BH_subgroup2_clustermap.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This script takes relative abundances of all genes and groups them into the KEGG 'Subgroup2' category. All categories are then plotted in a heat map using the Seaborn clustermap function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os as os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Navigate to directory containing read counts file\n",
    "os.chdir(\"/Users/nastassia.patin/Desktop/Projects/BlueHole/ISMEJ_revisions/\")\n",
    "path = \"60Ms_vs_deeps_KOs_readcounts.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Group</th>\n",
       "      <th>Subgroup</th>\n",
       "      <th>Subgroup2</th>\n",
       "      <th>KO</th>\n",
       "      <th>Function</th>\n",
       "      <th>ReadCounts</th>\n",
       "      <th>Sample</th>\n",
       "      <th>GE</th>\n",
       "      <th>ReadCounts_Norm</th>\n",
       "      <th>dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Metabolism</td>\n",
       "      <td>Carbohydrate metabolism</td>\n",
       "      <td>Glycolysis / Gluconeogenesis [PATH:ko00010]</td>\n",
       "      <td>K00845</td>\n",
       "      <td>glk; glucokinase [EC:2.7.1.2]</td>\n",
       "      <td>629</td>\n",
       "      <td>BH091960M</td>\n",
       "      <td>2401.2</td>\n",
       "      <td>0.261952</td>\n",
       "      <td>60Ms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Metabolism</td>\n",
       "      <td>Carbohydrate metabolism</td>\n",
       "      <td>Galactose metabolism [PATH:ko00052]</td>\n",
       "      <td>K00845</td>\n",
       "      <td>glk; glucokinase [EC:2.7.1.2]</td>\n",
       "      <td>629</td>\n",
       "      <td>BH091960M</td>\n",
       "      <td>2401.2</td>\n",
       "      <td>0.261952</td>\n",
       "      <td>60Ms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Metabolism</td>\n",
       "      <td>Carbohydrate metabolism</td>\n",
       "      <td>Starch and sucrose metabolism [PATH:ko00500]</td>\n",
       "      <td>K00845</td>\n",
       "      <td>glk; glucokinase [EC:2.7.1.2]</td>\n",
       "      <td>629</td>\n",
       "      <td>BH091960M</td>\n",
       "      <td>2401.2</td>\n",
       "      <td>0.261952</td>\n",
       "      <td>60Ms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Metabolism</td>\n",
       "      <td>Carbohydrate metabolism</td>\n",
       "      <td>Amino sugar and nucleotide sugar metabolism [P...</td>\n",
       "      <td>K00845</td>\n",
       "      <td>glk; glucokinase [EC:2.7.1.2]</td>\n",
       "      <td>629</td>\n",
       "      <td>BH091960M</td>\n",
       "      <td>2401.2</td>\n",
       "      <td>0.261952</td>\n",
       "      <td>60Ms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Metabolism</td>\n",
       "      <td>Biosynthesis of other secondary metabolites</td>\n",
       "      <td>Streptomycin biosynthesis [PATH:ko00521]</td>\n",
       "      <td>K00845</td>\n",
       "      <td>glk; glucokinase [EC:2.7.1.2]</td>\n",
       "      <td>629</td>\n",
       "      <td>BH091960M</td>\n",
       "      <td>2401.2</td>\n",
       "      <td>0.261952</td>\n",
       "      <td>60Ms</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Group                                     Subgroup  \\\n",
       "0  Metabolism                      Carbohydrate metabolism   \n",
       "1  Metabolism                      Carbohydrate metabolism   \n",
       "2  Metabolism                      Carbohydrate metabolism   \n",
       "3  Metabolism                      Carbohydrate metabolism   \n",
       "4  Metabolism  Biosynthesis of other secondary metabolites   \n",
       "\n",
       "                                           Subgroup2      KO  \\\n",
       "0        Glycolysis / Gluconeogenesis [PATH:ko00010]  K00845   \n",
       "1                Galactose metabolism [PATH:ko00052]  K00845   \n",
       "2       Starch and sucrose metabolism [PATH:ko00500]  K00845   \n",
       "3  Amino sugar and nucleotide sugar metabolism [P...  K00845   \n",
       "4           Streptomycin biosynthesis [PATH:ko00521]  K00845   \n",
       "\n",
       "                         Function  ReadCounts     Sample      GE  \\\n",
       "0   glk; glucokinase [EC:2.7.1.2]         629  BH091960M  2401.2   \n",
       "1   glk; glucokinase [EC:2.7.1.2]         629  BH091960M  2401.2   \n",
       "2   glk; glucokinase [EC:2.7.1.2]         629  BH091960M  2401.2   \n",
       "3   glk; glucokinase [EC:2.7.1.2]         629  BH091960M  2401.2   \n",
       "4   glk; glucokinase [EC:2.7.1.2]         629  BH091960M  2401.2   \n",
       "\n",
       "   ReadCounts_Norm dataset  \n",
       "0         0.261952    60Ms  \n",
       "1         0.261952    60Ms  \n",
       "2         0.261952    60Ms  \n",
       "3         0.261952    60Ms  \n",
       "4         0.261952    60Ms  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(path)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exclude irrelevant KEGG categories\n",
    "df = df.loc[(df['Group'] != 'Human Diseases') & \n",
    "               (df['Group'] != 'Brite Hierarchies') & \n",
    "               (df['Group'] != 'Organismal Systems') &\n",
    "               (df['Subgroup'] != 'Cellular community - eukaryotes')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Group</th>\n",
       "      <th>Subgroup</th>\n",
       "      <th>Subgroup2</th>\n",
       "      <th>KO</th>\n",
       "      <th>Function</th>\n",
       "      <th>ReadCounts</th>\n",
       "      <th>Sample</th>\n",
       "      <th>GE</th>\n",
       "      <th>ReadCounts_Norm</th>\n",
       "      <th>dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Metabolism</td>\n",
       "      <td>Carbohydrate metabolism</td>\n",
       "      <td>Glycolysis / Gluconeogenesis</td>\n",
       "      <td>K00845</td>\n",
       "      <td>glk; glucokinase [EC:2.7.1.2]</td>\n",
       "      <td>629</td>\n",
       "      <td>BH091960M</td>\n",
       "      <td>2401.2</td>\n",
       "      <td>0.261952</td>\n",
       "      <td>60Ms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Metabolism</td>\n",
       "      <td>Carbohydrate metabolism</td>\n",
       "      <td>Galactose metabolism</td>\n",
       "      <td>K00845</td>\n",
       "      <td>glk; glucokinase [EC:2.7.1.2]</td>\n",
       "      <td>629</td>\n",
       "      <td>BH091960M</td>\n",
       "      <td>2401.2</td>\n",
       "      <td>0.261952</td>\n",
       "      <td>60Ms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Metabolism</td>\n",
       "      <td>Carbohydrate metabolism</td>\n",
       "      <td>Starch and sucrose metabolism</td>\n",
       "      <td>K00845</td>\n",
       "      <td>glk; glucokinase [EC:2.7.1.2]</td>\n",
       "      <td>629</td>\n",
       "      <td>BH091960M</td>\n",
       "      <td>2401.2</td>\n",
       "      <td>0.261952</td>\n",
       "      <td>60Ms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Metabolism</td>\n",
       "      <td>Carbohydrate metabolism</td>\n",
       "      <td>Amino sugar and nucleotide sugar metabolism</td>\n",
       "      <td>K00845</td>\n",
       "      <td>glk; glucokinase [EC:2.7.1.2]</td>\n",
       "      <td>629</td>\n",
       "      <td>BH091960M</td>\n",
       "      <td>2401.2</td>\n",
       "      <td>0.261952</td>\n",
       "      <td>60Ms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Metabolism</td>\n",
       "      <td>Biosynthesis of other secondary metabolites</td>\n",
       "      <td>Streptomycin biosynthesis</td>\n",
       "      <td>K00845</td>\n",
       "      <td>glk; glucokinase [EC:2.7.1.2]</td>\n",
       "      <td>629</td>\n",
       "      <td>BH091960M</td>\n",
       "      <td>2401.2</td>\n",
       "      <td>0.261952</td>\n",
       "      <td>60Ms</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Group                                     Subgroup  \\\n",
       "0  Metabolism                      Carbohydrate metabolism   \n",
       "1  Metabolism                      Carbohydrate metabolism   \n",
       "2  Metabolism                      Carbohydrate metabolism   \n",
       "3  Metabolism                      Carbohydrate metabolism   \n",
       "4  Metabolism  Biosynthesis of other secondary metabolites   \n",
       "\n",
       "                                      Subgroup2      KO  \\\n",
       "0                 Glycolysis / Gluconeogenesis   K00845   \n",
       "1                         Galactose metabolism   K00845   \n",
       "2                Starch and sucrose metabolism   K00845   \n",
       "3  Amino sugar and nucleotide sugar metabolism   K00845   \n",
       "4                    Streptomycin biosynthesis   K00845   \n",
       "\n",
       "                         Function  ReadCounts     Sample      GE  \\\n",
       "0   glk; glucokinase [EC:2.7.1.2]         629  BH091960M  2401.2   \n",
       "1   glk; glucokinase [EC:2.7.1.2]         629  BH091960M  2401.2   \n",
       "2   glk; glucokinase [EC:2.7.1.2]         629  BH091960M  2401.2   \n",
       "3   glk; glucokinase [EC:2.7.1.2]         629  BH091960M  2401.2   \n",
       "4   glk; glucokinase [EC:2.7.1.2]         629  BH091960M  2401.2   \n",
       "\n",
       "   ReadCounts_Norm dataset  \n",
       "0         0.261952    60Ms  \n",
       "1         0.261952    60Ms  \n",
       "2         0.261952    60Ms  \n",
       "3         0.261952    60Ms  \n",
       "4         0.261952    60Ms  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Edit 'Subgroup2' annotations to just the category name\n",
    "df[['Subgroup2', 'PATH']] = df['Subgroup2'].str.split(\"[\", n=1, expand=True)\n",
    "df2 = df.drop('PATH', axis=1)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the data frame by dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_G1 = df2.loc[(df2['dataset'] == '60Ms')]\n",
    "df_G2 = df2.loc[(df2['dataset'] == 'deeps')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_G1_sub = df_G1[['KO','Sample','ReadCounts_Norm']]\n",
    "df_G2_sub = df_G2[['KO','Sample','ReadCounts_Norm']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Concatenate the two data sets while maintaining the information as to which row is from which data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>KO</th>\n",
       "      <th>Sample</th>\n",
       "      <th>ReadCounts_Norm</th>\n",
       "      <th>dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>K09991</td>\n",
       "      <td>BH091960M</td>\n",
       "      <td>0.000833</td>\n",
       "      <td>60Ms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>K13588</td>\n",
       "      <td>BH091960M</td>\n",
       "      <td>0.000833</td>\n",
       "      <td>60Ms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>K19411</td>\n",
       "      <td>BH091960M</td>\n",
       "      <td>0.000833</td>\n",
       "      <td>60Ms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>K15761</td>\n",
       "      <td>BH091960M</td>\n",
       "      <td>0.001249</td>\n",
       "      <td>60Ms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>K08973</td>\n",
       "      <td>BH37</td>\n",
       "      <td>0.001520</td>\n",
       "      <td>60Ms</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       KO     Sample  ReadCounts_Norm dataset\n",
       "2  K09991  BH091960M         0.000833    60Ms\n",
       "3  K13588  BH091960M         0.000833    60Ms\n",
       "4  K19411  BH091960M         0.000833    60Ms\n",
       "5  K15761  BH091960M         0.001249    60Ms\n",
       "6  K08973       BH37         0.001520    60Ms"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concat = pd.concat([df_G1_sub.assign(dataset='60Ms'), df_G2_sub.assign(dataset='Deeps')])\n",
    "concat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>KO</th>\n",
       "      <th>K00003</th>\n",
       "      <th>K00005</th>\n",
       "      <th>K00009</th>\n",
       "      <th>K00010</th>\n",
       "      <th>K00012</th>\n",
       "      <th>K00013</th>\n",
       "      <th>K00014</th>\n",
       "      <th>K00015</th>\n",
       "      <th>K00016</th>\n",
       "      <th>K00018</th>\n",
       "      <th>...</th>\n",
       "      <th>K23492</th>\n",
       "      <th>K23508</th>\n",
       "      <th>K23514</th>\n",
       "      <th>K23518</th>\n",
       "      <th>K23535</th>\n",
       "      <th>K23536</th>\n",
       "      <th>K23537</th>\n",
       "      <th>K23548</th>\n",
       "      <th>K23549</th>\n",
       "      <th>K23573</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sample</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>BH37</th>\n",
       "      <td>1.358456</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.308464</td>\n",
       "      <td>0.512080</td>\n",
       "      <td>0.743048</td>\n",
       "      <td>0.761282</td>\n",
       "      <td>0.057742</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.217292</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.945145</td>\n",
       "      <td>0.791673</td>\n",
       "      <td>1.186750</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BH091960M</th>\n",
       "      <td>0.592620</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.084541</td>\n",
       "      <td>0.534733</td>\n",
       "      <td>0.415209</td>\n",
       "      <td>0.906630</td>\n",
       "      <td>0.034150</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.285274</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016242</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.032067</td>\n",
       "      <td>0.052057</td>\n",
       "      <td>0.853323</td>\n",
       "      <td>1.465517</td>\n",
       "      <td>0.294436</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BH51</th>\n",
       "      <td>1.137606</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.072032</td>\n",
       "      <td>0.444610</td>\n",
       "      <td>1.043219</td>\n",
       "      <td>1.140089</td>\n",
       "      <td>0.094386</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.057129</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.074516</td>\n",
       "      <td>0.074516</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.146547</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.176354</td>\n",
       "      <td>1.266766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BH091995M</th>\n",
       "      <td>0.840408</td>\n",
       "      <td>0.053743</td>\n",
       "      <td>0.031155</td>\n",
       "      <td>0.031155</td>\n",
       "      <td>1.314744</td>\n",
       "      <td>0.498481</td>\n",
       "      <td>0.774204</td>\n",
       "      <td>0.056858</td>\n",
       "      <td>0.014799</td>\n",
       "      <td>0.071657</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022587</td>\n",
       "      <td>0.116832</td>\n",
       "      <td>0.038165</td>\n",
       "      <td>0.147987</td>\n",
       "      <td>0.153439</td>\n",
       "      <td>0.232884</td>\n",
       "      <td>0.102033</td>\n",
       "      <td>0.131630</td>\n",
       "      <td>2.562505</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 3374 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "KO           K00003    K00005    K00009    K00010    K00012    K00013  \\\n",
       "Sample                                                                  \n",
       "BH37       1.358456  0.000000  0.000000  0.308464  0.512080  0.743048   \n",
       "BH091960M  0.592620  0.000000  0.000000  0.084541  0.534733  0.415209   \n",
       "BH51       1.137606  0.000000  0.000000  0.072032  0.444610  1.043219   \n",
       "BH091995M  0.840408  0.053743  0.031155  0.031155  1.314744  0.498481   \n",
       "\n",
       "KO           K00014    K00015    K00016    K00018  ...    K23492    K23508  \\\n",
       "Sample                                             ...                       \n",
       "BH37       0.761282  0.057742  0.000000  0.217292  ...  0.000000  0.000000   \n",
       "BH091960M  0.906630  0.034150  0.000000  0.285274  ...  0.016242  0.000000   \n",
       "BH51       1.140089  0.094386  0.000000  0.057129  ...  0.000000  0.000000   \n",
       "BH091995M  0.774204  0.056858  0.014799  0.071657  ...  0.000000  0.022587   \n",
       "\n",
       "KO           K23514    K23518    K23535    K23536    K23537    K23548  \\\n",
       "Sample                                                                  \n",
       "BH37       0.000000  0.000000  0.945145  0.791673  1.186750  0.000000   \n",
       "BH091960M  0.032067  0.052057  0.853323  1.465517  0.294436  0.000000   \n",
       "BH51       0.074516  0.074516  0.000000  0.000000  0.146547  0.000000   \n",
       "BH091995M  0.116832  0.038165  0.147987  0.153439  0.232884  0.102033   \n",
       "\n",
       "KO           K23549    K23573  \n",
       "Sample                         \n",
       "BH37       0.000000  0.000000  \n",
       "BH091960M  0.000000  0.018741  \n",
       "BH51       0.176354  1.266766  \n",
       "BH091995M  0.131630  2.562505  \n",
       "\n",
       "[4 rows x 3374 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reformat data frame for the heat map\n",
    "df4 = pd.pivot_table(data=concat, index='Sample', values='ReadCounts_Norm', columns='KO')\n",
    "df4 = df4.fillna(0).reindex([\"BH37\",\"BH091960M\",\"BH51\",\"BH091995M\"])\n",
    "df4.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate the fold difference between sample pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fold_difference(a, b, c, d):\n",
    "    diff = ((a + b)/2) / ((c + d)/2)\n",
    "    return(diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-117-00ed9ada64a9>:2: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  df4 = df4.append(pd.Series(name='Average'))\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>KO</th>\n",
       "      <th>K00003</th>\n",
       "      <th>K00005</th>\n",
       "      <th>K00009</th>\n",
       "      <th>K00010</th>\n",
       "      <th>K00012</th>\n",
       "      <th>K00013</th>\n",
       "      <th>K00014</th>\n",
       "      <th>K00015</th>\n",
       "      <th>K00016</th>\n",
       "      <th>K00018</th>\n",
       "      <th>...</th>\n",
       "      <th>K23492</th>\n",
       "      <th>K23508</th>\n",
       "      <th>K23514</th>\n",
       "      <th>K23518</th>\n",
       "      <th>K23535</th>\n",
       "      <th>K23536</th>\n",
       "      <th>K23537</th>\n",
       "      <th>K23548</th>\n",
       "      <th>K23549</th>\n",
       "      <th>K23573</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sample</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>BH37</th>\n",
       "      <td>1.358456</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>0.308464</td>\n",
       "      <td>0.512080</td>\n",
       "      <td>0.743048</td>\n",
       "      <td>0.761282</td>\n",
       "      <td>0.057742</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>0.217292</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>9.451451e-01</td>\n",
       "      <td>7.916730e-01</td>\n",
       "      <td>1.186750</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BH091960M</th>\n",
       "      <td>0.592620</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>0.084541</td>\n",
       "      <td>0.534733</td>\n",
       "      <td>0.415209</td>\n",
       "      <td>0.906630</td>\n",
       "      <td>0.034150</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>0.285274</td>\n",
       "      <td>...</td>\n",
       "      <td>1.624188e-02</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>3.206730e-02</td>\n",
       "      <td>5.205730e-02</td>\n",
       "      <td>8.533233e-01</td>\n",
       "      <td>1.465517e+00</td>\n",
       "      <td>0.294436</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.874063e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BH51</th>\n",
       "      <td>1.137606</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>0.072032</td>\n",
       "      <td>0.444610</td>\n",
       "      <td>1.043219</td>\n",
       "      <td>1.140089</td>\n",
       "      <td>0.094386</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>0.057129</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>7.451565e-02</td>\n",
       "      <td>7.451565e-02</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>0.146547</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.763537e-01</td>\n",
       "      <td>1.266766e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BH091995M</th>\n",
       "      <td>0.840408</td>\n",
       "      <td>5.374250e-02</td>\n",
       "      <td>3.115507e-02</td>\n",
       "      <td>0.031155</td>\n",
       "      <td>1.314744</td>\n",
       "      <td>0.498481</td>\n",
       "      <td>0.774204</td>\n",
       "      <td>0.056858</td>\n",
       "      <td>1.479866e-02</td>\n",
       "      <td>0.071657</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>2.258743e-02</td>\n",
       "      <td>1.168315e-01</td>\n",
       "      <td>3.816497e-02</td>\n",
       "      <td>1.479866e-01</td>\n",
       "      <td>1.534387e-01</td>\n",
       "      <td>0.232884</td>\n",
       "      <td>1.020329e-01</td>\n",
       "      <td>1.316302e-01</td>\n",
       "      <td>2.562505e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average</th>\n",
       "      <td>1.029561</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>0.155012</td>\n",
       "      <td>0.497141</td>\n",
       "      <td>0.733825</td>\n",
       "      <td>0.936001</td>\n",
       "      <td>0.062093</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>0.186565</td>\n",
       "      <td>...</td>\n",
       "      <td>5.413960e-03</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>3.552765e-02</td>\n",
       "      <td>4.219098e-02</td>\n",
       "      <td>5.994895e-01</td>\n",
       "      <td>7.523967e-01</td>\n",
       "      <td>0.542578</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>5.878457e-02</td>\n",
       "      <td>4.285022e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fold difference</th>\n",
       "      <td>0.986382</td>\n",
       "      <td>3.721449e-08</td>\n",
       "      <td>6.419500e-08</td>\n",
       "      <td>3.808671</td>\n",
       "      <td>0.594998</td>\n",
       "      <td>0.751286</td>\n",
       "      <td>0.871294</td>\n",
       "      <td>0.607570</td>\n",
       "      <td>1.351474e-07</td>\n",
       "      <td>3.902356</td>\n",
       "      <td>...</td>\n",
       "      <td>8.120940e+06</td>\n",
       "      <td>8.854482e-08</td>\n",
       "      <td>1.675870e-01</td>\n",
       "      <td>4.619899e-01</td>\n",
       "      <td>1.215291e+01</td>\n",
       "      <td>1.471069e+01</td>\n",
       "      <td>3.903696</td>\n",
       "      <td>1.960153e-08</td>\n",
       "      <td>6.493846e-09</td>\n",
       "      <td>4.894047e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 3374 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "KO                 K00003        K00005        K00009    K00010    K00012  \\\n",
       "Sample                                                                      \n",
       "BH37             1.358456  1.000000e-09  1.000000e-09  0.308464  0.512080   \n",
       "BH091960M        0.592620  1.000000e-09  1.000000e-09  0.084541  0.534733   \n",
       "BH51             1.137606  1.000000e-09  1.000000e-09  0.072032  0.444610   \n",
       "BH091995M        0.840408  5.374250e-02  3.115507e-02  0.031155  1.314744   \n",
       "Average          1.029561  1.000000e-09  1.000000e-09  0.155012  0.497141   \n",
       "Fold difference  0.986382  3.721449e-08  6.419500e-08  3.808671  0.594998   \n",
       "\n",
       "KO                 K00013    K00014    K00015        K00016    K00018  ...  \\\n",
       "Sample                                                                 ...   \n",
       "BH37             0.743048  0.761282  0.057742  1.000000e-09  0.217292  ...   \n",
       "BH091960M        0.415209  0.906630  0.034150  1.000000e-09  0.285274  ...   \n",
       "BH51             1.043219  1.140089  0.094386  1.000000e-09  0.057129  ...   \n",
       "BH091995M        0.498481  0.774204  0.056858  1.479866e-02  0.071657  ...   \n",
       "Average          0.733825  0.936001  0.062093  1.000000e-09  0.186565  ...   \n",
       "Fold difference  0.751286  0.871294  0.607570  1.351474e-07  3.902356  ...   \n",
       "\n",
       "KO                     K23492        K23508        K23514        K23518  \\\n",
       "Sample                                                                    \n",
       "BH37             1.000000e-09  1.000000e-09  1.000000e-09  1.000000e-09   \n",
       "BH091960M        1.624188e-02  1.000000e-09  3.206730e-02  5.205730e-02   \n",
       "BH51             1.000000e-09  1.000000e-09  7.451565e-02  7.451565e-02   \n",
       "BH091995M        1.000000e-09  2.258743e-02  1.168315e-01  3.816497e-02   \n",
       "Average          5.413960e-03  1.000000e-09  3.552765e-02  4.219098e-02   \n",
       "Fold difference  8.120940e+06  8.854482e-08  1.675870e-01  4.619899e-01   \n",
       "\n",
       "KO                     K23535        K23536    K23537        K23548  \\\n",
       "Sample                                                                \n",
       "BH37             9.451451e-01  7.916730e-01  1.186750  1.000000e-09   \n",
       "BH091960M        8.533233e-01  1.465517e+00  0.294436  1.000000e-09   \n",
       "BH51             1.000000e-09  1.000000e-09  0.146547  1.000000e-09   \n",
       "BH091995M        1.479866e-01  1.534387e-01  0.232884  1.020329e-01   \n",
       "Average          5.994895e-01  7.523967e-01  0.542578  1.000000e-09   \n",
       "Fold difference  1.215291e+01  1.471069e+01  3.903696  1.960153e-08   \n",
       "\n",
       "KO                     K23549        K23573  \n",
       "Sample                                       \n",
       "BH37             1.000000e-09  1.000000e-09  \n",
       "BH091960M        1.000000e-09  1.874063e-02  \n",
       "BH51             1.763537e-01  1.266766e+00  \n",
       "BH091995M        1.316302e-01  2.562505e+00  \n",
       "Average          5.878457e-02  4.285022e-01  \n",
       "Fold difference  6.493846e-09  4.894047e-03  \n",
       "\n",
       "[6 rows x 3374 columns]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df4.replace(0, 0.000000001)\n",
    "df4 = df4.append(pd.Series(name='Average'))\n",
    "df4 = df4.append(pd.Series(name='Fold difference', dtype='float64'))\n",
    "df4.loc['Average'] = np.array(np.mean(df4.iloc[:3, :], axis=0))\n",
    "df4.loc['Fold difference'] = df4.apply(lambda x: \n",
    "                                           fold_difference(x.loc['BH37'], x.loc['BH091960M'], \n",
    "                                                           x.loc['BH51'], x.loc['BH091995M']), axis=0)\n",
    "df4.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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": [
    "ax = df4.loc['Average'].plot.hist(bins=50, alpha=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select only the columns (subgroups2) with a five-fold or greater difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5 = df4.loc[:, ((df4.loc['Average'] > 0.2) & \n",
    "                  ((df4.loc['Fold difference'] < 0.05) | (df4.loc['Fold difference'] > 20)))]\n",
    "df5.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5 = df5.sort_values(by='Fold difference', axis=1)\n",
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "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>KO</th>\n",
       "      <th>K05349</th>\n",
       "      <th>K02567</th>\n",
       "      <th>K07716</th>\n",
       "      <th>K11942</th>\n",
       "      <th>K06959</th>\n",
       "      <th>K13040</th>\n",
       "      <th>K05367</th>\n",
       "      <th>K07641</th>\n",
       "      <th>K04561</th>\n",
       "      <th>K02319</th>\n",
       "      <th>...</th>\n",
       "      <th>K02182</th>\n",
       "      <th>K09472</th>\n",
       "      <th>K18593</th>\n",
       "      <th>K03338</th>\n",
       "      <th>K07577</th>\n",
       "      <th>K06954</th>\n",
       "      <th>K14260</th>\n",
       "      <th>K22187</th>\n",
       "      <th>K23464</th>\n",
       "      <th>K01637</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sample</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>BH37</th>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>...</td>\n",
       "      <td>6.154080e-01</td>\n",
       "      <td>6.321228e-01</td>\n",
       "      <td>1.066707e+00</td>\n",
       "      <td>1.098617e+00</td>\n",
       "      <td>1.021121e+00</td>\n",
       "      <td>7.582434e-01</td>\n",
       "      <td>7.308920e-01</td>\n",
       "      <td>1.189789e+00</td>\n",
       "      <td>1.083422e+00</td>\n",
       "      <td>1.340222e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BH091960M</th>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>...</td>\n",
       "      <td>3.452440e-01</td>\n",
       "      <td>3.960520e-01</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.415959e-02</td>\n",
       "      <td>1.465934e-01</td>\n",
       "      <td>4.193736e-01</td>\n",
       "      <td>5.234883e-01</td>\n",
       "      <td>1.961519e-01</td>\n",
       "      <td>5.805431e-01</td>\n",
       "      <td>1.033233e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BH51</th>\n",
       "      <td>6.756085e-01</td>\n",
       "      <td>3.060109e+00</td>\n",
       "      <td>2.071535e+00</td>\n",
       "      <td>7.973174e-01</td>\n",
       "      <td>6.209637e-01</td>\n",
       "      <td>1.671634e+00</td>\n",
       "      <td>6.135122e-01</td>\n",
       "      <td>1.403378e+00</td>\n",
       "      <td>1.239444e+00</td>\n",
       "      <td>9.612519e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BH091995M</th>\n",
       "      <td>4.618740e+00</td>\n",
       "      <td>9.253057e-01</td>\n",
       "      <td>9.790482e-01</td>\n",
       "      <td>1.671470e+00</td>\n",
       "      <td>1.336553e+00</td>\n",
       "      <td>8.801309e-02</td>\n",
       "      <td>1.084197e+00</td>\n",
       "      <td>2.102968e-02</td>\n",
       "      <td>8.411870e-02</td>\n",
       "      <td>1.386401e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "KO               K05349        K02567        K07716        K11942  \\\n",
       "Sample                                                              \n",
       "BH37       1.000000e-09  1.000000e-09  1.000000e-09  1.000000e-09   \n",
       "BH091960M  1.000000e-09  1.000000e-09  1.000000e-09  1.000000e-09   \n",
       "BH51       6.756085e-01  3.060109e+00  2.071535e+00  7.973174e-01   \n",
       "BH091995M  4.618740e+00  9.253057e-01  9.790482e-01  1.671470e+00   \n",
       "\n",
       "KO               K06959        K13040        K05367        K07641  \\\n",
       "Sample                                                              \n",
       "BH37       1.000000e-09  1.000000e-09  1.000000e-09  1.000000e-09   \n",
       "BH091960M  1.000000e-09  1.000000e-09  1.000000e-09  1.000000e-09   \n",
       "BH51       6.209637e-01  1.671634e+00  6.135122e-01  1.403378e+00   \n",
       "BH091995M  1.336553e+00  8.801309e-02  1.084197e+00  2.102968e-02   \n",
       "\n",
       "KO               K04561        K02319  ...        K02182        K09472  \\\n",
       "Sample                                 ...                               \n",
       "BH37       1.000000e-09  1.000000e-09  ...  6.154080e-01  6.321228e-01   \n",
       "BH091960M  1.000000e-09  1.000000e-09  ...  3.452440e-01  3.960520e-01   \n",
       "BH51       1.239444e+00  9.612519e-01  ...  1.000000e-09  1.000000e-09   \n",
       "BH091995M  8.411870e-02  1.386401e-01  ...  1.000000e-09  1.000000e-09   \n",
       "\n",
       "KO               K18593        K03338        K07577        K06954  \\\n",
       "Sample                                                              \n",
       "BH37       1.066707e+00  1.098617e+00  1.021121e+00  7.582434e-01   \n",
       "BH091960M  1.000000e-09  1.415959e-02  1.465934e-01  4.193736e-01   \n",
       "BH51       1.000000e-09  1.000000e-09  1.000000e-09  1.000000e-09   \n",
       "BH091995M  1.000000e-09  1.000000e-09  1.000000e-09  1.000000e-09   \n",
       "\n",
       "KO               K14260        K22187        K23464        K01637  \n",
       "Sample                                                             \n",
       "BH37       7.308920e-01  1.189789e+00  1.083422e+00  1.340222e+00  \n",
       "BH091960M  5.234883e-01  1.961519e-01  5.805431e-01  1.033233e+00  \n",
       "BH51       1.000000e-09  1.000000e-09  1.000000e-09  1.000000e-09  \n",
       "BH091995M  1.000000e-09  1.000000e-09  1.000000e-09  1.000000e-09  \n",
       "\n",
       "[4 rows x 101 columns]"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Just get the sample rows\n",
    "df6 = df5.iloc[0:4, :]\n",
    "df6.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Match Subgroups2 with Groups and make color bar for heat map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "groups = list(df6.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(106, 2)"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6_func = df3.loc[df3['KO'].str.fullmatch('|'.join(groups))]\n",
    "df6_func = df6_func[['Group','KO']]\n",
    "df6_func = df6_func.drop_duplicates()\n",
    "df6_func.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "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>Group</th>\n",
       "      <th>KO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>Metabolism</td>\n",
       "      <td>K02568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>Not Included in Pathway or Brite</td>\n",
       "      <td>K07182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>Metabolism</td>\n",
       "      <td>K02305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>Not Included in Pathway or Brite</td>\n",
       "      <td>K03281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>Metabolism</td>\n",
       "      <td>K01434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7801</th>\n",
       "      <td>Metabolism</td>\n",
       "      <td>K02567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7893</th>\n",
       "      <td>Environmental Information Processing</td>\n",
       "      <td>K07716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7988</th>\n",
       "      <td>Metabolism</td>\n",
       "      <td>K01637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8045</th>\n",
       "      <td>Metabolism</td>\n",
       "      <td>K18593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8174</th>\n",
       "      <td>Environmental Information Processing</td>\n",
       "      <td>K15580</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>106 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Group      KO\n",
       "127                             Metabolism  K02568\n",
       "171       Not Included in Pathway or Brite  K07182\n",
       "362                             Metabolism  K02305\n",
       "371       Not Included in Pathway or Brite  K03281\n",
       "383                             Metabolism  K01434\n",
       "...                                    ...     ...\n",
       "7801                            Metabolism  K02567\n",
       "7893  Environmental Information Processing  K07716\n",
       "7988                            Metabolism  K01637\n",
       "8045                            Metabolism  K18593\n",
       "8174  Environmental Information Processing  K15580\n",
       "\n",
       "[106 rows x 2 columns]"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6_func"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform data frame for better visualization\n",
    "df6_log = np.log(df6)\n",
    "df6_sqrt = np.sqrt(df6)\n",
    "df6_frthrt = np.sqrt(df6_sqrt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Make the color bar for KEGG Groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Metabolism': (0.6509803921568628, 0.807843137254902, 0.8901960784313725),\n",
       " 'Not Included in Pathway or Brite': (0.12156862745098039,\n",
       "  0.47058823529411764,\n",
       "  0.7058823529411765),\n",
       " 'Environmental Information Processing': (0.6980392156862745,\n",
       "  0.8745098039215686,\n",
       "  0.5411764705882353),\n",
       " 'Genetic Information Processing': (0.2,\n",
       "  0.6274509803921569,\n",
       "  0.17254901960784313),\n",
       " 'Cellular Processes': (0.984313725490196, 0.6039215686274509, 0.6)}"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lut = dict(zip(df6_func['Group'].unique(), \n",
    "                sns.color_palette(\"Paired\")))\n",
    "lut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(106, 3)"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6_func['Color'] = df6_func['Group'].map(lut)\n",
    "df6_func.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "lut3 = dict(zip(df6_func['KO'], df6_func['Color']))\n",
    "column_colors = pd.Series(lut3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "K02568    (0.6509803921568628, 0.807843137254902, 0.8901...\n",
       "K07182    (0.12156862745098039, 0.47058823529411764, 0.7...\n",
       "K02305    (0.6509803921568628, 0.807843137254902, 0.8901...\n",
       "K03281    (0.12156862745098039, 0.47058823529411764, 0.7...\n",
       "K01434    (0.6509803921568628, 0.807843137254902, 0.8901...\n",
       "                                ...                        \n",
       "K12308    (0.6509803921568628, 0.807843137254902, 0.8901...\n",
       "K02567    (0.6509803921568628, 0.807843137254902, 0.8901...\n",
       "K07716    (0.6980392156862745, 0.8745098039215686, 0.541...\n",
       "K01637    (0.6509803921568628, 0.807843137254902, 0.8901...\n",
       "K18593    (0.6509803921568628, 0.807843137254902, 0.8901...\n",
       "Length: 101, dtype: object"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_colors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set palette for color bar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_palette = dict(zip(concat.dataset.unique(), [\"purple\",\"orange\"]))\n",
    "row_colors = concat.dataset.map(my_palette)\n",
    "sampz = concat.Sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>dataset</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sample</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BH091960M</th>\n",
       "      <td>purple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BH37</th>\n",
       "      <td>purple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BH091995M</th>\n",
       "      <td>orange</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BH51</th>\n",
       "      <td>orange</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          dataset\n",
       "Sample           \n",
       "BH091960M  purple\n",
       "BH37       purple\n",
       "BH091995M  orange\n",
       "BH51       orange"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_colors = sampz.to_frame().join(row_colors)\n",
    "sample_colors = sample_colors.drop_duplicates(subset=['Sample'])\n",
    "sample_colors = sample_colors.set_index('Sample')\n",
    "sample_colors.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make the clustermap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = sns.clustermap(df6_frthrt, cmap='YlGnBu', figsize=(20,10), xticklabels=True, method='ward',\n",
    "                   metric='euclidean', row_colors = sample_colors, col_colors=column_colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = sns.clustermap(df6_sqrt, cmap='YlGnBu', method='ward', metric='euclidean', xticklabels=False, row_cluster=False,\n",
    "                   col_cluster=False, yticklabels=True, row_colors = sample_colors, col_colors = column_colors)\n",
    "hm = p.ax_heatmap.get_position()\n",
    "col = p.ax_col_dendrogram.get_position()\n",
    "#p.ax_heatmap.invert_yaxis()\n",
    "#plt.setp(p.ax_heatmap.yaxis.get_majorticklabels(), fontsize=18)\n",
    "#plt.setp(p.ax_heatmap.xaxis.get_majorticklabels(), fontsize=22)\n",
    "#plt.setp(p.ax_heatmap.yaxis.invert())\n",
    "p.ax_heatmap.set_position([hm.x0, hm.y0, hm.width*3, hm.height])\n",
    "p.ax_col_colors.set_position([col.x0, col.y0-0.04, col.width*3, col.height*0.25])\n",
    "#row = p.ax_row_dendrogram.get_position()\n",
    "#p.ax_col_dendrogram.set_position([col.x0, col.y0, col.width*3, col.height*0.5])\n",
    "#p.ax_row_dendrogram.set_position([row.x0+0.07, row.y0, row.width*0.75, row.height])\n",
    "#p.ax_col_dendrogram.set_visible(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "p.savefig(\"FigS6B_new.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Make legend for KEGG Groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": [
    "x, y = (0.1, 0)\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "for func in df6_func['Group'].unique():\n",
    "    y = y + 0.1\n",
    "    color = lut[func]\n",
    "    #cat = lut3[ko]\n",
    "    square = plt.Rectangle((x, y), 0.1, 0.1, fc=color)\n",
    "    plt.gca().add_patch(square)\n",
    "    plt.text(x + 0.12, y + 0.03, func, fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig('FigS6B_legend.png')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}