CPTAC_Analysis_CTNND1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Is there a relationship between HER2 overexpression and SHC, CTNND1 phosphorylation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline \n",
    "import pandas as pd\n",
    "import re\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "\n",
    "metadataFile = './data/BCRA_metadata.txt' #Annotations from Mertins et al.\n",
    "\n",
    "dataFile = './data/experiment.1726.2478.1467384999.tsv' #phosphoproteomic CPTAC dataset by Mertins et al. 2016\n",
    "\n",
    "patientFile = './data/TCGA_annotations.csv' #Annotations from TCGA, includes more annotations\n",
    "\n",
    "df = pd.DataFrame.from_csv(dataFile, sep='\\t')\n",
    "\n",
    "# Either load Mertins et al and filter by QC status or use patient File\n",
    "df_meta = pd.DataFrame.from_csv(metadataFile, sep='\\t')\n",
    "df_meta = df_meta[df_meta['QC Status']=='pass']\n",
    "\n",
    "#Here is patient file load\n",
    "#df_meta = pd.DataFrame.from_csv(patientFile, sep=',')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'TCGA ID', u'Biospecimen Barcode Side', u'UIUD', u'PAM50',\n",
       "       u'ER Status', u'PR Status', u'HER2 Status', u'QC Status',\n",
       "       u'Proteome Cluster (see Fig. 3b)',\n",
       "       u'Phosphoproteome Cluster (see Fig. 3b)',\n",
       "       u'Phosphoproteome-pathways cluster (see Fig. 3d)', u'runOrder',\n",
       "       u'experiment', u'iTRAQReporterIon', u'CommonControl', u'GATA3 Mutation',\n",
       "       u'PIK3CA Mutation', u'TP53 Mutation',\n",
       "       u'PIK3CA missense mutation in helical domain all tumors',\n",
       "       u'PIK3CA missense mutation in kinase domain all tumors',\n",
       "       u'TP53 Nonsense/Frameshift all tumors',\n",
       "       u'TP53 Missense mutation all tumors',\n",
       "       u'TP53 missense mutations in DNA binding domain all tumors'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Print the metadata columns one could use \n",
    "df_meta.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Create a map between the names of patients in the two dataframes\n",
    "patient_map_dataToMeta = {}\n",
    "patient_map_metaToData = {}\n",
    "df_col_headers = df.columns\n",
    "df_meta_patients = list(df_meta['TCGA ID'])\n",
    "for patient in df_meta_patients:\n",
    "    patientTemp = patient.replace('TCGA-', '')\n",
    "    for col in df_col_headers:\n",
    "        a = re.search(patientTemp, col)\n",
    "        if a: \n",
    "            patient_map_dataToMeta[col] = patient\n",
    "            patient_map_metaToData[patient] = col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what patients with data do not have entries \n",
    "dataPatients = patient_map_dataToMeta.keys()\n",
    "metaPatients = patient_map_metaToData.keys() #there are 3 more patients than matches to metaData\n",
    "toKeep = (set(dataPatients) & set(metaPatients))\n",
    "toKeep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_cols = patient_map_dataToMeta.keys() # names of columns that have data and can map into metadata information\n",
    "data_cols.append('gene_site')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], dtype=int64)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# are their patients with no data?\n",
    "data_cols = patient_map_dataToMeta.keys()\n",
    "df_dataOnly = df[data_cols]\n",
    "df_dataOnly.isnull().all(axis=0).nonzero()[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the protein names of interest, for referencing data vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SHC1_Y317\n",
      "SHC1_Y239_Y240\n"
     ]
    }
   ],
   "source": [
    "protein_sites = list(df['gene_site'])\n",
    "protein_name = 'SHC1'\n",
    "for protein_site in protein_sites:\n",
    "    a = re.match(protein_name, protein_site)\n",
    "    if a:\n",
    "        print protein_site"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def returnSelectedDataByGeneSite(df, gene_sites, data_cols):\n",
    "    selectData = pd.DataFrame(columns=data_cols)\n",
    "    for site in gene_sites:\n",
    "        series = df[df['gene_site']==site][data_cols]\n",
    "        selectData = selectData.append(series)\n",
    "    return selectData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>average:data:patient:A8-A08Z.14TCGA</th>\n",
       "      <th>average:data:patient:AO-A0JE.21TCGA</th>\n",
       "      <th>average:data:patient:A7-A0CJ.22TCGA</th>\n",
       "      <th>average:data:patient:D8-A142.18TCGA</th>\n",
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       "      <th>average:data:patient:A2-A0CM.07TCGA</th>\n",
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       "      <th>...</th>\n",
       "      <th>average:data:patient:BH-A0AV.05TCGA</th>\n",
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       "      <th>average:data:patient:A2-A0EY.16TCGA</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>-1.59152</td>\n",
       "      <td>1.641340</td>\n",
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       "      <td>-0.459827</td>\n",
       "      <td>-2.87148</td>\n",
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       "      <td>0.114181</td>\n",
       "      <td>-2.62057</td>\n",
       "      <td>0.371174</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.638418</td>\n",
       "      <td>-1.75583</td>\n",
       "      <td>-2.51759</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.41542</td>\n",
       "      <td>-2.55287</td>\n",
       "      <td>1.57609</td>\n",
       "      <td>-2.59495</td>\n",
       "      <td>-1.79149</td>\n",
       "      <td>-0.05598</td>\n",
       "      <td>-2.88385</td>\n",
       "      <td>-0.251476</td>\n",
       "      <td>-2.79621</td>\n",
       "      <td>-1.639310</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 80 columns</p>\n",
       "</div>"
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      "text/plain": [
       "         average:data:patient:A8-A08Z.14TCGA  \\\n",
       "6209118                            -0.937099   \n",
       "6208797                             2.912480   \n",
       "6209162                             2.143850   \n",
       "\n",
       "         average:data:patient:AO-A0JE.21TCGA  \\\n",
       "6209118                            -0.036047   \n",
       "6208797                            -1.062590   \n",
       "6209162                            -1.264720   \n",
       "\n",
       "         average:data:patient:A7-A0CJ.22TCGA  \\\n",
       "6209118                            -1.037090   \n",
       "6208797                             1.608380   \n",
       "6209162                             0.665169   \n",
       "\n",
       "         average:data:patient:D8-A142.18TCGA  \\\n",
       "6209118                                  NaN   \n",
       "6208797                             0.643398   \n",
       "6209162                             0.114181   \n",
       "\n",
       "         average:data:patient:AO-A12B.34TCGA  \\\n",
       "6209118                                  NaN   \n",
       "6208797                             -1.59152   \n",
       "6209162                             -2.62057   \n",
       "\n",
       "         average:data:patient:A2-A0CM.07TCGA  \\\n",
       "6209118                                  NaN   \n",
       "6208797                             1.641340   \n",
       "6209162                             0.371174   \n",
       "\n",
       "         average:data:patient:A8-A09G.32TCGA  \\\n",
       "6209118                                  NaN   \n",
       "6208797                                  NaN   \n",
       "6209162                                  NaN   \n",
       "\n",
       "         average:data:patient:BH-A0DG.19TCGA  \\\n",
       "6209118                                  NaN   \n",
       "6208797                            -0.459827   \n",
       "6209162                            -0.638418   \n",
       "\n",
       "         average:data:patient:AO-A12D.01TCGA  \\\n",
       "6209118                                  NaN   \n",
       "6208797                             -2.87148   \n",
       "6209162                             -1.75583   \n",
       "\n",
       "         average:data:patient:AR-A1AV.17TCGA  \\\n",
       "6209118                                  NaN   \n",
       "6208797                             -1.51965   \n",
       "6209162                             -2.51759   \n",
       "\n",
       "                        ...                   \\\n",
       "6209118                 ...                    \n",
       "6208797                 ...                    \n",
       "6209162                 ...                    \n",
       "\n",
       "         average:data:patient:BH-A0AV.05TCGA  \\\n",
       "6209118                                  NaN   \n",
       "6208797                             -3.97160   \n",
       "6209162                             -2.41542   \n",
       "\n",
       "         average:data:patient:AO-A0JM.30TCGA  \\\n",
       "6209118                                  NaN   \n",
       "6208797                             -3.01661   \n",
       "6209162                             -2.55287   \n",
       "\n",
       "         average:data:patient:AR-A0TX.14TCGA  \\\n",
       "6209118                             -2.01457   \n",
       "6208797                             -2.15439   \n",
       "6209162                              1.57609   \n",
       "\n",
       "         average:data:patient:BH-A18Q.02TCGA  \\\n",
       "6209118                                  NaN   \n",
       "6208797                             -2.39064   \n",
       "6209162                             -2.59495   \n",
       "\n",
       "         average:data:patient:AN-A0FL.19TCGA  \\\n",
       "6209118                                  NaN   \n",
       "6208797                             -1.59996   \n",
       "6209162                             -1.79149   \n",
       "\n",
       "         average:data:patient:AO-A12B.01TCGA  \\\n",
       "6209118                                  NaN   \n",
       "6208797                             -1.56390   \n",
       "6209162                             -0.05598   \n",
       "\n",
       "         average:data:patient:E2-A15A.29TCGA  \\\n",
       "6209118                                  NaN   \n",
       "6208797                             -2.80242   \n",
       "6209162                             -2.88385   \n",
       "\n",
       "         average:data:patient:AN-A0AM.18TCGA  \\\n",
       "6209118                                  NaN   \n",
       "6208797                             0.174611   \n",
       "6209162                            -0.251476   \n",
       "\n",
       "         average:data:patient:A2-A0EY.16TCGA  \\\n",
       "6209118                                  NaN   \n",
       "6208797                             -1.68631   \n",
       "6209162                             -2.79621   \n",
       "\n",
       "         average:data:patient:AO-A03O.25TCGA  \n",
       "6209118                                  NaN  \n",
       "6208797                            -0.638983  \n",
       "6209162                            -1.639310  \n",
       "\n",
       "[3 rows x 80 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Example of how to select data for a specific group\n",
    "gene_sites_24H_CTNND1_cluster = ['CTNND1_Y228', 'SHC1_Y349', 'MAPK1_Y187', 'MAPK3_Y204'] \n",
    "\n",
    "gene_sites_of_interest = gene_sites_24H_CTNND1_cluster\n",
    "\n",
    "selectData = returnSelectedDataByGeneSite(df, gene_sites_of_interest, data_cols)\n",
    "selectData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def returnPatientLabels(df_meta, metaDataOI):\n",
    "    '''Assign all patients to a class label in the metaDataOI column'''\n",
    "    types = df_meta.groupby(metaDataOI)\n",
    "    classDict = {}\n",
    "    labelDict = {}\n",
    "    for t, row in types:\n",
    "        print t\n",
    "        classDict[t] = df_meta[df_meta[metaDataOI]==t]['TCGA ID'] # get all TCGA IDs in a certain class\n",
    "    for key in classDict.keys():\n",
    "        for patient in classDict[key]:\n",
    "            if patient in patient_map_metaToData:\n",
    "                labelDict[patient_map_metaToData[patient]] = key\n",
    "    return labelDict, classDict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Equivocal\n",
      "Negative\n",
      "Positive\n"
     ]
    }
   ],
   "source": [
    "# HERE: SELECT The metadata column of interest for plotting and p-value calculations\n",
    "\n",
    "#1. Group patients into groups based on the metadata\n",
    "#metaDataOI = 'PAM50'\n",
    "metaDataOI = 'HER2 Status'\n",
    "classArray = []\n",
    "#metaDataOI = 'Metastasis-Coded'\n",
    "#metaDataOI = 'Node-Coded'\n",
    "\n",
    "#metaDataOI = 'Tumor'\n",
    "#metaDataOI = 'Node'\n",
    "#metaDataOI = 'ER Status'\n",
    "#classArray = []\n",
    "#metaDataOI = 'Metastasis'\n",
    "\n",
    "#metaDataOI = 'PAM50'\n",
    "\n",
    "#metaDataOI = 'HER2 Final Status'\n",
    "#classArray = ['Negative', 'Positive', 'Equivocal', 'Not Available']\n",
    "\n",
    "#metaDataOI = 'AJCC Stage'\n",
    "#classArray = ['Stage I', 'Stage IA', 'Stage IB', 'Stage II', 'Stage IIA', 'Stage IIB', \n",
    "#              'Stage III', 'Stage IIIA', 'Stage IIIB', 'Stage IIIC', 'Stage IV', 'Stage X', '[Not Available]']\n",
    "\n",
    "labelDict, classDict = returnPatientLabels(df_meta, metaDataOI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def boxplot_forProtein(labelDict, classDict, series, site, sort=[]):\n",
    "    \"\"\"\n",
    "    Returns patientNumberDict and arrayDict, which is the number of patients that are in a class, based on the\n",
    "    keys in classDict (i.e. types of patient classes). ArrayDict keeps a running vector of entries of data in series\n",
    "    that exists. Plots the data with the title name equal to site.\n",
    "    \"\"\"\n",
    "    \n",
    "    arrayDict = {}\n",
    "    patientNumberDict = {}\n",
    "    for classType in classDict.keys():\n",
    "        arrayDict[classType] = []\n",
    "        patientNumberDict[classType] = 0\n",
    "    for patient in labelDict.keys():\n",
    "        value = np.asscalar(series[patient])\n",
    "        patientNumberDict[labelDict[patient]] += 1\n",
    "        if ~np.isnan(value):\n",
    "            arrayDict[labelDict[patient]].append(value)\n",
    "            \n",
    "    #create a tuple \n",
    "    dataT = []\n",
    "    labels = []\n",
    "    if sort:\n",
    "        classArray = sort\n",
    "    else:\n",
    "        classArray = arrayDict.keys()\n",
    "    for classType in classArray:\n",
    "        dataT.append(arrayDict[classType])\n",
    "        labels.append(classType)\n",
    "\n",
    "    # Create a figure instance\n",
    "    fig = plt.figure(1, figsize=(9, 6))\n",
    "\n",
    "    # Create an axes instance\n",
    "    ax = fig.add_subplot(111)\n",
    "    #n, bins, patches = ax.hist(dataT)\n",
    "\n",
    "    # Create the boxplot\n",
    "    bp = ax.boxplot(dataT)\n",
    "    ax.set_xticklabels(labels, rotation=90) #rotate these 45deg\n",
    "    #plt.ylabel('Permeability (relative to Parental pre-treatment)')\n",
    "    name = \"boxplot_%s.eps\"%(site)\n",
    "    plt.savefig(name,dpi=600,bbox_inches='tight',pad_inches=0.05, format='eps')\n",
    "    return patientNumberDict, arrayDict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110877110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ffbb790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110b40750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1108aa650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1102b9210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11088b710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dfbc3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110c42c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110e0da10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2RsQ6fWcy9Z7FBLCF7bHZW0z4RUSRstsbEUVK+EVEkRJ+EVGkhF9EFOn/A6ZB\njSe8HhXsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ffbbbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110e6f090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110c7c0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1119924d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1104be790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110890fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1107f9190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110888290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x111974d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110e361d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110581f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110581790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ff40690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110860f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x111946e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1104bac50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1105af350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110502950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110f78350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110f60c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1102b99d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1102b93d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ffa57d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1107b9450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1119d5dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ffbb0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fec6d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1108c2a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1109a9410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1109a9650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dd31490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# walk through every site that can map between CPTAC and HER2OE set and plot the data\n",
    "dfH = pd.DataFrame.from_csv('./data/sites_coOccDiff.csv', sep=',')\n",
    "sites_with_data = []\n",
    "arrayDict = {}\n",
    "for site in dfH['Gene_site']:\n",
    "    x = df[df['gene_site']== site]\n",
    "    if not x.empty:\n",
    "        sites_with_data.append(site)\n",
    "        fig = plt.figure(figsize=(5,5))\n",
    "        series = df[df['gene_site']==site][data_cols]\n",
    "        #patientNumberDict, arrayDict[gene_site] = boxplot_forProtein_withSort(labelDict, classDict, label_array, series, gene_site)\n",
    "        patientNumberDict, arrayDict[site] = boxplot_forProtein(labelDict, classDict, series, site, sort=classArray)\n",
    "        plt.title(site)\n",
    "        plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/knaegle/anaconda/lib/python2.7/site-packages/numpy/core/_methods.py:82: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  warnings.warn(\"Degrees of freedom <= 0 for slice\", RuntimeWarning)\n"
     ]
    }
   ],
   "source": [
    "# For a two-class label system, like HER2 positive and HER2 negative, calculate the p-values by \n",
    "#t-test (one-sided for an increase in the positive group)\n",
    "# Fisher's Exact test (one-sided for an enrichment in the positive group for having a measurement)\n",
    "\n",
    "pvalue_ttest = {}\n",
    "pvalue_fisher ={}\n",
    "\n",
    "class1 = 'Positive'\n",
    "class2 = 'Negative'\n",
    "\n",
    "\n",
    "posPatients = patientNumberDict[class1]\n",
    "negPatients = patientNumberDict[class2]\n",
    "posPatientDict = {}\n",
    "negPatientDict = {}\n",
    "#for gene_site in arrayDict.keys():\n",
    "for gene_site in sites_with_data:\n",
    "    Positives = arrayDict[gene_site][class1]\n",
    "    #other = np.concatenate([arrayDict[gene_site]['Negative'], arrayDict[gene_site]['Equivocal']], axis=0)\n",
    "    Negatives = arrayDict[gene_site][class2]\n",
    "    statistic, pvalue_ttest[gene_site] = stats.ttest_ind(Positives, Negatives, equal_var=False)\n",
    "    posPatientsWithValue = len(Positives)\n",
    "    negPatientsWithValue = len(Negatives)\n",
    "    conTable = [[posPatientsWithValue, negPatientsWithValue], [posPatients, negPatients]]\n",
    "    odds, pvalue_fisher[gene_site] = stats.fisher_exact(conTable, 'greater')\n",
    "    posPatientDict[gene_site] = posPatientsWithValue\n",
    "    negPatientDict[gene_site] = negPatientsWithValue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gene_site\t#posSamples \t #negSamples \t \tfisher_pvalue\tttest_pvalue\tCo-Occurrence Difference\n",
      "ANXA2_Y238\t16\t53\t0.60\t0.007\t0.317\n",
      "PTK2_Y397\t3\t6\t0.39\t0.423\t0.309\n",
      "PTPRA_Y798\t16\t52\t0.59\t0.412\t0.248\n",
      "CAV1_Y14\t0\t3\t1.00\tnan\t0.239\n",
      "PXN_Y118\t16\t51\t0.57\t0.076\t0.218\n",
      "PIK3R2_Y464\t12\t33\t0.44\t0.205\t0.214\n",
      "CTNND1_Y228\t6\t9\t0.16\t0.310\t0.210\n",
      "PTK2_Y576\t17\t56\t0.60\t0.122\t0.208\n",
      "DYRK1A_Y321\t18\t58\t0.58\t0.256\t0.203\n",
      "ARHGAP35_Y1105\t6\t16\t0.46\t0.380\t0.202\n",
      "GPRC5A_Y347\t16\t37\t0.27\t0.481\t0.201\n",
      "PRPF4B_Y849\t18\t58\t0.58\t0.027\t0.199\n",
      "ANXA2_Y24\t14\t43\t0.53\t0.196\t0.196\n",
      "ENO1_Y44\t18\t58\t0.58\t0.168\t0.192\n",
      "PTRF_Y308\t12\t37\t0.54\t0.401\t0.185\n",
      "ERBB2_Y1248\t10\t27\t0.43\t0.004\t0.182\n",
      "ACP1_Y132\t4\t8\t0.35\t0.363\t0.175\n",
      "SGK223_Y413\t3\t9\t0.59\t0.283\t0.169\n",
      "FAM59A_Y453\t3\t10\t0.64\t0.061\t0.166\n",
      "MPZL1_Y263\t14\t35\t0.34\t0.211\t0.153\n",
      "MAPK1_T185_Y187\t17\t56\t0.60\t0.269\t0.152\n",
      "GSK3A_Y279\t17\t55\t0.58\t0.416\t0.152\n",
      "ANXA2_Y30\t8\t27\t0.63\t0.243\t0.150\n",
      "BCAR1_Y234\t3\t18\t0.89\t0.121\t0.149\n",
      "CDK1_Y15\t18\t58\t0.58\t0.403\t0.147\n",
      "KIAA1217_Y393\t0\t3\t1.00\tnan\t0.146\n",
      "PKP3_Y176\t2\t6\t0.61\t0.062\t0.141\n",
      "EPHA2_Y772\t0\t0\t1.00\tnan\t0.135\n",
      "EGFR_Y1092\t3\t1\t0.05\tnan\t0.132\n",
      "STAT3_Y704\t17\t53\t0.54\t0.037\t0.127\n",
      "MAPK3_Y204\t17\t56\t0.60\t0.489\t0.127\n",
      "BCAR1_Y249\t10\t28\t0.46\t0.030\t0.126\n",
      "EGFR_Y1172\t4\t6\t0.23\t0.062\t0.126\n",
      "EGFR_Y1197\t1\t1\t0.43\tnan\t0.126\n",
      "MAPK14_Y182\t17\t56\t0.60\t0.278\t0.123\n",
      "STAT3_Y705\t18\t55\t0.52\t0.476\t0.122\n",
      "MAPK1_Y187\t17\t56\t0.60\t0.461\t0.121\n",
      "RIN1_Y36\t1\t1\t0.43\tnan\t0.114\n",
      "TNK2_Y859\t0\t2\t1.00\tnan\t0.109\n",
      "EPHA2_Y575\t0\t0\t1.00\tnan\t0.104\n"
     ]
    }
   ],
   "source": [
    "# Print the pvalues\n",
    "print \"gene_site\\t#posSamples \\t #negSamples \\t \\tfisher_pvalue\\tttest_pvalue\\tCo-Occurrence Difference\"\n",
    "for gene_site in sites_with_data:\n",
    "    if posPatientDict[gene_site]:\n",
    "        proportion = float(negPatientDict[gene_site])/negPatients/float(posPatientDict[gene_site])/posPatients\n",
    "    else:\n",
    "        proportion = 0\n",
    "        \n",
    "    print \"%s\\t%d\\t%d\\t%0.2f\\t%0.3f\\t%1.3f\"%(gene_site, posPatientDict[gene_site], negPatientDict[gene_site],\n",
    "                                            pvalue_fisher[gene_site], pvalue_ttest[gene_site]/2,\n",
    "                                            dfH[dfH['Gene_site']==gene_site]['co-occ diff']) #the t-test was two-sided, choose a one sided test\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Correlations between pSites across patients\n",
    "Since patient categories shows interesting results (like HER2 Y1248 variability with HER2 status), but correlation between CTNND1_Y228 and ERBB2_Y1248 based on staging, do a direct correlation of phosphorylation across patients (irrespective of patient categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#gene_sites_of_interest = gene_sites_24H_CTNND1_cluster\n",
    "gene_sites_of_interest = ['CTNND1_Y228', 'ERBB2_Y1248']\n",
    "selectData = returnSelectedDataByGeneSite(df, gene_sites_of_interest, data_cols)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "selectData.to_excel('cttnd1_erbb_data.xls') # cannot correlate the data, almost no patients have both, so have to group by patient type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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