuff/gwas_enrichments/monocyte_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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>gosselin_microglia</td>\n",
       "      <td>4.415073e-08</td>\n",
       "      <td>3.694149e-08</td>\n",
       "      <td>0.116014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>monocyte_dnase</td>\n",
       "      <td>1.042658e-08</td>\n",
       "      <td>2.829146e-08</td>\n",
       "      <td>0.356235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Name   Coefficient  Coefficient_std_error  \\\n",
       "0  gosselin_microglia  4.415073e-08           3.694149e-08   \n",
       "1      monocyte_dnase  1.042658e-08           2.829146e-08   \n",
       "\n",
       "   Coefficient_P_value  \n",
       "0             0.116014  \n",
       "1             0.356235  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jansen_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>gosselin_microglia</td>\n",
       "      <td>-1.202519e-07</td>\n",
       "      <td>1.598233e-07</td>\n",
       "      <td>0.774096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>monocyte_dnase</td>\n",
       "      <td>2.954993e-07</td>\n",
       "      <td>1.438036e-07</td>\n",
       "      <td>0.019945</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Name   Coefficient  Coefficient_std_error  \\\n",
       "1  gosselin_microglia -1.202519e-07           1.598233e-07   \n",
       "0      monocyte_dnase  2.954993e-07           1.438036e-07   \n",
       "\n",
       "   Coefficient_P_value  \n",
       "1             0.774096  \n",
       "0             0.019945  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kunkle_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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=[\"Gosselin_microglia\", \"ENCODE_monocyte\"])\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"monocyte_microglia_pairwise_jansen.svg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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=[\"Gosselin_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_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
}