Gene Counts and Drug Targets Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Goal: combine RNASeq gene count data with information about top performing drugs and top drug targets to find insights about drug screening and gene expression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "gene_counts_df = pd.read_csv(\"harmonized_gene_counts.csv\", low_memory = False)\n",
    "drug_target_df = pd.read_csv(\"drug_target_associations_v_3_0_1.csv\", low_memory = False)\n",
    "unique_top_targets = list(set([line.replace(\"\\n\",\"\") for line in open(\"genes_targeted_by_top_drugs.txt\", \"r\")]))\n",
    "## 25 unique targets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Identify top gene targets in gene expression data and label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_genes_counts = []\n",
    "is_top_target = []\n",
    "for i in range(0,len(gene_counts_df)):\n",
    "    ## iterate over rows\n",
    "    if gene_counts_df.Symbol[i] in unique_top_targets:\n",
    "        top_genes_counts.append([gene_counts_df.Symbol[i],\n",
    "                                 gene_counts_df.totalCounts[i],\n",
    "                                 gene_counts_df.diagnosis[i]])\n",
    "        is_top_target.append(1)\n",
    "    else:\n",
    "        is_top_target.append(0)\n",
    "gene_counts_df[\"isTopTarget\"] = is_top_target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot distribution of non-top target gene counts vs top target gene counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gene_counts_df.boxplot(column=\"totalCounts\",by=\"isTopTarget\",showfliers=False)\n",
    "plt.ylabel(\"Gene Counts\")\n",
    "plt.title(\"\")\n",
    "plt.suptitle(\"\")\n",
    "plt.title(\"Gene Count Distributions for Top Drug Targets\")\n",
    "plt.show()\n",
    "plt.savefig(\"genecountsboxplotsgroupedbytoptargets.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not surprisingly, top targets tend to have higher counts than non-top targets.\n",
    "\n",
    "Now we visualize the gene counts grouped by diagnosis (NF1 vs NF2), for the overall dataset and only top targets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": [
    "gene_counts_df.boxplot(column=\"totalCounts\",by=\"diagnosis\",showfliers=False)\n",
    "plt.ylabel(\"Gene Counts\")\n",
    "plt.xlabel(\"Diagnosis\")\n",
    "plt.title(\"\")\n",
    "plt.suptitle(\"\")\n",
    "plt.title(\"Gene Count Distributions by Diagnosis, Overall Dataset\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": [
    "gene_counts_df[gene_counts_df.isTopTarget == 1].boxplot(column=\"totalCounts\",by=\"diagnosis\",showfliers=False)\n",
    "plt.ylabel(\"Gene Counts\")\n",
    "plt.xlabel(\"Diagnosis\")\n",
    "plt.title(\"\")\n",
    "plt.suptitle(\"\")\n",
    "plt.title(\"Gene Count Distributions by Diagnosis, Top Targets Only\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interestingly, while gene counts between NF1 and NF2 are similarly distributed for the overall data, within the top drug targets, NF2 samples tend to have higher gene counts than NF1 samples\n",
    "\n",
    "Now we can visualize the distribution of z scores for top and non-top drug targets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gene_counts_df.boxplot(column=\"zScore\",by=\"isTopTarget\",showfliers=False)\n",
    "plt.ylabel(\"Z Scores\")\n",
    "plt.suptitle(\"\")\n",
    "plt.title(\"Z Score Distributions for Top Drug Targets\")\n",
    "plt.show()\n",
    "plt.savefig(\"zscoreboxplotsgroupedbytoptargets.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, probably not surprisingly, the top drug targets tend to have higher z-scores"
   ]
  }
 ],
 "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
}