Shirin RNASeq for MPNST.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mining RNASeq for new MPNST drug targets\n",
    "\n",
    "## Summary:\n",
    "\n",
    "This notebook covers our NF Hackathon 2 effort to mine the RNASeq gene expression dataset for possible new drug targets. RNASeq includes a variety of tumor types; we concentrated on CNF. Our strategy was to compare gene expression values between CNF samples and as close as we could get to a control group of normal tissue. We then took the set of genes that appeared \"most disrupted\" vs. normal, and cross-checked it against Synapese's Drug Target Explorer. We found that a high proportion of this \"most disrupted\" gene set appeared in the Drug Target Explorer as having known associations with copper. This seems intriguing - while copper is apparently novel in the NF1/CNF context it has a well-established role in many disease processes - notably, it appears heavily involved in angiogenesis and tumor formation, likely through a variety of pathways. A number of drugs are used to reduce copper levels (ex. in Wilson's disease), including penicillamine and TM (tetrathiomolybdate); TM has also been investigated for cancer treatment. Perhaps this suggests a direction for drug development that could work on a number of pathways simultaneously, and possibly be synergistic with other drug approaches. For more details and references, see the readme file.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Import libraries and retrieve the RNASeq dataset from Synapse:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "from copy import deepcopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "use_saved = os.path.exists('rnaseq.pkl')\n",
    "if not use_saved :\n",
    "    import synapseclient\n",
    "    syn = synapseclient.Synapse()\n",
    "    ## log in using username and password\n",
    "    syn.login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\n",
    "import csv\n",
    "import itertools\n",
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "import matplotlib.style as style\n",
    "style.use('ggplot')\n",
    "#style.use('Solarize_Light2')\n",
    "from matplotlib import rcParams\n",
    "rcParams['pdf.fonttype'] = 42 ## Output Type 3 (Type3) or Type 42 (TrueType)\n",
    "rcParams['font.sans-serif'] = 'Arial'\n",
    "from pylab import *\n",
    "\n",
    "# ignore FutureWarning that may pop up when plotting\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import urllib3\n",
    "urllib3.disable_warnings()\n",
    "\n",
    "from dfply import *\n",
    "\n",
    "# For PCA\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not use_saved :\n",
    "    entity = syn.get('syn20449214')\n",
    "    #entity.id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not use_saved :\n",
    "    results = syn.tableQuery(\"SELECT * FROM syn20449214\")\n",
    "    genes_with_meta = results.asDataFrame()\n",
    "    genes_with_meta.to_pickle('rnaseq.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "if use_saved :\n",
    "    genes_with_meta = pd.read_pickle('rnaseq.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['totalCounts', 'Symbol', 'path', 'zScore', 'specimenID', 'parent',\n",
       "       'individualID', 'assay', 'dataType', 'sex', 'consortium', 'study',\n",
       "       'diagnosis', 'tumorType', 'species', 'fundingAgency', 'resourceType',\n",
       "       'nf1Genotype', 'nf2Genotype', 'studyName', 'used', 'age', 'isCellLine',\n",
       "       'experimentalCondition', 'transplantationType', 'modelOf'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genes_with_meta.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Clean up data format\n",
    "genes_with_meta = genes_with_meta.replace(to_replace = \"male\", value = \"Male\")\n",
    "genes_with_meta = genes_with_meta.replace(to_replace = \"female\", value = \"Female\")\n",
    "genes_with_meta = genes_with_meta.rename(columns={'used': 'id'})\n",
    "genes_with_meta = genes_with_meta.reset_index()\n",
    "genes_with_meta = genes_with_meta.drop(labels=[\"index\", \"path\"], axis =1)\n",
    "#Quality control\n",
    "#removing any rows or columns with uncompliant values\n",
    "genes_with_meta = genes_with_meta[genes_with_meta.totalCounts != \"Male\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## A quick look at the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Contents of the file:\n"
     ]
    },
    {
     "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>totalCounts</th>\n",
       "      <th>Symbol</th>\n",
       "      <th>zScore</th>\n",
       "      <th>specimenID</th>\n",
       "      <th>parent</th>\n",
       "      <th>individualID</th>\n",
       "      <th>assay</th>\n",
       "      <th>dataType</th>\n",
       "      <th>sex</th>\n",
       "      <th>consortium</th>\n",
       "      <th>...</th>\n",
       "      <th>resourceType</th>\n",
       "      <th>nf1Genotype</th>\n",
       "      <th>nf2Genotype</th>\n",
       "      <th>studyName</th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "      <th>isCellLine</th>\n",
       "      <th>experimentalCondition</th>\n",
       "      <th>transplantationType</th>\n",
       "      <th>modelOf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>136.993</td>\n",
       "      <td>A1BG</td>\n",
       "      <td>-0.226861</td>\n",
       "      <td>patient10tumor1</td>\n",
       "      <td>syn18407530</td>\n",
       "      <td>patient10</td>\n",
       "      <td>rnaSeq</td>\n",
       "      <td>geneExpression</td>\n",
       "      <td>Male</td>\n",
       "      <td>CTF Biobank</td>\n",
       "      <td>...</td>\n",
       "      <td>experimentalData</td>\n",
       "      <td>-/-</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Cutaneous Neurofibroma Data Resource</td>\n",
       "      <td>syn20430744</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cutaneous Neurofibroma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>168.454</td>\n",
       "      <td>A1BG</td>\n",
       "      <td>-0.248889</td>\n",
       "      <td>patient10tumor2</td>\n",
       "      <td>syn18407530</td>\n",
       "      <td>patient10</td>\n",
       "      <td>rnaSeq</td>\n",
       "      <td>geneExpression</td>\n",
       "      <td>Male</td>\n",
       "      <td>CTF Biobank</td>\n",
       "      <td>...</td>\n",
       "      <td>experimentalData</td>\n",
       "      <td>-/-</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Cutaneous Neurofibroma Data Resource</td>\n",
       "      <td>syn20430745</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cutaneous Neurofibroma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>115.456</td>\n",
       "      <td>A1BG</td>\n",
       "      <td>-0.115625</td>\n",
       "      <td>patient10tumor3</td>\n",
       "      <td>syn18407530</td>\n",
       "      <td>patient10</td>\n",
       "      <td>rnaSeq</td>\n",
       "      <td>geneExpression</td>\n",
       "      <td>Male</td>\n",
       "      <td>CTF Biobank</td>\n",
       "      <td>...</td>\n",
       "      <td>experimentalData</td>\n",
       "      <td>-/-</td>\n",
       "      <td>unknown</td>\n",
       "      <td>Cutaneous Neurofibroma Data Resource</td>\n",
       "      <td>syn20430746</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cutaneous Neurofibroma</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   totalCounts Symbol    zScore       specimenID       parent individualID  \\\n",
       "0      136.993   A1BG -0.226861  patient10tumor1  syn18407530    patient10   \n",
       "1      168.454   A1BG -0.248889  patient10tumor2  syn18407530    patient10   \n",
       "2      115.456   A1BG -0.115625  patient10tumor3  syn18407530    patient10   \n",
       "\n",
       "    assay        dataType   sex   consortium  ...      resourceType  \\\n",
       "0  rnaSeq  geneExpression  Male  CTF Biobank  ...  experimentalData   \n",
       "1  rnaSeq  geneExpression  Male  CTF Biobank  ...  experimentalData   \n",
       "2  rnaSeq  geneExpression  Male  CTF Biobank  ...  experimentalData   \n",
       "\n",
       "  nf1Genotype nf2Genotype                             studyName           id  \\\n",
       "0         -/-     unknown  Cutaneous Neurofibroma Data Resource  syn20430744   \n",
       "1         -/-     unknown  Cutaneous Neurofibroma Data Resource  syn20430745   \n",
       "2         -/-     unknown  Cutaneous Neurofibroma Data Resource  syn20430746   \n",
       "\n",
       "  age isCellLine experimentalCondition transplantationType  \\\n",
       "0 NaN        NaN                   NaN                 NaN   \n",
       "1 NaN        NaN                   NaN                 NaN   \n",
       "2 NaN        NaN                   NaN                 NaN   \n",
       "\n",
       "                  modelOf  \n",
       "0  Cutaneous Neurofibroma  \n",
       "1  Cutaneous Neurofibroma  \n",
       "2  Cutaneous Neurofibroma  \n",
       "\n",
       "[3 rows x 25 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Contents of the file:\")\n",
    "genes_with_meta.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following table contains detailed descriptions of the metadata variables included in the data file:\n",
    "\n",
    "Var | Description\n",
    "--- | --- \n",
    " _id_  |  the synapseIDs of the individual files with raw data\n",
    " _specimenID_  |  individual samples\n",
    " _species_  |  the source of the specimen\n",
    " _age_  |  the age of the patient \n",
    " _Sex_  |  the sex of the patient\n",
    " _tumorType_  |  the the type of tumor, can be one of 7 different diagnoses\n",
    " _isCellLine_  |  indicates whether the origin tissue was a cell line or a patient\n",
    " _study_  |  the specific initiative/consortia that the study was a part of\n",
    " _Symbol_  |  the common names of genes \n",
    " _cellCulture_  |  whether the data was obtained from a cell culture experiment or an _in-vivo_ experiment. Many inherent differences exist between _cell culture_ experiments and _in-vivo_ experiments. These differences may need to be taken into account during analysis and interpretation of the data\n",
    " _totalCounts_  |  the main measure of expression levels of the gene. _(the values documented here are not corrected for batch/study)_ \n",
    " _zScore_  |  normalized counts (counts for a gene can vary significantly due to technical differences in different studies, so we normalized the counts into a zScore for a comparable measurement between samples for the same gene)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of the file:\n",
      "(7778164, 25)\n"
     ]
    }
   ],
   "source": [
    "print(\"Size of the file:\")\n",
    "print(genes_with_meta.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Cutaneous Neurofibroma', 'Neurofibroma',\n",
       "       'Malignant Peripheral Nerve Sheath Tumor',\n",
       "       'Plexiform Neurofibroma', 'normal', 'Low Grade Glioma',\n",
       "       'High Grade Glioma', 'Meningioma', 'schwannoma', 'NF1 tumor',\n",
       "       'Malignant peripheral nerve sheath tumor', 'Schwannoma',\n",
       "       'Ependymoma', 'Ganglioglioma', 'Other'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genes_with_meta['modelOf'].unique()  # tumor types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": [
    "# plot histogram of zScore values\n",
    "plt.xlim(-1,1)\n",
    "genes_with_meta.zScore.hist(bins=[0.01*i for i in range(-110,110)]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def howManyUnique(col,df=None) :\n",
    "    if df is None : df = genes_with_meta\n",
    "    return len(df[col].unique())\n",
    "def howManyWith(col,val,ucols=[]) :\n",
    "    df = genes_with_meta[genes_with_meta[col] ==val]\n",
    "    return (len(df),) + tuple(howManyUnique(ucol,df) for ucol in ucols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just change this line to MPNST:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "complication='Malignant Peripheral Nerve Sheath Tumor'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('modelOf', 'Malignant Peripheral Nerve Sheath Tumor', ['specimenID', 'individualID'], 345549, 18, 9)\n",
      "('modelOf', 'normal', ['specimenID', 'individualID'], 1970911, 100, 25)\n"
     ]
    }
   ],
   "source": [
    "for ntup in [tup + howManyWith(*tup) for tup in [\n",
    "      ('modelOf',complication, ['specimenID','individualID']), \n",
    "      ('modelOf','normal',['specimenID','individualID']), \n",
    "    ]] :\n",
    "    print(ntup)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Cut down to CNF data and controls derived from normal Schwannn cells:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def grabPartDF(cols,withVals=[],df=None) :\n",
    "    if df is None : df = genes_with_meta\n",
    "    partdf = df[cols]\n",
    "    newCols = list(cols)\n",
    "    for col,val in withVals :\n",
    "        partdf = partdf[partdf[col]==val]\n",
    "        newCols.remove(col)\n",
    "    return partdf[newCols]\n",
    "colsL = ['Symbol','specimenID','individualID','zScore','modelOf',]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnfDF = grabPartDF(colsL,\n",
    "                   [('modelOf',complication)],\n",
    "                )\n",
    "normDF = grabPartDF(colsL,\n",
    "                   [('modelOf','normal')],\n",
    "                )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CNF dataset: 18 specimens, 9 individuals\n"
     ]
    }
   ],
   "source": [
    "specimens = sorted(cnfDF.specimenID.unique())\n",
    "individuals = sorted(cnfDF.individualID.unique())\n",
    "print('CNF dataset: {} specimens, {} individuals'.format(len(specimens),len(individuals)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Next we cut the \"normal\" control dataset down** to two cell lines derived from immortalized normal Schwann cells. This was based on advice from Ray Mattingly and Sarah Gosline that these would make the best controls for comparison. See the readme for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['AC027', 'AC028', 'AC029', 'AC029-1', 'AC029-2', 'AC030-1', 'AC030-2', 'AC033-1', 'AC033-2', 'AC033-3', 'AC034', 'AC6-1', 'AC6-2', 'AC7-1', 'AC7-2', 'AC7_A3', 'AC7_A4', 'HS11_1', 'HS11_10', 'HS11_11', 'HS11_12', 'HS11_13', 'HS11_14', 'HS11_15', 'HS11_16', 'HS11_17', 'HS11_18', 'HS11_2', 'HS11_3', 'HS11_4', 'HS11_5', 'HS11_6', 'HS11_7', 'HS11_8', 'HS11_9', 'HSC1L_GATCAG_L002', 'HSC1L_S27', 'MTa450-1', 'MTa450-10', 'MTa450-11', 'MTa450-15', 'MTa450-16', 'MTa450-17', 'MTa450-2', 'MTa450-3', 'MTa450-9', 'MTa451-7', 'MTa451-8', 'MTa451-9', 'N10_S25', 'N15_S3', 'N23_S24', 'N3_S26', 'N87_S23', 'Syn10-1', 'Syn10-2', 'Syn10-3', 'Syn10-4', 'Syn10-5', 'Syn10-6', 'Syn10-7', 'Syn10-8', 'Syn1_1', 'Syn1_10', 'Syn1_11', 'Syn1_12', 'Syn1_13', 'Syn1_14', 'Syn1_15', 'Syn1_16', 'Syn1_17', 'Syn1_18', 'Syn1_19', 'Syn1_2', 'Syn1_20', 'Syn1_21', 'Syn1_22', 'Syn1_23', 'Syn1_24', 'Syn1_3', 'Syn1_4', 'Syn1_5', 'Syn1_6', 'Syn1_7', 'Syn1_8', 'Syn1_9', 'Syn2-1', 'Syn2-2', 'Syn2-3', 'Syn3-1', 'Syn3-2', 'Syn3-3', 'WT-N10_TAGCTT_L002', 'WT-N23_TGACCA_L002', 'WT-N3_ACAGTG_L002', 'WT-N87_TTAGGC_L002', 'ipn02.3', 'ipn97.4', 'pn97.4', 'pnNF95.12B']\n"
     ]
    }
   ],
   "source": [
    "print(sorted(normDF.specimenID.unique())) # all \"normal\" specimens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38196"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normDF = normDF[normDF.specimenID.isin(('ipn02.3','ipn97.4'))] # derived from normal Schwann cells\n",
    "len(normDF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True,)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(cnfDF.Symbol.unique())==set(normDF.Symbol.unique()), # sanity check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Symbol</th>\n",
       "      <th>specimenID</th>\n",
       "      <th>individualID</th>\n",
       "      <th>zScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1170500</td>\n",
       "      <td>A1BG</td>\n",
       "      <td>ipn02.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.172290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1170501</td>\n",
       "      <td>A1BG</td>\n",
       "      <td>ipn97.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.233391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1170517</td>\n",
       "      <td>A1CF</td>\n",
       "      <td>ipn02.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.190585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1170518</td>\n",
       "      <td>A1CF</td>\n",
       "      <td>ipn97.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.242574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1170534</td>\n",
       "      <td>A2M</td>\n",
       "      <td>ipn02.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.178365</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Symbol specimenID individualID    zScore\n",
       "1170500   A1BG    ipn02.3          NaN -0.172290\n",
       "1170501   A1BG    ipn97.4          NaN -0.233391\n",
       "1170517   A1CF    ipn02.3          NaN -0.190585\n",
       "1170518   A1CF    ipn97.4          NaN -0.242574\n",
       "1170534    A2M    ipn02.3          NaN -0.178365"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normDF.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Calculate \"most disturbed\" genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "normMeanDF = normDF.groupby(['Symbol']).mean() # average zScore values for each gene on the \"normal\" set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((345549, 4), (38196, 4), (19098, 1))"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnfDF.shape,normDF.shape,normMeanDF.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "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>zScore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Symbol</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>A1BG</td>\n",
       "      <td>-0.202840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A1CF</td>\n",
       "      <td>-0.216580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A2M</td>\n",
       "      <td>-0.181888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A2ML1</td>\n",
       "      <td>-0.216443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>A3GALT2</td>\n",
       "      <td>-0.216558</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           zScore\n",
       "Symbol           \n",
       "A1BG    -0.202840\n",
       "A1CF    -0.216580\n",
       "A2M     -0.181888\n",
       "A2ML1   -0.216443\n",
       "A3GALT2 -0.216558"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normMeanDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "normMeanMap = dict(zip(normMeanDF.index,normMeanDF.as_matrix().squeeze()))\n",
    "# maps gene symbol -> average expression value on the \"normal\" set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# map gene symbol -> list of absolute difference of zScore between CNF specimens and \"normal\" average\n",
    "disturbLMap = dict((sym,[]) for sym in normMeanMap)\n",
    "for sym,_,_,v in cnfDF.as_matrix() :\n",
    "    disturbLMap[sym].append(abs(v-normMeanMap[sym]))\n",
    "def countAllDisturbed(thresh) :\n",
    "    # count total number of absolute differences above thresh (used for sanity check)\n",
    "    tot = totLen = 0\n",
    "    for vs in disturbLMap.values() :\n",
    "        totLen += len(vs)\n",
    "        tot += sum(1 if v>=thresh else 0 for v in vs)\n",
    "    return tot,totLen\n",
    "def listAllDisturbVals(maxVal=2.0) :\n",
    "    # sorted list of all absolute differences clipped to maxVal (used for sanity check)\n",
    "    return sorted(min(v,maxVal) for v in itertools.chain.from_iterable(disturbLMap.values()))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# map gene symbol -> average absolute difference over CNF specimens, removing the maximal value\n",
    "disturbMap = dict((sym,[]) for sym in normMeanMap)\n",
    "for sym,vs in disturbLMap.items() :\n",
    "    vvs = list(vs)\n",
    "    vvs.remove(max(vs))\n",
    "    disturbMap[sym] = sum(vvs)/len(vvs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A few sanity checks:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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": [
    "plt.plot(listAllDisturbVals());"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21632, 345549)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countAllDisturbed(0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": [
    "plt.plot(sorted(disturbMap.values())[:-100]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read the Drug Target Explorer data and build a map: target -> list of known associated compounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def readDrugAssoc(fPath='drug_target_associations_v2.txt') :\n",
    "    with open(fPath,'r') as f :\n",
    "        return list(csv.reader(f,delimiter=' '))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "drugAssoc = readDrugAssoc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "drugAssocMap = collections.defaultdict(list)\n",
    "for r in drugAssoc[1:] :\n",
    "    drugAssocMap[r[1]].append(r[-6])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build a heatmap of \"most disturbed\" genes, annotated with known copper associations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "nSyms = 100\n",
    "syms = sorted(disturbMap.keys(), key=lambda x : disturbMap[x], reverse=True)[:nSyms]\n",
    "symInd = dict((sym,i) for i,sym in enumerate(syms))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x2016 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "disturbHM = np.zeros((nSyms,len(specimens)+3))\n",
    "maxHMVal = 20.0\n",
    "disturbHM[:,-3:] = maxHMVal\n",
    "y_labels=[]\n",
    "x_labels=[]\n",
    "fig,ax = plt.subplots(figsize=(10,28))\n",
    "#ax.xaxis.grid(False)\n",
    "ax.xaxis.tick_top()\n",
    "ax.set_yticks(np.arange(len(disturbHM)))\n",
    "ax.set_yticklabels(syms)\n",
    "y_labels=syms\n",
    "ax.set_xticks(np.arange(len(specimens)+4))\n",
    "ax.set_xticklabels(specimens + [' ','copper?',' '])\n",
    "x_labels=specimens + ['_1','copper?','_2']\n",
    "plt.setp(ax.get_xticklabels(),rotation=85)\n",
    "totKnown = totCopper = 0\n",
    "for sym,specimenID,_,v in cnfDF.as_matrix() :\n",
    "    if sym in symInd :\n",
    "        i = symInd[sym]\n",
    "        j = specimens.index(specimenID)\n",
    "        avgV = normMeanMap[sym]\n",
    "        disturbHM[i,j] = min(maxHMVal,abs(v-avgV))\n",
    "        if 'Copper' in drugAssocMap[sym] :\n",
    "            isHigher = v > avgV\n",
    "            ax.text(j,i,\n",
    "                    '>' if isHigher else '<',\n",
    "                    ha='center',va='center',\n",
    "                    color='g' if isHigher else 'y')\n",
    "for i,sym in enumerate(syms) :\n",
    "    if len(drugAssocMap[sym])==0 :\n",
    "        ax.text(len(specimens)+1,i,'X',ha='center',va='center',color='b')\n",
    "    else :\n",
    "        totKnown += 1\n",
    "        if 'Copper' in drugAssocMap[sym] :\n",
    "            totCopper += 1\n",
    "            ax.text(len(specimens)+1,i,'+',ha='center',va='center',color='g')\n",
    "        else :\n",
    "            ax.text(len(specimens)+1,i,'-',ha='center',va='center',color='r')\n",
    "ax.imshow(disturbHM, cmap='hot', interpolation='nearest', aspect='auto')\n",
    "plt.savefig('shirinRNA.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## copper? column:\n",
    "## X not in Drug Target Explorer \n",
    "## + = in DTE, known association with copper\n",
    "## - = in DTE, no known association with copper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16 of 54 (30%) of \"most disturbed\" genes in the DTE had known copper associations \n"
     ]
    }
   ],
   "source": [
    "print('{} of {} ({:.0%}) of \"most disturbed\" genes in the DTE had known copper associations '\n",
    "      .format(totCopper,totKnown,totCopper/float(totKnown)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "copperGene=[r for r in drugAssoc if r[-6].lower()=='copper']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "144 of 3739 (4%) of all target genes in the DTE have a known association with copper\n"
     ]
    }
   ],
   "source": [
    "totCopper,totKnown = len(copperGene),len(drugAssocMap)\n",
    "print('{} of {} ({:.0%}) of all target genes in the DTE have a known association with copper'\n",
    "      .format(totCopper,totKnown,totCopper/float(totKnown)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpnst=pd.DataFrame(disturbHM,columns=x_labels)\n",
    "mpnst.insert(loc=0,column='gene',value=y_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpnst['has_copper']=['Copper' in drugAssocMap[x] for x in y_labels]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpnst_cu = deepcopy(mpnst[['gene']+[x for x in x_labels if 'Malignant' in x]+['has_copper']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpnst_cu = mpnst_cu[mpnst_cu.has_copper]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpnst_cu['sum_score']=mpnst_cu.iloc[:,1:5].sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "genes=mpnst_cu.sort_values('sum_score',ascending=False).gene.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['EEF1A1', 'GAPDH', 'ENO1', 'B2M', 'RPSA', 'HSPA8', 'PKM', 'TKT',\n",
       "       'RACK1', 'PPIA', 'RPS2', 'HSPD1', 'TUBB', 'APP', 'LDHA', 'ACTG1'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "drugs=set()\n",
    "for gene in genes:\n",
    "    drugs.update(set(drugAssocMap[gene]))\n",
    "\n",
    "drugs = {x:0 for x in drugs}\n",
    "\n",
    "for gene in genes:\n",
    "    for drug in drugAssocMap[gene]:\n",
    "        drugs[drug] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "drugs=list(reversed(sorted([(y,x) for x,y in drugs.items()])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "drugs=[(y,x) for x,y in drugs if y not in ['Copper', 'Zinc'] and x > 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Artenimol', 9), ('NSC-119911', 2), ('NADH', 2)]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drugs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['EEF1A1', 'GAPDH', 'ENO1', 'HSPA8', 'PKM', 'PPIA', 'TUBB', 'LDHA', 'ACTG1']"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[gene for gene in genes if drugs[0][0] in drugAssocMap[gene]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Artenimol ['EEF1A1', 'GAPDH', 'ENO1', 'HSPA8', 'PKM', 'PPIA', 'TUBB', 'LDHA', 'ACTG1']\n",
      "NSC-119911 ['GAPDH', 'HSPA8']\n",
      "NADH ['GAPDH', 'LDHA']\n"
     ]
    }
   ],
   "source": [
    "for drug,count in drugs:\n",
    "    print(drug, [gene for gene in genes if drug in drugAssocMap[gene]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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