MAGs_KOs_clustermap_Fig5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This notebook is designed to assess functional differences between two groups of MAGs, eg those that belong to \"treatment\" and \"control\" groups. \n",
    "\n",
    "# To do this, you will take MAG-specific KO annotations for each group and identify genes that differ significantly between the two groups. \n",
    "\n",
    "# To run it, you will need KofamScan output files for each MAG, allocated to two directories according to group. You will also need the \"master\" KO file. Instructions for generating this file or downloading it are here: http://merenlab.org/2018/01/17/importing-ghostkoala-annotations/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os as os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import glob as glob\n",
    "from statsmodels.stats.weightstats import ttest_ind\n",
    "from statsmodels.stats.multitest import fdrcorrection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function to import kofamscan outputs for each MAG, remove empty rows, and add a column with the MAG name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_mag_ko_matrix(file):\n",
    "    a, b = file.split('.')\n",
    "    c, d, e, f, g = a.split('_')\n",
    "    name = c + '_' + d + '_' + e\n",
    "    ko = pd.read_csv(file, sep='\\t', names=['ORF', 'KO'])\n",
    "    ko_mag = pd.DataFrame(ko['KO'])\n",
    "    ko_mag.dropna(inplace=True)\n",
    "    ko_mag['MAG'] = name\n",
    "    return(ko_mag)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Navigate to directory with first group of kofamscan outputs (eg MAGs in the \"control\" group)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('/Users/npatin3/Desktop/GTPostdoc/Noro_Assemblies/mags/Group2_kofams')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loop through all kofamscan outputs and concatenate them into one file with MAG-specific column included"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>KO</th>\n",
       "      <th>MAG</th>\n",
       "    </tr>\n",
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       "      <td>K06113</td>\n",
       "      <td>36_12_003</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>K00208</td>\n",
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      "text/plain": [
       "        KO        MAG\n",
       "2   K18929  36_12_003\n",
       "3   K18928  36_12_003\n",
       "4   K02469  36_12_003\n",
       "8   K06113  36_12_003\n",
       "10  K00208  36_12_003"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ko_mags_all2 = []\n",
    "for file in glob.glob(\"*.txt\"):\n",
    "    x = make_mag_ko_matrix(file)\n",
    "    ko_mags_all2.append(x)\n",
    "    \n",
    "ko_mags_all2 = pd.concat(ko_mags_all2)\n",
    "ko_mags_all2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reformat the data frame for the heat map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>KO</th>\n",
       "      <th>K00003</th>\n",
       "      <th>K00005</th>\n",
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       "      <th>K00010</th>\n",
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       "      <th>K23537</th>\n",
       "      <th>K23573</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAG</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "<p>5 rows × 3432 columns</p>\n",
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      ],
      "text/plain": [
       "KO        K00003  K00005  K00009  K00010  K00012  K00013  K00014  K00016  \\\n",
       "MAG                                                                        \n",
       "13_1_001     0.0     0.0     0.0     0.0     1.0     1.0     1.0     0.0   \n",
       "14_1_002     0.0     0.0     1.0     0.0     1.0     1.0     0.0     0.0   \n",
       "15_1_010     1.0     0.0     0.0     1.0     0.0     1.0     1.0     1.0   \n",
       "15_1_014     1.0     0.0     0.0     0.0     0.0     1.0     1.0     0.0   \n",
       "15_1_015     0.0     0.0     0.0     0.0     0.0     0.0     1.0     0.0   \n",
       "\n",
       "KO        K00018  K00024  ...  K23393  K23475  K23508  K23509  K23514  K23518  \\\n",
       "MAG                       ...                                                   \n",
       "13_1_001     1.0     0.0  ...     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "14_1_002     1.0     0.0  ...     0.0     0.0     0.0     0.0     1.0     0.0   \n",
       "15_1_010     0.0     0.0  ...     1.0     0.0     0.0     0.0     0.0     0.0   \n",
       "15_1_014     1.0     0.0  ...     1.0     0.0     0.0     0.0     0.0     1.0   \n",
       "15_1_015     0.0     0.0  ...     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "KO        K23535  K23536  K23537  K23573  \n",
       "MAG                                       \n",
       "13_1_001     0.0     0.0     0.0     0.0  \n",
       "14_1_002     0.0     0.0     0.0     0.0  \n",
       "15_1_010     1.0     1.0     1.0     0.0  \n",
       "15_1_014     0.0     0.0     0.0     3.0  \n",
       "15_1_015     0.0     0.0     0.0     0.0  \n",
       "\n",
       "[5 rows x 3432 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.pivot_table(data=ko_mags_all2, index='MAG',columns='KO',aggfunc=len).fillna(0)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop all columns containing only zeros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df2.loc[:, (df2 != 0).any(axis=0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### If needed, go to a different directory and import the next group of MAG-specific kofams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('/Users/npatin3/Desktop/GTPostdoc/Noro_Assemblies/mags/Group1_kofams')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now we can do the same process for the second group of MAGs (\"treatment\")."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <th>MAG</th>\n",
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      "text/plain": [
       "       KO       MAG\n",
       "1  K07030  38_6_017\n",
       "4  K00962  38_6_017\n",
       "5  K02956  38_6_017\n",
       "6  K03310  38_6_017\n",
       "7  K07979  38_6_017"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ko_mags_all1 = []\n",
    "for file in glob.glob(\"*kofams.txt\"):\n",
    "    x = make_mag_ko_matrix(file)\n",
    "    ko_mags_all1.append(x)\n",
    "    \n",
    "ko_mags_all1 = pd.concat(ko_mags_all1)\n",
    "ko_mags_all1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "      <th>K23541</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAG</th>\n",
       "      <th></th>\n",
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       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15_3_006</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15_6_004</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15_6_011</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15_8_002</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 2008 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "KO        K00003  K00005  K00012  K00013  K00014  K00016  K00018  K00020  \\\n",
       "MAG                                                                        \n",
       "15_3_004     1.0     0.0     0.0     1.0     1.0     0.0     1.0     1.0   \n",
       "15_3_006     1.0     0.0     0.0     1.0     2.0     1.0     1.0     0.0   \n",
       "15_6_004     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "15_6_011     1.0     0.0     0.0     0.0     1.0     0.0     0.0     0.0   \n",
       "15_8_002     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "KO        K00027  K00029  ...  K23393  K23398  K23509  K23514  K23518  K23535  \\\n",
       "MAG                       ...                                                   \n",
       "15_3_004     1.0     0.0  ...     0.0     0.0     0.0     1.0     0.0     2.0   \n",
       "15_3_006     1.0     0.0  ...     0.0     0.0     0.0     0.0     2.0     0.0   \n",
       "15_6_004     0.0     0.0  ...     1.0     0.0     0.0     0.0     0.0     0.0   \n",
       "15_6_011     0.0     0.0  ...     0.0     0.0     0.0     0.0     1.0     0.0   \n",
       "15_8_002     0.0     0.0  ...     0.0     1.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "KO        K23536  K23537  K23541  K23573  \n",
       "MAG                                       \n",
       "15_3_004     2.0     1.0     0.0     0.0  \n",
       "15_3_006     0.0     0.0     0.0     1.0  \n",
       "15_6_004     0.0     0.0     1.0     0.0  \n",
       "15_6_011     0.0     0.0     0.0     0.0  \n",
       "15_8_002     0.0     0.0     0.0     1.0  \n",
       "\n",
       "[5 rows x 2008 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.pivot_table(data=ko_mags_all1, index='MAG',columns='KO',aggfunc=len).fillna(0)\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge the two group (\"treatment\" vs \"control\") data frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39, 3646)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_both = pd.concat([df1, df2]).fillna(0)\n",
    "df_both.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop columns in which every row (MAG) has the same value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "nunique = df_both.apply(pd.Series.nunique)\n",
    "cols_to_drop = nunique[nunique == 1].index\n",
    "df_both = df_both.drop(cols_to_drop, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39, 3645)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_both.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Looks like we only lost one gene here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check out overall range of total number of genes across all MAGs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "K21572    541.0\n",
       "K03327    309.0\n",
       "K02004    251.0\n",
       "K02014    191.0\n",
       "K10822    169.0\n",
       "          ...  \n",
       "K03597      1.0\n",
       "K03598      1.0\n",
       "K03599      1.0\n",
       "K03600      1.0\n",
       "K17234      1.0\n",
       "Length: 3645, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = df_both.sum(axis=0).sort_values(ascending=False, inplace=False)\n",
    "s "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This is a big range! A t-test should work well to test differences across two groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save data frame with values if you like\n",
    "df_both.to_csv(\"MAG_KOs_bothgroups.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate fold change and run t-tests on genes in increase MAGs / decrease MAGs "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add two new rows for statistical results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>K00003</th>\n",
       "      <th>K00005</th>\n",
       "      <th>K00012</th>\n",
       "      <th>K00013</th>\n",
       "      <th>K00014</th>\n",
       "      <th>K00016</th>\n",
       "      <th>K00018</th>\n",
       "      <th>K00020</th>\n",
       "      <th>K00027</th>\n",
       "      <th>K00029</th>\n",
       "      <th>...</th>\n",
       "      <th>K23265</th>\n",
       "      <th>K23270</th>\n",
       "      <th>K23300</th>\n",
       "      <th>K23301</th>\n",
       "      <th>K23302</th>\n",
       "      <th>K23303</th>\n",
       "      <th>K23349</th>\n",
       "      <th>K23356</th>\n",
       "      <th>K23475</th>\n",
       "      <th>K23508</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAG</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>4_1_004</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4_4_014</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ttest</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-value</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>corrected_p-value</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 3645 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   K00003  K00005  K00012  K00013  K00014  K00016  K00018  \\\n",
       "MAG                                                                         \n",
       "4_1_004               1.0     0.0     0.0     1.0     1.0     0.0     1.0   \n",
       "4_4_014               1.0     1.0     0.0     1.0     0.0     1.0     1.0   \n",
       "ttest                 NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "p-value               NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "corrected_p-value     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "                   K00020  K00027  K00029  ...  K23265  K23270  K23300  \\\n",
       "MAG                                        ...                           \n",
       "4_1_004               0.0     1.0     0.0  ...     0.0     0.0     0.0   \n",
       "4_4_014               0.0     1.0     0.0  ...     0.0     0.0     0.0   \n",
       "ttest                 NaN     NaN     NaN  ...     NaN     NaN     NaN   \n",
       "p-value               NaN     NaN     NaN  ...     NaN     NaN     NaN   \n",
       "corrected_p-value     NaN     NaN     NaN  ...     NaN     NaN     NaN   \n",
       "\n",
       "                   K23301  K23302  K23303  K23349  K23356  K23475  K23508  \n",
       "MAG                                                                        \n",
       "4_1_004               0.0     0.0     0.0     0.0     0.0     0.0     0.0  \n",
       "4_4_014               0.0     0.0     0.0     0.0     0.0     0.0     0.0  \n",
       "ttest                 NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "p-value               NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "corrected_p-value     NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "\n",
       "[5 rows x 3645 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ttest = df_both.append(pd.Series(name='ttest', dtype='int'))\n",
    "df_ttest = df_ttest.append(pd.Series(name='p-value', dtype='int'))\n",
    "df_ttest = df_ttest.append(pd.Series(name='corrected_p-value', dtype='int'))\n",
    "df_ttest.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fill new rows with results of scipy t-tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>K00003</th>\n",
       "      <th>K00005</th>\n",
       "      <th>K00012</th>\n",
       "      <th>K00013</th>\n",
       "      <th>K00014</th>\n",
       "      <th>K00016</th>\n",
       "      <th>K00018</th>\n",
       "      <th>K00020</th>\n",
       "      <th>K00027</th>\n",
       "      <th>K00029</th>\n",
       "      <th>...</th>\n",
       "      <th>K23265</th>\n",
       "      <th>K23270</th>\n",
       "      <th>K23300</th>\n",
       "      <th>K23301</th>\n",
       "      <th>K23302</th>\n",
       "      <th>K23303</th>\n",
       "      <th>K23349</th>\n",
       "      <th>K23356</th>\n",
       "      <th>K23475</th>\n",
       "      <th>K23508</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAG</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <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>4_1_004</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4_4_014</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ttest</th>\n",
       "      <td>1.334686</td>\n",
       "      <td>0.881591</td>\n",
       "      <td>-2.897914</td>\n",
       "      <td>-3.337119</td>\n",
       "      <td>0.147122</td>\n",
       "      <td>-0.239732</td>\n",
       "      <td>-2.002474</td>\n",
       "      <td>1.477098</td>\n",
       "      <td>0.899843</td>\n",
       "      <td>-2.480695</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.443376</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.443376</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-value</th>\n",
       "      <td>0.194754</td>\n",
       "      <td>0.392357</td>\n",
       "      <td>0.006440</td>\n",
       "      <td>0.002820</td>\n",
       "      <td>0.885068</td>\n",
       "      <td>0.812238</td>\n",
       "      <td>0.055083</td>\n",
       "      <td>0.165407</td>\n",
       "      <td>0.377901</td>\n",
       "      <td>0.017867</td>\n",
       "      <td>...</td>\n",
       "      <td>0.326892</td>\n",
       "      <td>0.326892</td>\n",
       "      <td>0.326892</td>\n",
       "      <td>0.161329</td>\n",
       "      <td>0.326892</td>\n",
       "      <td>0.326892</td>\n",
       "      <td>0.161329</td>\n",
       "      <td>0.326892</td>\n",
       "      <td>0.326892</td>\n",
       "      <td>0.326892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>corrected_p-value</th>\n",
       "      <td>0.449358</td>\n",
       "      <td>0.512228</td>\n",
       "      <td>0.089251</td>\n",
       "      <td>0.055458</td>\n",
       "      <td>0.925437</td>\n",
       "      <td>0.866704</td>\n",
       "      <td>0.334072</td>\n",
       "      <td>0.449358</td>\n",
       "      <td>0.496557</td>\n",
       "      <td>0.170932</td>\n",
       "      <td>...</td>\n",
       "      <td>0.449358</td>\n",
       "      <td>0.449358</td>\n",
       "      <td>0.449358</td>\n",
       "      <td>0.449358</td>\n",
       "      <td>0.449358</td>\n",
       "      <td>0.449358</td>\n",
       "      <td>0.449358</td>\n",
       "      <td>0.449358</td>\n",
       "      <td>0.449358</td>\n",
       "      <td>0.449358</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 3645 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     K00003    K00005    K00012    K00013    K00014    K00016  \\\n",
       "MAG                                                                             \n",
       "4_1_004            1.000000  0.000000  0.000000  1.000000  1.000000  0.000000   \n",
       "4_4_014            1.000000  1.000000  0.000000  1.000000  0.000000  1.000000   \n",
       "ttest              1.334686  0.881591 -2.897914 -3.337119  0.147122 -0.239732   \n",
       "p-value            0.194754  0.392357  0.006440  0.002820  0.885068  0.812238   \n",
       "corrected_p-value  0.449358  0.512228  0.089251  0.055458  0.925437  0.866704   \n",
       "\n",
       "                     K00018    K00020    K00027    K00029  ...    K23265  \\\n",
       "MAG                                                        ...             \n",
       "4_1_004            1.000000  0.000000  1.000000  0.000000  ...  0.000000   \n",
       "4_4_014            1.000000  0.000000  1.000000  0.000000  ...  0.000000   \n",
       "ttest             -2.002474  1.477098  0.899843 -2.480695  ... -1.000000   \n",
       "p-value            0.055083  0.165407  0.377901  0.017867  ...  0.326892   \n",
       "corrected_p-value  0.334072  0.449358  0.496557  0.170932  ...  0.449358   \n",
       "\n",
       "                     K23270    K23300    K23301    K23302    K23303    K23349  \\\n",
       "MAG                                                                             \n",
       "4_1_004            0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "4_4_014            0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "ttest             -1.000000 -1.000000 -1.443376 -1.000000 -1.000000 -1.443376   \n",
       "p-value            0.326892  0.326892  0.161329  0.326892  0.326892  0.161329   \n",
       "corrected_p-value  0.449358  0.449358  0.449358  0.449358  0.449358  0.449358   \n",
       "\n",
       "                     K23356    K23475    K23508  \n",
       "MAG                                              \n",
       "4_1_004            0.000000  0.000000  0.000000  \n",
       "4_4_014            0.000000  0.000000  0.000000  \n",
       "ttest             -1.000000 -1.000000 -1.000000  \n",
       "p-value            0.326892  0.326892  0.326892  \n",
       "corrected_p-value  0.449358  0.449358  0.449358  \n",
       "\n",
       "[5 rows x 3645 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ttest.iloc[39] = ttest_ind(df_ttest.iloc[:13, :], df_ttest.iloc[13:39, :], usevar='unequal', value=0)[0]\n",
    "df_ttest.iloc[40] = ttest_ind(df_ttest.iloc[:13, :], df_ttest.iloc[13:39, :], usevar='unequal', value=0)[1]\n",
    "df_ttest.iloc[41] = fdrcorrection(df_ttest.iloc[40], alpha=0.1, method='indep')[1]\n",
    "df_ttest.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function to calculate fold difference between asymptomatic and symptomatic samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fold_difference(df, num_symp_rows, num_sample_rows):\n",
    "    set1 = df.iloc[0:num_symp_rows]\n",
    "    set2 = df.iloc[num_symp_rows:num_sample_rows]\n",
    "    mean_diff = np.mean(set1) / np.mean(set2)\n",
    "    #print(mean_diff)\n",
    "    return(mean_diff)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply function to data frame to calculate fold difference of each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>K00003</th>\n",
       "      <th>K00005</th>\n",
       "      <th>K00012</th>\n",
       "      <th>K00013</th>\n",
       "      <th>K00014</th>\n",
       "      <th>K00016</th>\n",
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       "      <th>K00020</th>\n",
       "      <th>K00027</th>\n",
       "      <th>K00029</th>\n",
       "      <th>...</th>\n",
       "      <th>K23265</th>\n",
       "      <th>K23270</th>\n",
       "      <th>K23300</th>\n",
       "      <th>K23301</th>\n",
       "      <th>K23302</th>\n",
       "      <th>K23303</th>\n",
       "      <th>K23349</th>\n",
       "      <th>K23356</th>\n",
       "      <th>K23475</th>\n",
       "      <th>K23508</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAG</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>15_3_004</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e-09</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",
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       "    <tr>\n",
       "      <th>15_3_006</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e-09</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>15_6_004</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>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>15_6_011</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>1.000000e+00</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>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>15_8_002</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>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>5 rows × 3645 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                K00003        K00005        K00012        K00013  \\\n",
       "MAG                                                                \n",
       "15_3_004  1.000000e+00  1.000000e-09  1.000000e-09  1.000000e+00   \n",
       "15_3_006  1.000000e+00  1.000000e-09  1.000000e-09  1.000000e+00   \n",
       "15_6_004  1.000000e-09  1.000000e-09  1.000000e-09  1.000000e-09   \n",
       "15_6_011  1.000000e+00  1.000000e-09  1.000000e-09  1.000000e-09   \n",
       "15_8_002  1.000000e-09  1.000000e-09  1.000000e-09  1.000000e-09   \n",
       "\n",
       "                K00014        K00016        K00018        K00020  \\\n",
       "MAG                                                                \n",
       "15_3_004  1.000000e+00  1.000000e-09  1.000000e+00  1.000000e+00   \n",
       "15_3_006  2.000000e+00  1.000000e+00  1.000000e+00  1.000000e-09   \n",
       "15_6_004  1.000000e-09  1.000000e-09  1.000000e-09  1.000000e-09   \n",
       "15_6_011  1.000000e+00  1.000000e-09  1.000000e-09  1.000000e-09   \n",
       "15_8_002  1.000000e-09  1.000000e-09  1.000000e-09  1.000000e-09   \n",
       "\n",
       "                K00027        K00029  ...        K23265        K23270  \\\n",
       "MAG                                   ...                               \n",
       "15_3_004  1.000000e+00  1.000000e-09  ...  1.000000e-09  1.000000e-09   \n",
       "15_3_006  1.000000e+00  1.000000e-09  ...  1.000000e-09  1.000000e-09   \n",
       "15_6_004  1.000000e-09  1.000000e-09  ...  1.000000e-09  1.000000e-09   \n",
       "15_6_011  1.000000e-09  1.000000e-09  ...  1.000000e-09  1.000000e-09   \n",
       "15_8_002  1.000000e-09  1.000000e-09  ...  1.000000e-09  1.000000e-09   \n",
       "\n",
       "                K23300        K23301        K23302        K23303  \\\n",
       "MAG                                                                \n",
       "15_3_004  1.000000e-09  1.000000e-09  1.000000e-09  1.000000e-09   \n",
       "15_3_006  1.000000e-09  1.000000e-09  1.000000e-09  1.000000e-09   \n",
       "15_6_004  1.000000e-09  1.000000e-09  1.000000e-09  1.000000e-09   \n",
       "15_6_011  1.000000e-09  1.000000e-09  1.000000e-09  1.000000e-09   \n",
       "15_8_002  1.000000e-09  1.000000e-09  1.000000e-09  1.000000e-09   \n",
       "\n",
       "                K23349        K23356        K23475        K23508  \n",
       "MAG                                                               \n",
       "15_3_004  1.000000e-09  1.000000e-09  1.000000e-09  1.000000e-09  \n",
       "15_3_006  1.000000e-09  1.000000e-09  1.000000e-09  1.000000e-09  \n",
       "15_6_004  1.000000e-09  1.000000e-09  1.000000e-09  1.000000e-09  \n",
       "15_6_011  1.000000e-09  1.000000e-09  1.000000e-09  1.000000e-09  \n",
       "15_8_002  1.000000e-09  1.000000e-09  1.000000e-09  1.000000e-09  \n",
       "\n",
       "[5 rows x 3645 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ttest = df_ttest.replace(0, 0.000000001)\n",
    "df_ttest.loc['fold change'] = fold_difference(df_ttest, 13, 39)\n",
    "df_ttest.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select only KOs that are significantly different between the groups, with a corrected p-value < 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(43, 166)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kos_signif = df_ttest.loc[:, df_ttest.loc['corrected_p-value'] < 0.05]\n",
    "kos_signif.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>K00174</th>\n",
       "      <th>K00175</th>\n",
       "      <th>K00177</th>\n",
       "      <th>K00262</th>\n",
       "      <th>K00278</th>\n",
       "      <th>K00548</th>\n",
       "      <th>K00566</th>\n",
       "      <th>K00661</th>\n",
       "      <th>K00748</th>\n",
       "      <th>K00845</th>\n",
       "      <th>...</th>\n",
       "      <th>K12524</th>\n",
       "      <th>K13043</th>\n",
       "      <th>K15977</th>\n",
       "      <th>K16053</th>\n",
       "      <th>K16363</th>\n",
       "      <th>K18691</th>\n",
       "      <th>K18929</th>\n",
       "      <th>K19222</th>\n",
       "      <th>K19802</th>\n",
       "      <th>K21572</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAG</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",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4_4_014</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000e-09</td>\n",
       "      <td>2.000000</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>ttest</th>\n",
       "      <td>-4.225771</td>\n",
       "      <td>-3.731967</td>\n",
       "      <td>-3.594078</td>\n",
       "      <td>-3.701166</td>\n",
       "      <td>-3.992918</td>\n",
       "      <td>-4.221875</td>\n",
       "      <td>3.992918</td>\n",
       "      <td>-4.479173</td>\n",
       "      <td>-3.992918e+00</td>\n",
       "      <td>-3.651867</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.638034e+00</td>\n",
       "      <td>-4.281744e+00</td>\n",
       "      <td>-3.638034e+00</td>\n",
       "      <td>-3.875131e+00</td>\n",
       "      <td>-3.952847e+00</td>\n",
       "      <td>-4.319342e+00</td>\n",
       "      <td>-3.638034e+00</td>\n",
       "      <td>-3.638034e+00</td>\n",
       "      <td>-4.281744e+00</td>\n",
       "      <td>-3.604882e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-value</th>\n",
       "      <td>0.000151</td>\n",
       "      <td>0.000638</td>\n",
       "      <td>0.001042</td>\n",
       "      <td>0.000771</td>\n",
       "      <td>0.000302</td>\n",
       "      <td>0.000289</td>\n",
       "      <td>0.000302</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>3.023379e-04</td>\n",
       "      <td>0.001023</td>\n",
       "      <td>...</td>\n",
       "      <td>1.247050e-03</td>\n",
       "      <td>2.396480e-04</td>\n",
       "      <td>1.247050e-03</td>\n",
       "      <td>6.822968e-04</td>\n",
       "      <td>5.591738e-04</td>\n",
       "      <td>2.174415e-04</td>\n",
       "      <td>1.247050e-03</td>\n",
       "      <td>1.247050e-03</td>\n",
       "      <td>2.396480e-04</td>\n",
       "      <td>1.355995e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>corrected_p-value</th>\n",
       "      <td>0.009469</td>\n",
       "      <td>0.021139</td>\n",
       "      <td>0.029224</td>\n",
       "      <td>0.024261</td>\n",
       "      <td>0.012382</td>\n",
       "      <td>0.012382</td>\n",
       "      <td>0.012382</td>\n",
       "      <td>0.007273</td>\n",
       "      <td>1.238227e-02</td>\n",
       "      <td>0.028895</td>\n",
       "      <td>...</td>\n",
       "      <td>3.102569e-02</td>\n",
       "      <td>1.105718e-02</td>\n",
       "      <td>3.102569e-02</td>\n",
       "      <td>2.220510e-02</td>\n",
       "      <td>1.887212e-02</td>\n",
       "      <td>1.105718e-02</td>\n",
       "      <td>3.102569e-02</td>\n",
       "      <td>3.102569e-02</td>\n",
       "      <td>1.105718e-02</td>\n",
       "      <td>3.209481e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fold change</th>\n",
       "      <td>0.193548</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.344828</td>\n",
       "      <td>1.866667</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>1.333333e-01</td>\n",
       "      <td>0.378378</td>\n",
       "      <td>...</td>\n",
       "      <td>2.888889e-09</td>\n",
       "      <td>2.363636e-09</td>\n",
       "      <td>2.166667e-09</td>\n",
       "      <td>1.368421e-09</td>\n",
       "      <td>2.600000e-09</td>\n",
       "      <td>1.300000e-09</td>\n",
       "      <td>2.888889e-09</td>\n",
       "      <td>2.888889e-09</td>\n",
       "      <td>2.363636e-09</td>\n",
       "      <td>4.805915e-11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 166 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     K00174    K00175    K00177    K00262    K00278    K00548  \\\n",
       "MAG                                                                             \n",
       "4_4_014            1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "ttest             -4.225771 -3.731967 -3.594078 -3.701166 -3.992918 -4.221875   \n",
       "p-value            0.000151  0.000638  0.001042  0.000771  0.000302  0.000289   \n",
       "corrected_p-value  0.009469  0.021139  0.029224  0.024261  0.012382  0.012382   \n",
       "fold change        0.193548  0.230769  0.222222  0.375000  0.133333  0.344828   \n",
       "\n",
       "                     K00566    K00661        K00748    K00845  ...  \\\n",
       "MAG                                                            ...   \n",
       "4_4_014            1.000000  4.000000  1.000000e-09  2.000000  ...   \n",
       "ttest              3.992918 -4.479173 -3.992918e+00 -3.651867  ...   \n",
       "p-value            0.000302  0.000100  3.023379e-04  0.001023  ...   \n",
       "corrected_p-value  0.012382  0.007273  1.238227e-02  0.028895  ...   \n",
       "fold change        1.866667  0.095238  1.333333e-01  0.378378  ...   \n",
       "\n",
       "                         K12524        K13043        K15977        K16053  \\\n",
       "MAG                                                                         \n",
       "4_4_014            1.000000e-09  1.000000e-09  1.000000e-09  1.000000e-09   \n",
       "ttest             -3.638034e+00 -4.281744e+00 -3.638034e+00 -3.875131e+00   \n",
       "p-value            1.247050e-03  2.396480e-04  1.247050e-03  6.822968e-04   \n",
       "corrected_p-value  3.102569e-02  1.105718e-02  3.102569e-02  2.220510e-02   \n",
       "fold change        2.888889e-09  2.363636e-09  2.166667e-09  1.368421e-09   \n",
       "\n",
       "                         K16363        K18691        K18929        K19222  \\\n",
       "MAG                                                                         \n",
       "4_4_014            1.000000e-09  1.000000e-09  1.000000e-09  1.000000e-09   \n",
       "ttest             -3.952847e+00 -4.319342e+00 -3.638034e+00 -3.638034e+00   \n",
       "p-value            5.591738e-04  2.174415e-04  1.247050e-03  1.247050e-03   \n",
       "corrected_p-value  1.887212e-02  1.105718e-02  3.102569e-02  3.102569e-02   \n",
       "fold change        2.600000e-09  1.300000e-09  2.888889e-09  2.888889e-09   \n",
       "\n",
       "                         K19802        K21572  \n",
       "MAG                                            \n",
       "4_4_014            1.000000e-09  1.000000e-09  \n",
       "ttest             -4.281744e+00 -3.604882e+00  \n",
       "p-value            2.396480e-04  1.355995e-03  \n",
       "corrected_p-value  1.105718e-02  3.209481e-02  \n",
       "fold change        2.363636e-09  4.805915e-11  \n",
       "\n",
       "[5 rows x 166 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kos_signif.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save just the list of significantly different KOs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "ko_sd_list = list(kos_signif.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"MAG_SDKOs_CORRECTED_p05_list.txt\", \"w\") as output:\n",
    "    output.write(str(ko_sd_list))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get annotations of significantly different KOs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the \"master\" KO htext file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "ko_master = '/Users/npatin3/Desktop/GTPostdoc/Noro_Assemblies/KO_Orthology_ko00001.txt'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Match the KO number in column 3 of the master file with the KO list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# KO list\n",
    "kos_unfilt = df_both.columns.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(ko_master, 'r') as file, open('MAG_KOs_fxns_unfilt.tsv', 'w') as outfile:\n",
    "    for line in file:\n",
    "        X = line.rstrip().split('\\t')\n",
    "        konumber = X[3].split(' ')[0]\n",
    "        if konumber in kos_unfilt:\n",
    "            outfile.write(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
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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>Subgroup1</th>\n",
       "      <th>Subgroup2</th>\n",
       "      <th>KO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>09100 Metabolism</td>\n",
       "      <td>09101 Carbohydrate metabolism</td>\n",
       "      <td>00010 Glycolysis / Gluconeogenesis [PATH:ko00010]</td>\n",
       "      <td>K00844  HK; hexokinase [EC:2.7.1.1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>09100 Metabolism</td>\n",
       "      <td>09101 Carbohydrate metabolism</td>\n",
       "      <td>00010 Glycolysis / Gluconeogenesis [PATH:ko00010]</td>\n",
       "      <td>K00845  glk; glucokinase [EC:2.7.1.2]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>09100 Metabolism</td>\n",
       "      <td>09101 Carbohydrate metabolism</td>\n",
       "      <td>00010 Glycolysis / Gluconeogenesis [PATH:ko00010]</td>\n",
       "      <td>K01810  GPI, pgi; glucose-6-phosphate isomeras...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>09100 Metabolism</td>\n",
       "      <td>09101 Carbohydrate metabolism</td>\n",
       "      <td>00010 Glycolysis / Gluconeogenesis [PATH:ko00010]</td>\n",
       "      <td>K06859  pgi1; glucose-6-phosphate isomerase, a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>09100 Metabolism</td>\n",
       "      <td>09101 Carbohydrate metabolism</td>\n",
       "      <td>00010 Glycolysis / Gluconeogenesis [PATH:ko00010]</td>\n",
       "      <td>K00850  pfkA, PFK; 6-phosphofructokinase 1 [EC...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Group                      Subgroup1  \\\n",
       "0  09100 Metabolism  09101 Carbohydrate metabolism   \n",
       "1  09100 Metabolism  09101 Carbohydrate metabolism   \n",
       "2  09100 Metabolism  09101 Carbohydrate metabolism   \n",
       "3  09100 Metabolism  09101 Carbohydrate metabolism   \n",
       "4  09100 Metabolism  09101 Carbohydrate metabolism   \n",
       "\n",
       "                                           Subgroup2  \\\n",
       "0  00010 Glycolysis / Gluconeogenesis [PATH:ko00010]   \n",
       "1  00010 Glycolysis / Gluconeogenesis [PATH:ko00010]   \n",
       "2  00010 Glycolysis / Gluconeogenesis [PATH:ko00010]   \n",
       "3  00010 Glycolysis / Gluconeogenesis [PATH:ko00010]   \n",
       "4  00010 Glycolysis / Gluconeogenesis [PATH:ko00010]   \n",
       "\n",
       "                                                  KO  \n",
       "0                K00844  HK; hexokinase [EC:2.7.1.1]  \n",
       "1              K00845  glk; glucokinase [EC:2.7.1.2]  \n",
       "2  K01810  GPI, pgi; glucose-6-phosphate isomeras...  \n",
       "3  K06859  pgi1; glucose-6-phosphate isomerase, a...  \n",
       "4  K00850  pfkA, PFK; 6-phosphofructokinase 1 [EC...  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kos = pd.read_csv('MAG_KOs_fxns_unfilt.tsv', names=['Group','Subgroup1','Subgroup2','KO'], sep='\\t')\n",
    "kos.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6197, 4)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kos.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exclude all irrelevant KEGG categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "searchfor = ['Human Diseases','Brite Hierarchies','Organismal Systems',\n",
    "                                    'Cellular community - eukaryotes']\n",
    "kos2 = kos.loc[~kos['Group'].str.contains('|'.join(searchfor))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sanity check - kos2 should be much smaller than kos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3816, 4)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kos2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reformat the data frame text to make it more readable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/npatin3/miniconda/lib/python3.6/site-packages/pandas/core/frame.py:2963: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[k1] = value[k2]\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",
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       "    }\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>Subgroup1</th>\n",
       "      <th>Subgroup2</th>\n",
       "      <th>KO</th>\n",
       "      <th>Function</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>09100 Metabolism</td>\n",
       "      <td>09101 Carbohydrate metabolism</td>\n",
       "      <td>00010 Glycolysis / Gluconeogenesis [PATH:ko00010]</td>\n",
       "      <td>K00844</td>\n",
       "      <td>HK; hexokinase [EC:2.7.1.1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>09100 Metabolism</td>\n",
       "      <td>09101 Carbohydrate metabolism</td>\n",
       "      <td>00010 Glycolysis / Gluconeogenesis [PATH:ko00010]</td>\n",
       "      <td>K00845</td>\n",
       "      <td>glk; glucokinase [EC:2.7.1.2]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>09100 Metabolism</td>\n",
       "      <td>09101 Carbohydrate metabolism</td>\n",
       "      <td>00010 Glycolysis / Gluconeogenesis [PATH:ko00010]</td>\n",
       "      <td>K01810</td>\n",
       "      <td>GPI, pgi; glucose-6-phosphate isomerase [EC:5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>09100 Metabolism</td>\n",
       "      <td>09101 Carbohydrate metabolism</td>\n",
       "      <td>00010 Glycolysis / Gluconeogenesis [PATH:ko00010]</td>\n",
       "      <td>K06859</td>\n",
       "      <td>pgi1; glucose-6-phosphate isomerase, archaeal...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>09100 Metabolism</td>\n",
       "      <td>09101 Carbohydrate metabolism</td>\n",
       "      <td>00010 Glycolysis / Gluconeogenesis [PATH:ko00010]</td>\n",
       "      <td>K00850</td>\n",
       "      <td>pfkA, PFK; 6-phosphofructokinase 1 [EC:2.7.1.11]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Group                      Subgroup1  \\\n",
       "0  09100 Metabolism  09101 Carbohydrate metabolism   \n",
       "1  09100 Metabolism  09101 Carbohydrate metabolism   \n",
       "2  09100 Metabolism  09101 Carbohydrate metabolism   \n",
       "3  09100 Metabolism  09101 Carbohydrate metabolism   \n",
       "4  09100 Metabolism  09101 Carbohydrate metabolism   \n",
       "\n",
       "                                           Subgroup2      KO  \\\n",
       "0  00010 Glycolysis / Gluconeogenesis [PATH:ko00010]  K00844   \n",
       "1  00010 Glycolysis / Gluconeogenesis [PATH:ko00010]  K00845   \n",
       "2  00010 Glycolysis / Gluconeogenesis [PATH:ko00010]  K01810   \n",
       "3  00010 Glycolysis / Gluconeogenesis [PATH:ko00010]  K06859   \n",
       "4  00010 Glycolysis / Gluconeogenesis [PATH:ko00010]  K00850   \n",
       "\n",
       "                                            Function  \n",
       "0                        HK; hexokinase [EC:2.7.1.1]  \n",
       "1                      glk; glucokinase [EC:2.7.1.2]  \n",
       "2   GPI, pgi; glucose-6-phosphate isomerase [EC:5...  \n",
       "3   pgi1; glucose-6-phosphate isomerase, archaeal...  \n",
       "4   pfkA, PFK; 6-phosphofructokinase 1 [EC:2.7.1.11]  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split the function from the KO\n",
    "kos2[['KO', 'Function']] = kos2['KO'].str.split(\" \", n=1, expand=True)\n",
    "kos2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/npatin3/miniconda/lib/python3.6/site-packages/pandas/core/frame.py:2963: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[k1] = value[k2]\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Group</th>\n",
       "      <th>Subgroup1</th>\n",
       "      <th>Subgroup2</th>\n",
       "      <th>KO</th>\n",
       "      <th>Function</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>09100 Metabolism</td>\n",
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       "      <td>00010 Glycolysis / Gluconeogenesis</td>\n",
       "      <td>K00844</td>\n",
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       "      <th>1</th>\n",
       "      <td>09100 Metabolism</td>\n",
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       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>09100 Metabolism</td>\n",
       "      <td>09101 Carbohydrate metabolism</td>\n",
       "      <td>00010 Glycolysis / Gluconeogenesis</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>09100 Metabolism</td>\n",
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       "      <td>00010 Glycolysis / Gluconeogenesis</td>\n",
       "      <td>K06859</td>\n",
       "      <td>pgi1; glucose-6-phosphate isomerase, archaeal...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>09100 Metabolism</td>\n",
       "      <td>09101 Carbohydrate metabolism</td>\n",
       "      <td>00010 Glycolysis / Gluconeogenesis</td>\n",
       "      <td>K00850</td>\n",
       "      <td>pfkA, PFK; 6-phosphofructokinase 1 [EC:2.7.1.11]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Group                      Subgroup1  \\\n",
       "0  09100 Metabolism  09101 Carbohydrate metabolism   \n",
       "1  09100 Metabolism  09101 Carbohydrate metabolism   \n",
       "2  09100 Metabolism  09101 Carbohydrate metabolism   \n",
       "3  09100 Metabolism  09101 Carbohydrate metabolism   \n",
       "4  09100 Metabolism  09101 Carbohydrate metabolism   \n",
       "\n",
       "                             Subgroup2      KO  \\\n",
       "0  00010 Glycolysis / Gluconeogenesis   K00844   \n",
       "1  00010 Glycolysis / Gluconeogenesis   K00845   \n",
       "2  00010 Glycolysis / Gluconeogenesis   K01810   \n",
       "3  00010 Glycolysis / Gluconeogenesis   K06859   \n",
       "4  00010 Glycolysis / Gluconeogenesis   K00850   \n",
       "\n",
       "                                            Function  \n",
       "0                        HK; hexokinase [EC:2.7.1.1]  \n",
       "1                      glk; glucokinase [EC:2.7.1.2]  \n",
       "2   GPI, pgi; glucose-6-phosphate isomerase [EC:5...  \n",
       "3   pgi1; glucose-6-phosphate isomerase, archaeal...  \n",
       "4   pfkA, PFK; 6-phosphofructokinase 1 [EC:2.7.1.11]  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split out the \"PATH\" part of Subgroup2 label\n",
    "kos2[['Subgroup2', 'PATH']] = kos2['Subgroup2'].str.split(\"[\", n=1, expand=True)\n",
    "kos2 = kos2.drop('PATH', axis=1)\n",
    "kos2 = kos2.drop_duplicates(subset=['KO'])\n",
    "kos2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select only the KOs that are significantly different (listed earlier in ko_sd_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "kos_funct = kos2.loc[kos2['KO'].isin(ko_sd_list), :]\n",
    "kos_funct.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "ko_sd_list_prokaryo = kos_funct['KO']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "kos_signif_final = kos_signif[kos_signif.columns.intersection(ko_sd_list_prokaryo)]\n",
    "kos_signif_final.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export dataframes with KO and function information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is the significantly different KOs that are in the relevant categories, with fold changes and p-values\n",
    "kos_signif_final.to_csv(\"MAG_SDKOs_CORRECTED_p05.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is the final list of significantly different KOs and their functions\n",
    "kos_funct[['KO','Function']].to_csv('MAG_KOs_SD_CORRECTED_functions.csv', index=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Format for heat map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exclude the last two rows (p-value and t-test results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hmap = kos_signif.iloc[0:39, ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Need to use list of KOs from 'kos_funct' because it excluded the irrelevant categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_ko_list = kos_funct.KO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "                K00174        K00175        K00177        K00262  \\\n",
       "MAG                                                                \n",
       "15_3_004  1.000000e-09  1.000000e-09  1.000000e-09  1.000000e+00   \n",
       "15_3_006  1.000000e-09  1.000000e-09  1.000000e-09  1.000000e-09   \n",
       "15_6_004  1.000000e-09  1.000000e-09  1.000000e-09  1.000000e-09   \n",
       "15_6_011  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e-09   \n",
       "15_8_002  1.000000e-09  1.000000e-09  1.000000e-09  1.000000e-09   \n",
       "\n",
       "                K00278        K00548  K00566        K00661        K00748  \\\n",
       "MAG                                                                        \n",
       "15_3_004  1.000000e-09  1.000000e+00     1.0  1.000000e-09  1.000000e-09   \n",
       "15_3_006  1.000000e-09  1.000000e+00     1.0  1.000000e-09  1.000000e-09   \n",
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       "\n",
       "                K00845  ...        K11085        K11720        K12340  \\\n",
       "MAG                     ...                                             \n",
       "15_3_004  1.000000e+00  ...  1.000000e-09  1.000000e-09  1.000000e-09   \n",
       "15_3_006  1.000000e-09  ...  1.000000e-09  1.000000e-09  1.000000e-09   \n",
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       "15_8_002  1.000000e-09  ...  1.000000e-09  1.000000e-09  1.000000e-09   \n",
       "\n",
       "                K12524        K13043        K15977        K16363  \\\n",
       "MAG                                                                \n",
       "15_3_004  1.000000e-09  1.000000e-09  1.000000e-09  1.000000e-09   \n",
       "15_3_006  1.000000e-09  1.000000e-09  1.000000e-09  1.000000e-09   \n",
       "15_6_004  1.000000e-09  1.000000e-09  1.000000e-09  1.000000e-09   \n",
       "15_6_011  1.000000e-09  1.000000e-09  1.000000e-09  1.000000e-09   \n",
       "15_8_002  1.000000e-09  1.000000e-09  1.000000e-09  1.000000e-09   \n",
       "\n",
       "                K18929        K19222        K19802  \n",
       "MAG                                                 \n",
       "15_3_004  1.000000e-09  1.000000e-09  1.000000e-09  \n",
       "15_3_006  1.000000e-09  1.000000e-09  1.000000e-09  \n",
       "15_6_004  1.000000e-09  1.000000e-09  1.000000e-09  \n",
       "15_6_011  1.000000e-09  1.000000e-09  1.000000e-09  \n",
       "15_8_002  1.000000e-09  1.000000e-09  1.000000e-09  \n",
       "\n",
       "[5 rows x 116 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hmap = df_hmap[df_hmap.columns.intersection(final_ko_list)]\n",
    "df_hmap.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set palettes for color bars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. MAG colors (increase vs decrease)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use information about your samples to assing colors to each sample according to its group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "mags1 = pd.DataFrame(ko_mags_all1['MAG']).drop_duplicates(subset=['MAG'])\n",
    "mags2 = pd.DataFrame(ko_mags_all2['MAG']).drop_duplicates(subset=['MAG'])\n",
    "mags1['Group'] = 'purple'\n",
    "mags2['Group'] = 'orange'\n",
    "sample_colors = pd.concat([mags1, mags2]).set_index('MAG')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Gene colors (by functional group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(116, 2)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kos_column_colors = kos_funct[['Group', 'KO']]\n",
    "kos_column_colors.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/npatin3/miniconda/lib/python3.6/site-packages/pandas/core/frame.py:2963: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[k1] = value[k2]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>KO</th>\n",
       "      <th>Functional category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>K00845</td>\n",
       "      <td>Metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>K00174</td>\n",
       "      <td>Metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>K00175</td>\n",
       "      <td>Metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>K01785</td>\n",
       "      <td>Metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>K00177</td>\n",
       "      <td>Metabolism</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        KO Functional category\n",
       "1   K00845          Metabolism\n",
       "31  K00174          Metabolism\n",
       "32  K00175          Metabolism\n",
       "39  K01785          Metabolism\n",
       "65  K00177          Metabolism"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kos_column_colors = kos_funct[['Group', 'KO']]\n",
    "kos_column_colors[['Group', 'Functional category']] = kos_column_colors['Group'].str.split(\" \", n=1, expand=True)\n",
    "kos_column_colors = kos_column_colors.drop('Group', axis=1)\n",
    "kos_column_colors.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Metabolism', 'Genetic Information Processing',\n",
       "       'Environmental Information Processing', 'Cellular Processes',\n",
       "       'Not Included in Pathway or Brite'], dtype=object)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kos_column_colors['Functional category'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Metabolism': (0.4, 0.7607843137254902, 0.6470588235294118),\n",
       " 'Genetic Information Processing': (0.9882352941176471,\n",
       "  0.5529411764705883,\n",
       "  0.3843137254901961),\n",
       " 'Environmental Information Processing': (0.5529411764705883,\n",
       "  0.6274509803921569,\n",
       "  0.796078431372549),\n",
       " 'Cellular Processes': (0.9058823529411765,\n",
       "  0.5411764705882353,\n",
       "  0.7647058823529411),\n",
       " 'Not Included in Pathway or Brite': (0.6509803921568628,\n",
       "  0.8470588235294118,\n",
       "  0.32941176470588235)}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lut = dict(zip(kos_column_colors['Functional category'].unique(), \n",
    "                sns.color_palette(\"Set2\")))\n",
    "lut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>KO</th>\n",
       "      <th>Functional category</th>\n",
       "      <th>Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>K00845</td>\n",
       "      <td>Metabolism</td>\n",
       "      <td>(0.4, 0.7607843137254902, 0.6470588235294118)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>K00174</td>\n",
       "      <td>Metabolism</td>\n",
       "      <td>(0.4, 0.7607843137254902, 0.6470588235294118)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>K00175</td>\n",
       "      <td>Metabolism</td>\n",
       "      <td>(0.4, 0.7607843137254902, 0.6470588235294118)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>K01785</td>\n",
       "      <td>Metabolism</td>\n",
       "      <td>(0.4, 0.7607843137254902, 0.6470588235294118)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>K00177</td>\n",
       "      <td>Metabolism</td>\n",
       "      <td>(0.4, 0.7607843137254902, 0.6470588235294118)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        KO Functional category                                          Color\n",
       "1   K00845          Metabolism  (0.4, 0.7607843137254902, 0.6470588235294118)\n",
       "31  K00174          Metabolism  (0.4, 0.7607843137254902, 0.6470588235294118)\n",
       "32  K00175          Metabolism  (0.4, 0.7607843137254902, 0.6470588235294118)\n",
       "39  K01785          Metabolism  (0.4, 0.7607843137254902, 0.6470588235294118)\n",
       "65  K00177          Metabolism  (0.4, 0.7607843137254902, 0.6470588235294118)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kos_column_colors['Color'] = kos_column_colors['Functional category'].map(lut)\n",
    "kos_column_colors.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "lut3 = dict(zip(kos_column_colors['KO'], kos_column_colors['Color']))\n",
    "column_colors = pd.Series(lut3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Quick and dirty to check\n",
    "p = sns.clustermap(df_hmap, cmap='YlGnBu', figsize=(20,10), xticklabels=True, yticklabels=True,\n",
    "                   row_cluster=False, method='ward', metric='euclidean', row_colors=sample_colors, \n",
    "                   col_colors=column_colors)\n",
    "p.ax_col_dendrogram.set_visible(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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": [
    "# Make it look pretty\n",
    "# See below for sample_colors and column_colors\n",
    "p = sns.clustermap(df_hmap, figsize=(20,10), cmap='YlGnBu', xticklabels=True, yticklabels=False, row_cluster=False,\n",
    "                   method='ward', metric='euclidean', row_colors=sample_colors, col_colors=column_colors)\n",
    "hm = p.ax_heatmap.get_position()\n",
    "plt.setp(p.ax_heatmap.yaxis.get_majorticklabels(), fontsize=14)\n",
    "plt.setp(p.ax_heatmap.xaxis.get_majorticklabels(), fontsize=8)\n",
    "p.ax_heatmap.set_position([hm.x0, hm.y0, hm.width, hm.height])\n",
    "col = p.ax_col_dendrogram.get_position()\n",
    "row = p.ax_row_dendrogram.get_position()\n",
    "p.ax_col_dendrogram.set_position([col.x0, col.y0, col.width, col.height])\n",
    "p.ax_col_colors.set_position([col.x0, col.y0-0.04, col.width, col.height*0.25])\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": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "p.savefig(\"MAGs_KOs_clustermap_SDCORRECTEDp05_KOlabels.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom legend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "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 kos_column_colors['Functional category'].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": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig('fxn_labels.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}