jupyterNotebooks/cancerExploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Disease Classifier\n",
    "- Andrew E. Davidson\n",
    "- [aedavids@ucsc.edu](mailto:aedavids@edu?subject=lungCancerBinaryClassifier.ipynb)\n",
    "- ?? what is the attribution for the Kim lab\n",
    "\n",
    "## Overview\n",
    "We do not have enough lung cancer examples to train a binary classifier. The model did not \n",
    "learning anything. simply predicting all example to be 'not lung cancer' will have about 93% \n",
    "accuracy. Work around train  multinomial classifier on the category label.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO <ipython-input-1-bf4d12904823>:8 - <module>()] using logging configuration file:../src/logging.test.ini.json\n"
     ]
    }
   ],
   "source": [
    "import pathlib as pl\n",
    "\n",
    "import logging\n",
    "from   setupLogging import setupLogging\n",
    "configFilePath = setupLogging( default_path=pl.Path('../src/logging.ini.json'))\n",
    "configFilePath = setupLogging( default_path=pl.Path('../src/logging.test.ini.json'))\n",
    "logger = logging.getLogger(\"notebook\")\n",
    "logger.info(\"using logging configuration file:{}\".format(configFilePath))\n",
    "\n",
    "from dataUtilities.TCGA_Target_GTex import TCGA_Target_GTex\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "# import matplotlib.patches as mplpatches\n",
    "\n",
    "from matplotlibUtilities.histogram import Histogram as muHistogram\n",
    "import matplotlibUtilities.utilities as mu\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# https://songhuiming.github.io/pages/2017/04/02/jupyter-and-pandas-display/\n",
    "# display(HTML(' <span style=\"color:red\">the title is: <h1>Hello, world!</h1> </span>  '))\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "# fix random seed for reproducibility\n",
    "theMeaningOfLife = 42\n",
    "np.random.seed(theMeaningOfLife)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO utils.py:129 - _init_num_threads()] Note: NumExpr detected 16 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
      "[INFO utils.py:141 - _init_num_threads()] NumExpr defaulting to 8 threads.\n"
     ]
    }
   ],
   "source": [
    "dataDir = pl.Path(\"../data\")\n",
    "if not dataDir.exists() :\n",
    "    dataDir.mkdir()\n",
    "    \n",
    "loader = TCGA_Target_GTex(dataDir)\n",
    "# the first time you run will be slow, downloads data, and creates a tidy data set\n",
    "XDF, YDF = loader.loadTCGA_Target_GTex()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "YDF.columns\n",
      "Index(['category', 'disease', 'primary_site', 'sample_type', 'gender', 'study',\n",
      "       'tumor_normal'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(\"YDF.columns\\n{}\".format(YDF.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Breast Invasive Carcinoma              1212\n",
       "Kidney Clear Cell Carcinoma             603\n",
       "Lung Adenocarcinoma                     574\n",
       "Thyroid Carcinoma                       571\n",
       "Head & Neck Squamous Cell Carcinoma     564\n",
       "                                       ... \n",
       "Bladder                                   9\n",
       "Cervix - Ectocervix                       6\n",
       "Fallopian Tube                            5\n",
       "Kidney, Rhabdoid Tumor                    5\n",
       "Cervix - Endocervix                       4\n",
       "Name: category, Length: 93, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diseaseCountsSeries = YDF.loc[ :, 'category' ].value_counts()\n",
    "diseaseCountsSeries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:red\">AEDWIP: data?</span>\n",
    "Should we drop categories that do not have many example, like Bladder?\n",
    "\n",
    "All the GTEX example where known to be normal. Where biopsy from people being screen for cancer?\n",
    "\n",
    "Should we lump LungCancerRows ?\n",
    "\n",
    "what do the 'category', 'disease', and 'primary site' mean?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for lung relelated examples, are the primary_site and category features the same:? True\n",
      "for lung relelated examples, are the category  and disease features the same:? True\n",
      "number of diffs:0\n"
     ]
    }
   ],
   "source": [
    "lungCancerPSRowsSeries     = YDF['primary_site'].str.contains('lun', case=False)\n",
    "lungCancerCatRowsSeries    = YDF['category'    ].str.contains('lun', case=False)\n",
    "lungCancerDieaseRowsSeries = YDF['disease'     ].str.contains('lun', case=False)\n",
    "\n",
    "print(\"for lung relelated examples, are the primary_site and category features the same:? {}\"\\\n",
    "      .format(pd.Series.equals(lungCancerPSRowsSeries, lungCancerCatRowsSeries) ))\n",
    "\n",
    "print(\"for lung relelated examples, are the category  and disease features the same:? {}\"\\\n",
    "      .format(pd.Series.equals(lungCancerCatRowsSeries, lungCancerDieaseRowsSeries) ))\n",
    "\n",
    "def numDiffs(df1, df2):\n",
    "    d = lungCancerPSRowsSeries == lungCancerCatRowsSeries\n",
    "    numSame = d.sum()\n",
    "    n = lungCancerPSRowsSeries.size\n",
    "    numDiff = n - numSame\n",
    "    return numDiff\n",
    "    \n",
    "d = numDiffs( lungCancerPSRowsSeries, lungCancerCatRowsSeries )\n",
    "print(\"number of diffs:{}\".format(d))\n",
    "\n",
    "# use short names\n",
    "lungCancerPSRowsSeries = None\n",
    "lungCancerRows = lungCancerCatRowsSeries\n",
    "lungCancerCatRowsSeries = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>category</th>\n",
       "      <th>disease</th>\n",
       "      <th>primary_site</th>\n",
       "      <th>sample_type</th>\n",
       "      <th>gender</th>\n",
       "      <th>study</th>\n",
       "      <th>tumor_normal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GTEX-111CU-0326-SM-5GZXO</th>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Normal Tissue</td>\n",
       "      <td>Male</td>\n",
       "      <td>GTEX</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GTEX-111FC-1126-SM-5GZWU</th>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Normal Tissue</td>\n",
       "      <td>Male</td>\n",
       "      <td>GTEX</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GTEX-111VG-0726-SM-5GIDC</th>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Normal Tissue</td>\n",
       "      <td>Male</td>\n",
       "      <td>GTEX</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GTEX-111YS-0626-SM-5GZXV</th>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Normal Tissue</td>\n",
       "      <td>Male</td>\n",
       "      <td>GTEX</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GTEX-1122O-0126-SM-5GICA</th>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Normal Tissue</td>\n",
       "      <td>Female</td>\n",
       "      <td>GTEX</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-O2-A52V-01</th>\n",
       "      <td>Lung Squamous Cell Carcinoma</td>\n",
       "      <td>Lung Squamous Cell Carcinoma</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Primary Tumor</td>\n",
       "      <td>Female</td>\n",
       "      <td>TCGA</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-O2-A52W-01</th>\n",
       "      <td>Lung Squamous Cell Carcinoma</td>\n",
       "      <td>Lung Squamous Cell Carcinoma</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Primary Tumor</td>\n",
       "      <td>Male</td>\n",
       "      <td>TCGA</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-O2-A5IB-01</th>\n",
       "      <td>Lung Squamous Cell Carcinoma</td>\n",
       "      <td>Lung Squamous Cell Carcinoma</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Primary Tumor</td>\n",
       "      <td>Female</td>\n",
       "      <td>TCGA</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-S2-AA1A-01</th>\n",
       "      <td>Lung Adenocarcinoma</td>\n",
       "      <td>Lung Adenocarcinoma</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Primary Tumor</td>\n",
       "      <td>Female</td>\n",
       "      <td>TCGA</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-XC-AA0X-01</th>\n",
       "      <td>Lung Squamous Cell Carcinoma</td>\n",
       "      <td>Lung Squamous Cell Carcinoma</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Primary Tumor</td>\n",
       "      <td>Female</td>\n",
       "      <td>TCGA</td>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1410 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              category  \\\n",
       "id                                                       \n",
       "GTEX-111CU-0326-SM-5GZXO                          Lung   \n",
       "GTEX-111FC-1126-SM-5GZWU                          Lung   \n",
       "GTEX-111VG-0726-SM-5GIDC                          Lung   \n",
       "GTEX-111YS-0626-SM-5GZXV                          Lung   \n",
       "GTEX-1122O-0126-SM-5GICA                          Lung   \n",
       "...                                                ...   \n",
       "TCGA-O2-A52V-01           Lung Squamous Cell Carcinoma   \n",
       "TCGA-O2-A52W-01           Lung Squamous Cell Carcinoma   \n",
       "TCGA-O2-A5IB-01           Lung Squamous Cell Carcinoma   \n",
       "TCGA-S2-AA1A-01                    Lung Adenocarcinoma   \n",
       "TCGA-XC-AA0X-01           Lung Squamous Cell Carcinoma   \n",
       "\n",
       "                                               disease primary_site  \\\n",
       "id                                                                    \n",
       "GTEX-111CU-0326-SM-5GZXO                          Lung         Lung   \n",
       "GTEX-111FC-1126-SM-5GZWU                          Lung         Lung   \n",
       "GTEX-111VG-0726-SM-5GIDC                          Lung         Lung   \n",
       "GTEX-111YS-0626-SM-5GZXV                          Lung         Lung   \n",
       "GTEX-1122O-0126-SM-5GICA                          Lung         Lung   \n",
       "...                                                ...          ...   \n",
       "TCGA-O2-A52V-01           Lung Squamous Cell Carcinoma         Lung   \n",
       "TCGA-O2-A52W-01           Lung Squamous Cell Carcinoma         Lung   \n",
       "TCGA-O2-A5IB-01           Lung Squamous Cell Carcinoma         Lung   \n",
       "TCGA-S2-AA1A-01                    Lung Adenocarcinoma         Lung   \n",
       "TCGA-XC-AA0X-01           Lung Squamous Cell Carcinoma         Lung   \n",
       "\n",
       "                            sample_type  gender study tumor_normal  \n",
       "id                                                                  \n",
       "GTEX-111CU-0326-SM-5GZXO  Normal Tissue    Male  GTEX       Normal  \n",
       "GTEX-111FC-1126-SM-5GZWU  Normal Tissue    Male  GTEX       Normal  \n",
       "GTEX-111VG-0726-SM-5GIDC  Normal Tissue    Male  GTEX       Normal  \n",
       "GTEX-111YS-0626-SM-5GZXV  Normal Tissue    Male  GTEX       Normal  \n",
       "GTEX-1122O-0126-SM-5GICA  Normal Tissue  Female  GTEX       Normal  \n",
       "...                                 ...     ...   ...          ...  \n",
       "TCGA-O2-A52V-01           Primary Tumor  Female  TCGA        Tumor  \n",
       "TCGA-O2-A52W-01           Primary Tumor    Male  TCGA        Tumor  \n",
       "TCGA-O2-A5IB-01           Primary Tumor  Female  TCGA        Tumor  \n",
       "TCGA-S2-AA1A-01           Primary Tumor  Female  TCGA        Tumor  \n",
       "TCGA-XC-AA0X-01           Primary Tumor  Female  TCGA        Tumor  \n",
       "\n",
       "[1410 rows x 7 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Do categories contain normal and cancer examples\n",
    "lungMetaDF = YDF[lungCancerRows]\n",
    "lungMetaDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index(['disease', 'primary_site', 'sample_type', 'gender', 'study',\n",
      "       'tumor_normal'],\n",
      "      dtype='object')\n",
      "Index(['Lung', 'Lung Adenocarcinoma', 'Lung Squamous Cell Carcinoma'], dtype='object', name='category')\n",
      "[[288 288 288 288 288 288]\n",
      " [574 574 574 574 574 574]\n",
      " [548 548 548 548 548 548]]\n",
      "[288 574 548]\n"
     ]
    }
   ],
   "source": [
    "c = lungMetaDF.groupby('category').count()\n",
    "print(type(c))\n",
    "print(c.columns)\n",
    "print(c.index)\n",
    "print(c.values)\n",
    "print(c.iloc[:, 0].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "   figureWidthInInches = 12\n",
    "figureHeightInInches = 5\n",
    "fig = plt.figure(figsize=(figureWidthInInches,figureHeightInInches))\n",
    "\n",
    "panelWidthInInches = figureWidthInInches\n",
    "panelHeightInInches = figureHeightInInches\n",
    "leftRelativeSize = 0\n",
    "bottomRelativeSize = 1\n",
    "panel = mu.createPanel(fig, panelWidthInInches, panelHeightInInches,\n",
    "                leftRelativeSize, bottomRelativeSize)\n",
    "    \n",
    "barNames = YDF.groupby('category')['disease'].count().index.to_numpy()\n",
    "barHeights = YDF.groupby('category')['disease'].count().to_numpy()\n",
    "xticksIdxList = [i for  i,str in enumerate(barNames) if 'Lung' in str ]\n",
    "\n",
    "mu.fancyBarGraph(panel, barHeights, barNames, xticksIdxList)\n",
    "\n",
    "panel.set_xlabel(\"Disease Categories\")\n",
    "panel.set_ylabel(\"Counts\")\n",
    "panel.text(60, 1500, \"categories contain normal and cancer examples\")\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Summary\n",
    "- 93 different disease categories\n",
    "- many only have a few examples\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color:red\">not all disease categories have normal and cancer examples</span>\n",
    "only 25 shared categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[318 197 128 207 118]\n",
      "['Adipose - Subcutaneous' 'Adipose - Visceral (Omentum)' 'Adrenal Gland'\n",
      " 'Artery - Aorta' 'Artery - Coronary']\n",
      "80\n"
     ]
    }
   ],
   "source": [
    "# YDF.loc[ YDF['tumor_normal'] == 'Normal']\n",
    "normalDF = YDF.loc[ YDF['tumor_normal'] == 'Normal']\n",
    "normalDiseaseCounts = normalDF.groupby('category')['disease'].count().to_numpy()\n",
    "print(normalDiseaseCounts[0:5])\n",
    "normalBarNames = normalDF.groupby('category')['disease'].count().index.to_numpy()\n",
    "print(normalBarNames[0:5])\n",
    "print(len(normalBarNames))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[194 369  32  77 407]\n",
      "['Acute Lymphoblastic Leukemia' 'Acute Myeloid Leukemia'\n",
      " 'Acute Myeloid Leukemia, Induction Failure Subproject'\n",
      " 'Adrenocortical Cancer' 'Bladder Urothelial Carcinoma']\n",
      "38\n"
     ]
    }
   ],
   "source": [
    "tumorDF = YDF.loc[ YDF['tumor_normal'] == 'Tumor']\n",
    "tumorDiseaseCounts = tumorDF.groupby('category')['disease'].count().to_numpy()\n",
    "print(tumorDiseaseCounts[0:5])\n",
    "tumorBarNames = tumorDF.groupby('category')['disease'].count().index.to_numpy()\n",
    "print(tumorBarNames[0:5])\n",
    "print(len(tumorBarNames))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['Bladder Urothelial Carcinoma', 'Breast Invasive Carcinoma',\n",
       "       'Cervical & Endocervical Cancer', 'Cholangiocarcinoma',\n",
       "       'Colon Adenocarcinoma', 'Esophageal Carcinoma',\n",
       "       'Glioblastoma Multiforme', 'Head & Neck Squamous Cell Carcinoma',\n",
       "       'Kidney Chromophobe', 'Kidney Clear Cell Carcinoma',\n",
       "       'Kidney Papillary Cell Carcinoma',\n",
       "       'Liver Hepatocellular Carcinoma', 'Lung Adenocarcinoma',\n",
       "       'Lung Squamous Cell Carcinoma', 'Pancreatic Adenocarcinoma',\n",
       "       'Pheochromocytoma & Paraganglioma', 'Prostate Adenocarcinoma',\n",
       "       'Rectum Adenocarcinoma', 'Sarcoma', 'Skin Cutaneous Melanoma',\n",
       "       'Stomach Adenocarcinoma', 'Thymoma', 'Thyroid Carcinoma',\n",
       "       'Uterine Corpus Endometrioid Carcinoma', 'Wilms Tumor'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sharedDiseaseTypes = np.intersect1d(normalBarNames, tumorBarNames)\n",
    "print(len(sharedDiseaseTypes))\n",
    "sharedDiseaseTypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select data set\n",
    "def selectSharedData(df, sharedDiseaseTypes):\n",
    "    shareDiseaseDFRows = df['disease'].isin(sharedDiseaseTypes)\n",
    "    sharedDF = df.loc[ shareDiseaseDFRows]\n",
    "    sharedBarNames = sharedDF.groupby('category')['disease'].count().index.to_numpy()\n",
    "    sharedBarHeights = sharedDF.groupby('category')['disease'].count().to_numpy()\n",
    "    sharedXticksIdxList = [i for  i,str in enumerate(sharedBarNames) if 'Lung' in str ]\n",
    "    return (sharedDF, sharedBarNames, sharedBarHeights, sharedXticksIdxList)\n",
    "\n",
    "tumorBarPlotData = selectSharedData(tumorDF, sharedDiseaseTypes)\n",
    "normalBarPlotData = selectSharedData(normalDF, sharedDiseaseTypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figureWidthInInches = 10\n",
    "figureHeightInInches = 5\n",
    "fig = plt.figure(figsize=(figureWidthInInches,figureHeightInInches))\n",
    "\n",
    "panelWidthInInches = figureWidthInInches\n",
    "panelHeightInInches = figureHeightInInches\n",
    "leftRelativeSize = 0\n",
    "bottomRelativeSize = 1\n",
    "panel = mu.createPanel(fig, panelWidthInInches, panelHeightInInches,\n",
    "                leftRelativeSize, bottomRelativeSize)\n",
    "  \n",
    "normalColor = (255,0,0)\n",
    "alpha = 0.3\n",
    "nDF, sharedNBarNames, sharedNBarHeights, sharedNXticksIdxList = normalBarPlotData\n",
    "# display all desease names\n",
    "xticksIdxList = list(range(len(sharedNBarNames)))\n",
    "mu.fancyBarGraph(panel, sharedNBarHeights, sharedNBarNames, xticksIdxList,\n",
    "             RGBTuple=normalColor, alpha=alpha)\n",
    "\n",
    "tumorColor = (0,0,255)\n",
    "sharedTDF, sharedTBarNames, sharedTBarHeights, sharedTrXticksIdxList = tumorBarPlotData\n",
    "mu.fancyBarGraph(panel, sharedTBarHeights, sharedTBarNames, RGBTuple=tumorColor, alpha=alpha)\n",
    "\n",
    "panel.set_xlabel(\"Disease Categories\")\n",
    "panel.set_ylabel(\"Counts\")\n",
    "#panel.text(60, 1500, \"Diseases with both normal and tumor examples\")\n",
    "panel.set_title(\"Diseases with both normal and tumor examples\")\n",
    "\n",
    "colorMapTuples = [normalColor, tumorColor]\n",
    "labels         = ['normal',    'tumor']\n",
    "mu.addLegend(panel, colorMapTuples, labels, alpha=alpha)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
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     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
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   "types_to_exclude": [
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