(L) Geographical & Lineage Distribution and Accession ID wrangle for Samples.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This notebook was created to construct a supplementary table with *accession IDs* for all isolates in our sample and to identify the geographical origin of each isolate for isolates that have this meta-data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import vcf\n",
    "\n",
    "%matplotlib inline\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import matplotlib.ticker as ticker\n",
    "from itertools import compress\n",
    "from pylab import MaxNLocator\n",
    "import seaborn as sns; sns.set()\n",
    "from matplotlib.colors import LogNorm\n",
    "from matplotlib import gridspec\n",
    "from matplotlib.gridspec import GridSpec\n",
    "import ast\n",
    "import itertools\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "import fastcluster\n",
    "from sklearn import cluster, datasets\n",
    "import scipy.cluster.hierarchy as hier\n",
    "from sklearn.cluster import KMeans\n",
    "import time\n",
    "import sys\n",
    "\n",
    "import Bio\n",
    "from Bio.Alphabet import IUPAC\n",
    "from Bio.Blast.Applications import NcbiblastnCommandline\n",
    "from Bio.Blast import NCBIXML\n",
    "from Bio.Seq import Seq\n",
    "from Bio.SeqRecord import SeqRecord\n",
    "from Bio.SeqFeature import SeqFeature, FeatureLocation\n",
    "from Bio import pairwise2\n",
    "from Bio import SeqIO\n",
    "from Bio.Graphics import GenomeDiagram\n",
    "from Bio.SeqUtils import GC\n",
    "from Bio import Phylo\n",
    "\n",
    "from Bio.Align.Applications import MuscleCommandline\n",
    "from StringIO import StringIO\n",
    "from Bio import AlignIO\n",
    "from Bio.Align import AlignInfo\n",
    "from Bio.Seq import MutableSeq\n",
    "import itertools\n",
    "import gzip\n",
    "\n",
    "import networkx as nx\n",
    "import scipy\n",
    "from collections import Counter\n",
    "from itertools import groupby\n",
    "from operator import itemgetter\n",
    "\n",
    "#for exporting to Adobe Illustrator\n",
    "mpl.rcParams['pdf.fonttype'] = 42\n",
    "mpl.rcParams['ps.fonttype'] = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####################################################################################################################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [1] Load Sample Annotation Files & Sample Metadata Files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####################################################################################################################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load isolate annotation file (columns of Genotype Matrix)\n",
    "isolate_annotation_DF = pd.read_pickle('/n/data1/hms/dbmi/farhat/Roger/homoplasy_project/rolling_DB_scrape/Genotypes_Filtered_2/genotypes_isolate_annotation.pkl')\n",
    "\n",
    "#load SNP annotation file (rows of Genotype Matrix) with gene annotation information\n",
    "SNP_annotation_DF = pd.read_pickle('/n/data1/hms/dbmi/farhat/Roger/homoplasy_project/rolling_DB_scrape/Genotypes_Filtered_2/genotypes_SNP_annotation.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Columns of Genotype Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>lineage_1</th>\n",
       "      <th>lineage_2</th>\n",
       "      <th>lineage_3</th>\n",
       "      <th>lineage_4</th>\n",
       "      <th>lineage_5</th>\n",
       "      <th>lineage_6</th>\n",
       "      <th>lineage_7</th>\n",
       "      <th>lineage_8</th>\n",
       "      <th>lineage_9</th>\n",
       "      <th>lineage_10</th>\n",
       "      <th>lineage_11</th>\n",
       "      <th>isolate_ID</th>\n",
       "      <th>lineage_call</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>i3</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SAMEA3558733</td>\n",
       "      <td>4.2.1.2.1.1.i3.1</td>\n",
       "      <td>4B</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SAMN03648641</td>\n",
       "      <td>4.2.1.2.2.1.1</td>\n",
       "      <td>4B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>i1</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>SAMN03647419</td>\n",
       "      <td>3.1.1.i1</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SAMEA3671418</td>\n",
       "      <td>4.2.1.2.1.1.i1</td>\n",
       "      <td>4B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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>SAMN07659096</td>\n",
       "      <td>1.1.1.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  lineage_1 lineage_2 lineage_3 lineage_4 lineage_5 lineage_6 lineage_7  \\\n",
       "0         4         2         1         2         1         1        i3   \n",
       "1         4         2         1         2         2         1         1   \n",
       "2         3         1         1        i1       NaN       NaN       NaN   \n",
       "3         4         2         1         2         1         1        i1   \n",
       "4         1         1         1         2       NaN       NaN       NaN   \n",
       "\n",
       "  lineage_8 lineage_9 lineage_10 lineage_11    isolate_ID      lineage_call  \\\n",
       "0         1       NaN        NaN        NaN  SAMEA3558733  4.2.1.2.1.1.i3.1   \n",
       "1       NaN       NaN        NaN        NaN  SAMN03648641     4.2.1.2.2.1.1   \n",
       "2       NaN       NaN        NaN        NaN  SAMN03647419          3.1.1.i1   \n",
       "3       NaN       NaN        NaN        NaN  SAMEA3671418    4.2.1.2.1.1.i1   \n",
       "4       NaN       NaN        NaN        NaN  SAMN07659096           1.1.1.2   \n",
       "\n",
       "  group  \n",
       "0    4B  \n",
       "1    4B  \n",
       "2     3  \n",
       "3    4B  \n",
       "4     1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isolate_annotation_DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31428, 14)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(isolate_annotation_DF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rows of Genotype Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>gene_pos</th>\n",
       "      <th>gene_category</th>\n",
       "      <th>gene_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48</td>\n",
       "      <td>C</td>\n",
       "      <td>[T]</td>\n",
       "      <td>Rv0001</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Essential</td>\n",
       "      <td>dnaA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>64</td>\n",
       "      <td>G</td>\n",
       "      <td>[C]</td>\n",
       "      <td>Rv0001</td>\n",
       "      <td>63.0</td>\n",
       "      <td>Essential</td>\n",
       "      <td>dnaA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67</td>\n",
       "      <td>G</td>\n",
       "      <td>[A, T]</td>\n",
       "      <td>Rv0001</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Essential</td>\n",
       "      <td>dnaA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>69</td>\n",
       "      <td>C</td>\n",
       "      <td>[T]</td>\n",
       "      <td>Rv0001</td>\n",
       "      <td>68.0</td>\n",
       "      <td>Essential</td>\n",
       "      <td>dnaA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>71</td>\n",
       "      <td>C</td>\n",
       "      <td>[T]</td>\n",
       "      <td>Rv0001</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Essential</td>\n",
       "      <td>dnaA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pos ref     alt gene_id  gene_pos gene_category gene_name\n",
       "0   48   C     [T]  Rv0001      47.0     Essential      dnaA\n",
       "1   64   G     [C]  Rv0001      63.0     Essential      dnaA\n",
       "2   67   G  [A, T]  Rv0001      66.0     Essential      dnaA\n",
       "3   69   C     [T]  Rv0001      68.0     Essential      dnaA\n",
       "4   71   C     [T]  Rv0001      70.0     Essential      dnaA"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SNP_annotation_DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(782565, 7)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(SNP_annotation_DF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Retrieve geographical data for isolates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "geographic_isolate_df_A = pd.read_csv('/n/data1/hms/dbmi/farhat/Roger/homoplasy_project/RRDR_mutant_project/GEO data/geo_sampling.txt', sep=\"\\t\", header=0).set_index('BioSample')\n",
    "geographic_isolate_df_B = pd.read_csv('/n/data1/hms/dbmi/farhat/Roger/homoplasy_project/RRDR_mutant_project/GEO data/validation_ids_TableS4.csv', sep=\",\", header=0).set_index('Isolate ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>isolation_country</th>\n",
       "      <th>collection_year</th>\n",
       "      <th>tag</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BioSample</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SAMEA787746</th>\n",
       "      <td>Uganda</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>patric,ncbi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAMN08684019</th>\n",
       "      <td>Lebanon</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>patric,ncbi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAMN09566410</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>patric,ncbi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             isolation_country  collection_year          tag\n",
       "BioSample                                                   \n",
       "SAMEA787746             Uganda           2005.0  patric,ncbi\n",
       "SAMN08684019           Lebanon           2017.0  patric,ncbi\n",
       "SAMN09566410      South Africa           2011.0  patric,ncbi"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geographic_isolate_df_A.head(n=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(64933, 3)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(geographic_isolate_df_A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "      <th>Source</th>\n",
       "      <th>Lab</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Isolate ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1478813216721T177115lib4769nextseqn0035151bp</th>\n",
       "      <td>1</td>\n",
       "      <td>ReseqTB</td>\n",
       "      <td>Borstel</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1478813216721T177215lib4770nextseqn0035151bp</th>\n",
       "      <td>2</td>\n",
       "      <td>ReseqTB</td>\n",
       "      <td>Borstel</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1478813216721T177315lib4771nextseqn0035151bp</th>\n",
       "      <td>3</td>\n",
       "      <td>ReseqTB</td>\n",
       "      <td>Borstel</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Count   Source      Lab  Country\n",
       "Isolate ID                                                                    \n",
       "1478813216721T177115lib4769nextseqn0035151bp      1  ReseqTB  Borstel  Germany\n",
       "1478813216721T177215lib4770nextseqn0035151bp      2  ReseqTB  Borstel  Germany\n",
       "1478813216721T177315lib4771nextseqn0035151bp      3  ReseqTB  Borstel  Germany"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geographic_isolate_df_B.head(n=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(792, 4)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(geographic_isolate_df_B)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####################################################################################################################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [2] Table with all BioSample / Patric IDs for Supplementary Materials"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####################################################################################################################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [2.1] check sample accessions and bin into sample category"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add accession IDs (NCBI) for additional 12 *eis* C-14T mutants with AG MICs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- IT123: SAMN21438148 (PRJNA763160)\n",
    "- IT184: SAMN21438149 (PRJNA763160)\n",
    "- IT952: SAMN21438150 (PRJNA763160)\n",
    "- IT524: SAMN21438151 (PRJNA763160)\n",
    "- 655-19: SAMN21438152 (PRJNA763160)\n",
    "- IT233: ERS2395000 (PRJEB25972)\n",
    "- IT77: ERS2394970 (PRJEB25972)\n",
    "- 622-19: SAMN21438153 (PRJNA763160)\n",
    "- IT1070: SAMN21438154 (PRJNA763160)\n",
    "- IT947: SAMN21438155 (PRJNA763160)\n",
    "- 168-19: SAMN21438156 (PRJNA763160)\n",
    "- IT634: ERS2395185 (PRJEB25972)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "# store each isolate ID and relevant info\n",
    "isolate_tag_list = ['IT123','IT184','IT952','IT524','655-19','IT233','IT77','622-19','IT1070','IT947','168-19','IT634']\n",
    "isolate_accession_ID_list = ['SAMN21438148','SAMN21438149','SAMN21438150','SAMN21438151','SAMN21438152','ERS2395000','ERS2394970','SAMN21438153','SAMN21438154','SAMN21438155','SAMN21438156','ERS2395185']\n",
    "isolate_info_list = ['ncbi']*12\n",
    "\n",
    "# create sets from DataFrame indices for faster checking\n",
    "geographic_isolate_df_A_IDs = set(geographic_isolate_df_A.index)\n",
    "geographic_isolate_df_B_IDs = set(geographic_isolate_df_B.index)\n",
    "\n",
    "#iterate through each isolate ID and check to see if it has associated info in either DataFrame\n",
    "for isolate_ID in isolate_annotation_DF.isolate_ID:\n",
    "    \n",
    "    if isolate_ID in geographic_isolate_df_A_IDs:\n",
    "        isolate_tag_list.append(isolate_ID)\n",
    "        \n",
    "        if geographic_isolate_df_A.loc[isolate_ID, 'tag'] == 'farhat_lab:cetr':\n",
    "            isolate_accession_ID_list.append('farhat_nat_comm_2019')\n",
    "            isolate_info_list.append('ncbi')\n",
    "        else:\n",
    "            isolate_accession_ID_list.append(isolate_ID)\n",
    "            isolate_info_list.append(geographic_isolate_df_A.loc[isolate_ID, 'tag'])\n",
    "        \n",
    "    elif isolate_ID in geographic_isolate_df_B_IDs:\n",
    "        isolate_tag_list.append(isolate_ID)\n",
    "        \n",
    "        if geographic_isolate_df_B.loc[isolate_ID, 'Source'] == 'farhat_lab:cetr':\n",
    "            isolate_accession_ID_list.append('farhat_nat_comm_2019')\n",
    "            isolate_info_list.append('ncbi')\n",
    "        else:\n",
    "            isolate_accession_ID_list.append(isolate_ID)\n",
    "            isolate_info_list.append(geographic_isolate_df_B.loc[isolate_ID, 'Source'])\n",
    "        \n",
    "    elif ((len(isolate_ID) == 6) or (len(isolate_ID) == 7) or (len(isolate_ID) == 8)) and ('R' in isolate_ID):\n",
    "        isolate_tag_list.append(isolate_ID)\n",
    "        isolate_accession_ID_list.append('farhat_nat_comm_2019')\n",
    "        isolate_info_list.append('ncbi')\n",
    "        \n",
    "    elif ('Peru' in isolate_ID):\n",
    "        isolate_tag_list.append(isolate_ID)\n",
    "        isolate_accession_ID_list.append('farhat_nat_comm_2019')\n",
    "        isolate_info_list.append('ncbi')\n",
    "\n",
    "    elif isolate_ID[0:3] == 'TDR':\n",
    "        isolate_tag_list.append(isolate_ID)\n",
    "        isolate_accession_ID_list.append('farhat_nat_comm_2019')\n",
    "        isolate_info_list.append('ncbi')\n",
    "\n",
    "    elif isolate_ID[0:3] == 'SAM':\n",
    "        isolate_tag_list.append(isolate_ID)\n",
    "        isolate_accession_ID_list.append(isolate_ID)\n",
    "        isolate_info_list.append('ncbi')\n",
    "        \n",
    "    elif isolate_ID[0:3] == 'ERR':\n",
    "        isolate_tag_list.append(isolate_ID)\n",
    "        isolate_accession_ID_list.append(isolate_ID)\n",
    "        isolate_info_list.append('ena')\n",
    "        \n",
    "    elif (len(isolate_ID) == 7):\n",
    "        isolate_tag_list.append(isolate_ID)\n",
    "        isolate_accession_ID_list.append('farhat_nat_comm_2019')\n",
    "        isolate_info_list.append('ncbi')\n",
    "        \n",
    "    else:\n",
    "        isolate_tag_list.append(isolate_ID)\n",
    "        isolate_accession_ID_list.append('N/A')\n",
    "        isolate_info_list.append('N/A')\n",
    "        \n",
    "# construct DataFrame from lists\n",
    "supplementary_table_isolate_IDs = pd.DataFrame()\n",
    "supplementary_table_isolate_IDs['sample ID'] = isolate_tag_list\n",
    "supplementary_table_isolate_IDs['accession ID'] = isolate_accession_ID_list\n",
    "supplementary_table_isolate_IDs['INFO'] = isolate_info_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample ID</th>\n",
       "      <th>accession ID</th>\n",
       "      <th>INFO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IT123</td>\n",
       "      <td>SAMN21438148</td>\n",
       "      <td>ncbi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IT184</td>\n",
       "      <td>SAMN21438149</td>\n",
       "      <td>ncbi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IT952</td>\n",
       "      <td>SAMN21438150</td>\n",
       "      <td>ncbi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IT524</td>\n",
       "      <td>SAMN21438151</td>\n",
       "      <td>ncbi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>655-19</td>\n",
       "      <td>SAMN21438152</td>\n",
       "      <td>ncbi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>IT233</td>\n",
       "      <td>ERS2395000</td>\n",
       "      <td>ncbi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>IT77</td>\n",
       "      <td>ERS2394970</td>\n",
       "      <td>ncbi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>622-19</td>\n",
       "      <td>SAMN21438153</td>\n",
       "      <td>ncbi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>IT1070</td>\n",
       "      <td>SAMN21438154</td>\n",
       "      <td>ncbi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>IT947</td>\n",
       "      <td>SAMN21438155</td>\n",
       "      <td>ncbi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>168-19</td>\n",
       "      <td>SAMN21438156</td>\n",
       "      <td>ncbi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>IT634</td>\n",
       "      <td>ERS2395185</td>\n",
       "      <td>ncbi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>SAMEA3558733</td>\n",
       "      <td>SAMEA3558733</td>\n",
       "      <td>ncbi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>SAMN03648641</td>\n",
       "      <td>SAMN03648641</td>\n",
       "      <td>patric,ncbi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>SAMN03647419</td>\n",
       "      <td>SAMN03647419</td>\n",
       "      <td>patric,ncbi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>SAMEA3671418</td>\n",
       "      <td>SAMEA3671418</td>\n",
       "      <td>ncbi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>SAMN07659096</td>\n",
       "      <td>SAMN07659096</td>\n",
       "      <td>patric,ncbi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>SAMEA1119683</td>\n",
       "      <td>SAMEA1119683</td>\n",
       "      <td>patric,ncbi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>SAMN05576173</td>\n",
       "      <td>SAMN05576173</td>\n",
       "      <td>ncbi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>SAMN07373373</td>\n",
       "      <td>SAMN07373373</td>\n",
       "      <td>ncbi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sample ID  accession ID         INFO\n",
       "0          IT123  SAMN21438148         ncbi\n",
       "1          IT184  SAMN21438149         ncbi\n",
       "2          IT952  SAMN21438150         ncbi\n",
       "3          IT524  SAMN21438151         ncbi\n",
       "4         655-19  SAMN21438152         ncbi\n",
       "5          IT233    ERS2395000         ncbi\n",
       "6           IT77    ERS2394970         ncbi\n",
       "7         622-19  SAMN21438153         ncbi\n",
       "8         IT1070  SAMN21438154         ncbi\n",
       "9          IT947  SAMN21438155         ncbi\n",
       "10        168-19  SAMN21438156         ncbi\n",
       "11         IT634    ERS2395185         ncbi\n",
       "12  SAMEA3558733  SAMEA3558733         ncbi\n",
       "13  SAMN03648641  SAMN03648641  patric,ncbi\n",
       "14  SAMN03647419  SAMN03647419  patric,ncbi\n",
       "15  SAMEA3671418  SAMEA3671418         ncbi\n",
       "16  SAMN07659096  SAMN07659096  patric,ncbi\n",
       "17  SAMEA1119683  SAMEA1119683  patric,ncbi\n",
       "18  SAMN05576173  SAMN05576173         ncbi\n",
       "19  SAMN07373373  SAMN07373373         ncbi"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplementary_table_isolate_IDs.head(n = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31440, 3)"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(supplementary_table_isolate_IDs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [2.2] check sample accessions that haven't been deposited"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample ID</th>\n",
       "      <th>accession ID</th>\n",
       "      <th>INFO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7626</th>\n",
       "      <td>1479144119992T178715lib4992nextseqn0036151bp</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9270</th>\n",
       "      <td>1479144119992T178015lib4985nextseqn0035151bp</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22582</th>\n",
       "      <td>1479144813357T180715lib5012nextseqn0035151bp</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          sample ID accession ID INFO\n",
       "7626   1479144119992T178715lib4992nextseqn0036151bp          N/A  N/A\n",
       "9270   1479144119992T178015lib4985nextseqn0035151bp          N/A  N/A\n",
       "22582  1479144813357T180715lib5012nextseqn0035151bp          N/A  N/A"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplementary_table_isolate_IDs[supplementary_table_isolate_IDs.INFO == 'N/A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 3)"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(supplementary_table_isolate_IDs[supplementary_table_isolate_IDs.INFO == 'N/A'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lineage_1</th>\n",
       "      <th>lineage_2</th>\n",
       "      <th>lineage_3</th>\n",
       "      <th>lineage_4</th>\n",
       "      <th>lineage_5</th>\n",
       "      <th>lineage_6</th>\n",
       "      <th>lineage_7</th>\n",
       "      <th>lineage_8</th>\n",
       "      <th>lineage_9</th>\n",
       "      <th>lineage_10</th>\n",
       "      <th>lineage_11</th>\n",
       "      <th>isolate_ID</th>\n",
       "      <th>lineage_call</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7614</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</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>1479144119992T178715lib4992nextseqn0036151bp</td>\n",
       "      <td>2.2.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     lineage_1 lineage_2 lineage_3 lineage_4 lineage_5 lineage_6 lineage_7  \\\n",
       "7614         2         2         2       NaN       NaN       NaN       NaN   \n",
       "\n",
       "     lineage_8 lineage_9 lineage_10 lineage_11  \\\n",
       "7614       NaN       NaN        NaN        NaN   \n",
       "\n",
       "                                        isolate_ID lineage_call group  \n",
       "7614  1479144119992T178715lib4992nextseqn0036151bp        2.2.2     2  "
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isolate_annotation_DF[isolate_annotation_DF.isolate_ID == '1479144119992T178715lib4992nextseqn0036151bp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lineage_1</th>\n",
       "      <th>lineage_2</th>\n",
       "      <th>lineage_3</th>\n",
       "      <th>lineage_4</th>\n",
       "      <th>lineage_5</th>\n",
       "      <th>lineage_6</th>\n",
       "      <th>lineage_7</th>\n",
       "      <th>lineage_8</th>\n",
       "      <th>lineage_9</th>\n",
       "      <th>lineage_10</th>\n",
       "      <th>lineage_11</th>\n",
       "      <th>isolate_ID</th>\n",
       "      <th>lineage_call</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9258</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1479144119992T178015lib4985nextseqn0035151bp</td>\n",
       "      <td>2.2.1.1.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     lineage_1 lineage_2 lineage_3 lineage_4 lineage_5 lineage_6 lineage_7  \\\n",
       "9258         2         2         1         1         1       NaN       NaN   \n",
       "\n",
       "     lineage_8 lineage_9 lineage_10 lineage_11  \\\n",
       "9258       NaN       NaN        NaN        NaN   \n",
       "\n",
       "                                        isolate_ID lineage_call group  \n",
       "9258  1479144119992T178015lib4985nextseqn0035151bp    2.2.1.1.1     2  "
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isolate_annotation_DF[isolate_annotation_DF.isolate_ID == '1479144119992T178015lib4985nextseqn0035151bp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lineage_1</th>\n",
       "      <th>lineage_2</th>\n",
       "      <th>lineage_3</th>\n",
       "      <th>lineage_4</th>\n",
       "      <th>lineage_5</th>\n",
       "      <th>lineage_6</th>\n",
       "      <th>lineage_7</th>\n",
       "      <th>lineage_8</th>\n",
       "      <th>lineage_9</th>\n",
       "      <th>lineage_10</th>\n",
       "      <th>lineage_11</th>\n",
       "      <th>isolate_ID</th>\n",
       "      <th>lineage_call</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22570</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1479144813357T180715lib5012nextseqn0035151bp</td>\n",
       "      <td>2.2.1.1.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      lineage_1 lineage_2 lineage_3 lineage_4 lineage_5 lineage_6 lineage_7  \\\n",
       "22570         2         2         1         1         1       NaN       NaN   \n",
       "\n",
       "      lineage_8 lineage_9 lineage_10 lineage_11  \\\n",
       "22570       NaN       NaN        NaN        NaN   \n",
       "\n",
       "                                         isolate_ID lineage_call group  \n",
       "22570  1479144813357T180715lib5012nextseqn0035151bp    2.2.1.1.1     2  "
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isolate_annotation_DF[isolate_annotation_DF.isolate_ID == '1479144813357T180715lib5012nextseqn0035151bp']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All **\"147\"** isolates came from the same source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ReseqTB'}"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(geographic_isolate_df_B[[('147' == isolate_ID[0:3]) for isolate_ID in geographic_isolate_df_B.index]].Source)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Borstel'}"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(geographic_isolate_df_B[[('147' == isolate_ID[0:3]) for isolate_ID in geographic_isolate_df_B.index]].Lab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Germany'}"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(geographic_isolate_df_B[[('147' == isolate_ID[0:3]) for isolate_ID in geographic_isolate_df_B.index]].Country)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replace N/A values with accession IDs that were uploaded to NCBI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 1479144119992T178715lib4992nextseqn0036151bp: SAMN21441812\n",
    "- 1479144119992T178015lib4985nextseqn0035151bp: SAMN21441811\n",
    "- 1479144813357T180715lib5012nextseqn0035151bp: SAMN21441813"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "supplementary_table_isolate_IDs.loc[7626, ['accession ID','INFO']] = ['SAMN21441812','ncbi']\n",
    "supplementary_table_isolate_IDs.loc[9270, ['accession ID','INFO']] = ['SAMN21441811','ncbi']\n",
    "supplementary_table_isolate_IDs.loc[22582, ['accession ID','INFO']] = ['SAMN21441813','ncbi']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample ID</th>\n",
       "      <th>accession ID</th>\n",
       "      <th>INFO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [sample ID, accession ID, INFO]\n",
       "Index: []"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplementary_table_isolate_IDs[supplementary_table_isolate_IDs.INFO == 'N/A']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [2.3] check sample accessions that belong to *peru* samples from *NatComm* 2019 paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample ID</th>\n",
       "      <th>accession ID</th>\n",
       "      <th>INFO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>02R1945</td>\n",
       "      <td>farhat_nat_comm_2019</td>\n",
       "      <td>ncbi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>01R0685</td>\n",
       "      <td>farhat_nat_comm_2019</td>\n",
       "      <td>ncbi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Peru4532</td>\n",
       "      <td>farhat_nat_comm_2019</td>\n",
       "      <td>ncbi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>TDR236S160L002</td>\n",
       "      <td>farhat_nat_comm_2019</td>\n",
       "      <td>ncbi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>TDR94S189L002</td>\n",
       "      <td>farhat_nat_comm_2019</td>\n",
       "      <td>ncbi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          sample ID          accession ID  INFO\n",
       "45          02R1945  farhat_nat_comm_2019  ncbi\n",
       "61          01R0685  farhat_nat_comm_2019  ncbi\n",
       "63         Peru4532  farhat_nat_comm_2019  ncbi\n",
       "90   TDR236S160L002  farhat_nat_comm_2019  ncbi\n",
       "111   TDR94S189L002  farhat_nat_comm_2019  ncbi"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplementary_table_isolate_IDs[supplementary_table_isolate_IDs.loc[:,'accession ID'] == 'farhat_nat_comm_2019'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1226, 3)"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(supplementary_table_isolate_IDs[supplementary_table_isolate_IDs.loc[:,'accession ID'] == 'farhat_nat_comm_2019'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load in the CSV file that contains annotation information from isolates examined in *Farhat et al (2019) Nature Communications*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "farhat_2019_natcomm_annot_df = pd.read_csv('/n/data1/hms/dbmi/farhat/Roger/inhost_TB_dynamics_project/CSV_files/sample_annotation_files/41467_2019_10110_MOESM4_ESM.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BioProject</th>\n",
       "      <th>Run No</th>\n",
       "      <th>BioSample</th>\n",
       "      <th>WGSAccessionNumber OR Run Accession</th>\n",
       "      <th>ID</th>\n",
       "      <th>Alt ID</th>\n",
       "      <th>PATIENT_ID1</th>\n",
       "      <th>PATIENT_ID2</th>\n",
       "      <th>Country of Isolation</th>\n",
       "      <th>Phylogenetic lineage</th>\n",
       "      <th>spoligotype family</th>\n",
       "      <th>Coll et al Barcode based lineage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PRJEB26000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ERS2401270</td>\n",
       "      <td>ERR2516592</td>\n",
       "      <td>00-R0223</td>\n",
       "      <td>00-R0223</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Peru</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Haarlem</td>\n",
       "      <td>lineage4,lineage4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PRJEB26000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ERS2401327</td>\n",
       "      <td>ERR2516649</td>\n",
       "      <td>00-R0312</td>\n",
       "      <td>00-R0312</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Peru</td>\n",
       "      <td>4.3</td>\n",
       "      <td>LAM</td>\n",
       "      <td>lineage4,lineage4.3,lineage4.3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PRJEB26000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ERS2401310</td>\n",
       "      <td>ERR2516632</td>\n",
       "      <td>00-R0435</td>\n",
       "      <td>00-R0435</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Peru</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>lineage4,lineage4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PRJEB26000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ERS2401368</td>\n",
       "      <td>ERR2516690</td>\n",
       "      <td>00-R0453</td>\n",
       "      <td>00-R0453</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Peru</td>\n",
       "      <td>4.3</td>\n",
       "      <td>LAM</td>\n",
       "      <td>lineage4,lineage4.3,lineage4.3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PRJEB26000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ERS2401251</td>\n",
       "      <td>ERR2516573</td>\n",
       "      <td>00-R1566</td>\n",
       "      <td>00-R1566</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Peru</td>\n",
       "      <td>4.1</td>\n",
       "      <td>X – clade</td>\n",
       "      <td>lineage4,lineage4.1,lineage4.1.1,lineage4.1.1.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   BioProject Run No   BioSample WGSAccessionNumber OR Run Accession  \\\n",
       "0  PRJEB26000    NaN  ERS2401270                          ERR2516592   \n",
       "1  PRJEB26000    NaN  ERS2401327                          ERR2516649   \n",
       "2  PRJEB26000    NaN  ERS2401310                          ERR2516632   \n",
       "3  PRJEB26000    NaN  ERS2401368                          ERR2516690   \n",
       "4  PRJEB26000    NaN  ERS2401251                          ERR2516573   \n",
       "\n",
       "         ID    Alt ID PATIENT_ID1 PATIENT_ID2 Country of Isolation  \\\n",
       "0  00-R0223  00-R0223         NaN         NaN                 Peru   \n",
       "1  00-R0312  00-R0312         NaN         NaN                 Peru   \n",
       "2  00-R0435  00-R0435         NaN         NaN                 Peru   \n",
       "3  00-R0453  00-R0453         NaN         NaN                 Peru   \n",
       "4  00-R1566  00-R1566         NaN         NaN                 Peru   \n",
       "\n",
       "  Phylogenetic lineage spoligotype family  \\\n",
       "0                  4.8            Haarlem   \n",
       "1                  4.3                LAM   \n",
       "2                  NaN                NaN   \n",
       "3                  4.3                LAM   \n",
       "4                  4.1          X – clade   \n",
       "\n",
       "                  Coll et al Barcode based lineage  \n",
       "0                              lineage4,lineage4.8  \n",
       "1                 lineage4,lineage4.3,lineage4.3.3  \n",
       "2                              lineage4,lineage4.8  \n",
       "3                 lineage4,lineage4.3,lineage4.3.3  \n",
       "4  lineage4,lineage4.1,lineage4.1.1,lineage4.1.1.3  "
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "farhat_2019_natcomm_annot_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1483, 12)"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(farhat_2019_natcomm_annot_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fill in Run Accession IDs where missing for annotation files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_missing_runIDs_from_farhat2019_NatComm(isolate_annot_df):\n",
    "    \n",
    "    '''\n",
    "    This function takes as input a df of isolate annotations and fills in missing Run ID accessions that are available from the Farhat et. al. (2019) Nature Communications paper.\n",
    "    '''\n",
    "\n",
    "    accession_ID_list = []\n",
    "\n",
    "    for row_i in isolate_annot_df.index:\n",
    "        \n",
    "        #if accession ID 'farhat_nat_comm_2019', then search for it in annotation file\n",
    "        if supplementary_table_isolate_IDs.loc[row_i, 'accession ID'] == 'farhat_nat_comm_2019':\n",
    "            \n",
    "            sample_ID = isolate_annot_df.loc[row_i , 'sample ID']\n",
    "            \n",
    "            #the sample ID matches a row for an isolate in the annotation file\n",
    "            if np.shape( farhat_2019_natcomm_annot_df[farhat_2019_natcomm_annot_df.ID == sample_ID] )[0] == 1:\n",
    "\n",
    "                WGS_or_Run_Accession = farhat_2019_natcomm_annot_df[farhat_2019_natcomm_annot_df.ID == sample_ID].loc[: , 'WGSAccessionNumber OR Run Accession'].values[0]\n",
    "                accession_ID_list.append(WGS_or_Run_Accession)\n",
    "                \n",
    "            # check for sample ID for the \"TDR\" farhat 2019 NatComm isolates\n",
    "            elif sample_ID[0:3] == \"TDR\":\n",
    "                \n",
    "                sample_ID_TDR = 'TDR ' + sample_ID.split('TDR')[1].split('S')[0] + ' S' + sample_ID.split('TDR')[1].split('S')[1].split('L')[0] + ' L' + sample_ID.split('TDR')[1].split('S')[1].split('L')[1]\n",
    "                \n",
    "                if np.shape( farhat_2019_natcomm_annot_df[farhat_2019_natcomm_annot_df.ID == sample_ID_TDR] )[0] == 1:\n",
    "                    \n",
    "                    WGS_or_Run_Accession = farhat_2019_natcomm_annot_df[farhat_2019_natcomm_annot_df.ID == sample_ID_TDR].loc[: , 'WGSAccessionNumber OR Run Accession'].values[0]\n",
    "                    accession_ID_list.append(WGS_or_Run_Accession)\n",
    "                    \n",
    "                else:\n",
    "                    accession_ID_list.append('PRJEB31023')\n",
    "                    \n",
    "            # check for sample ID for the \"M\" farhat 2019 NatComm isolates\n",
    "            elif sample_ID[0] == \"M\":\n",
    "            \n",
    "                if np.shape( farhat_2019_natcomm_annot_df[farhat_2019_natcomm_annot_df.loc[:,\"Alt ID\"] == sample_ID] )[0] == 1:\n",
    "\n",
    "                    WGS_or_Run_Accession = farhat_2019_natcomm_annot_df[farhat_2019_natcomm_annot_df.loc[:,\"Alt ID\"] == sample_ID].loc[: , 'WGSAccessionNumber OR Run Accession'].values[0]\n",
    "                    accession_ID_list.append(WGS_or_Run_Accession)\n",
    "\n",
    "                else:\n",
    "                    accession_ID_list.append('PRJNA343736')\n",
    "                \n",
    "\n",
    "            #the sample ID was NOT found in the other annotation file(s)\n",
    "            else:\n",
    "                accession_ID_list.append('PRJEB26000')\n",
    "        \n",
    "        #accession ID is same\n",
    "        else:\n",
    "            accession_ID_list.append(isolate_annot_df.loc[row_i , 'accession ID'])\n",
    "\n",
    "    #replace Run ID column from annotation df with updated Run ID column\n",
    "    isolate_annot_df['accession ID'] = accession_ID_list\n",
    "    \n",
    "    return isolate_annot_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Update the CSV file that contains annotation information for isolates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "supplementary_table_isolate_IDs = get_missing_runIDs_from_farhat2019_NatComm(supplementary_table_isolate_IDs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
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       "        text-align: left;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample ID</th>\n",
       "      <th>accession ID</th>\n",
       "      <th>INFO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IT123</td>\n",
       "      <td>SAMN21438148</td>\n",
       "      <td>ncbi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IT184</td>\n",
       "      <td>SAMN21438149</td>\n",
       "      <td>ncbi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IT952</td>\n",
       "      <td>SAMN21438150</td>\n",
       "      <td>ncbi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IT524</td>\n",
       "      <td>SAMN21438151</td>\n",
       "      <td>ncbi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>655-19</td>\n",
       "      <td>SAMN21438152</td>\n",
       "      <td>ncbi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  sample ID  accession ID  INFO\n",
       "0     IT123  SAMN21438148  ncbi\n",
       "1     IT184  SAMN21438149  ncbi\n",
       "2     IT952  SAMN21438150  ncbi\n",
       "3     IT524  SAMN21438151  ncbi\n",
       "4    655-19  SAMN21438152  ncbi"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplementary_table_isolate_IDs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample ID</th>\n",
       "      <th>accession ID</th>\n",
       "      <th>INFO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [sample ID, accession ID, INFO]\n",
       "Index: []"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supplementary_table_isolate_IDs[supplementary_table_isolate_IDs.loc[:,'accession ID'] == 'farhat_nat_comm_2019'].head(n=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 3)"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(supplementary_table_isolate_IDs[supplementary_table_isolate_IDs.loc[:,'accession ID'] == 'farhat_nat_comm_2019'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "supplementary_table_isolate_IDs.to_excel('/n/data1/hms/dbmi/farhat/Roger/mmpR_BDQ_mutant_project/CSV files/Accession IDs for Isolates.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####################################################################################################################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [3] Get geographical information for samples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####################################################################################################################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Concatenate geographic information into one df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "isolate_tags = list(geographic_isolate_df_A.index) + list(geographic_isolate_df_B.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65725"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(isolate_tags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65725"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(isolate_tags)) #check for no repeats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "geographic_isolate_df = pd.DataFrame(index = isolate_tags)\n",
    "geographic_location_list = list(geographic_isolate_df_A.isolation_country) + list(geographic_isolate_df_B.Country)\n",
    "geographic_isolate_df.loc[:, 'country'] = geographic_location_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SAMEA787746</th>\n",
       "      <td>Uganda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAMN08684019</th>\n",
       "      <td>Lebanon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAMN09566410</th>\n",
       "      <td>South Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   country\n",
       "SAMEA787746         Uganda\n",
       "SAMN08684019       Lebanon\n",
       "SAMN09566410  South Africa"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geographic_isolate_df.head(n=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(65725, 1)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(geographic_isolate_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop **nan** values, isolates with no country info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "isolates_with_country_filter = geographic_isolate_df.loc[: , 'country'].isnull() == False\n",
    "geographic_isolate_df = geographic_isolate_df[isolates_with_country_filter]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48261, 1)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(geographic_isolate_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Append to isolate annotation DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "isolate_country_origin_list = []\n",
    "\n",
    "for isolate_id in isolate_annotation_DF.isolate_ID:\n",
    "    \n",
    "    if isolate_id in geographic_isolate_df.index:\n",
    "        \n",
    "        isolate_country_origin_list.append(geographic_isolate_df.loc[isolate_id , 'country'])\n",
    "        \n",
    "    else:\n",
    "        \n",
    "        isolate_country_origin_list.append('#')\n",
    "        \n",
    "isolate_annotation_DF['country'] = isolate_country_origin_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>lineage_1</th>\n",
       "      <th>lineage_2</th>\n",
       "      <th>lineage_3</th>\n",
       "      <th>lineage_4</th>\n",
       "      <th>lineage_5</th>\n",
       "      <th>lineage_6</th>\n",
       "      <th>lineage_7</th>\n",
       "      <th>lineage_8</th>\n",
       "      <th>lineage_9</th>\n",
       "      <th>lineage_10</th>\n",
       "      <th>lineage_11</th>\n",
       "      <th>isolate_ID</th>\n",
       "      <th>lineage_call</th>\n",
       "      <th>group</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>i3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SAMEA3558733</td>\n",
       "      <td>4.2.1.2.1.1.i3.1</td>\n",
       "      <td>4B</td>\n",
       "      <td>#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SAMN03648641</td>\n",
       "      <td>4.2.1.2.2.1.1</td>\n",
       "      <td>4B</td>\n",
       "      <td>#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>i1</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>SAMN03647419</td>\n",
       "      <td>3.1.1.i1</td>\n",
       "      <td>3</td>\n",
       "      <td>#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>i1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SAMEA3671418</td>\n",
       "      <td>4.2.1.2.1.1.i1</td>\n",
       "      <td>4B</td>\n",
       "      <td>#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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>SAMN07659096</td>\n",
       "      <td>1.1.1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  lineage_1 lineage_2 lineage_3 lineage_4 lineage_5 lineage_6 lineage_7  \\\n",
       "0         4         2         1         2         1         1        i3   \n",
       "1         4         2         1         2         2         1         1   \n",
       "2         3         1         1        i1       NaN       NaN       NaN   \n",
       "3         4         2         1         2         1         1        i1   \n",
       "4         1         1         1         2       NaN       NaN       NaN   \n",
       "\n",
       "  lineage_8 lineage_9 lineage_10 lineage_11    isolate_ID      lineage_call  \\\n",
       "0         1       NaN        NaN        NaN  SAMEA3558733  4.2.1.2.1.1.i3.1   \n",
       "1       NaN       NaN        NaN        NaN  SAMN03648641     4.2.1.2.2.1.1   \n",
       "2       NaN       NaN        NaN        NaN  SAMN03647419          3.1.1.i1   \n",
       "3       NaN       NaN        NaN        NaN  SAMEA3671418    4.2.1.2.1.1.i1   \n",
       "4       NaN       NaN        NaN        NaN  SAMN07659096           1.1.1.2   \n",
       "\n",
       "  group         country  \n",
       "0    4B               #  \n",
       "1    4B               #  \n",
       "2     3               #  \n",
       "3    4B               #  \n",
       "4     1  United Kingdom  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isolate_annotation_DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>lineage_2</th>\n",
       "      <th>lineage_3</th>\n",
       "      <th>lineage_4</th>\n",
       "      <th>lineage_5</th>\n",
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       "      <th>lineage_9</th>\n",
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       "      <th>country</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31422</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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>SAMEA1485358</td>\n",
       "      <td>1.2.2.1</td>\n",
       "      <td>1</td>\n",
       "      <td>Malawi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31423</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>i3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SAMEA1530754</td>\n",
       "      <td>4.2.1.2.1.1.i3.1</td>\n",
       "      <td>4B</td>\n",
       "      <td>Malawi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31424</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>i4</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SAMEA104708028</td>\n",
       "      <td>4.2.1.2.1.1.i4.1</td>\n",
       "      <td>4B</td>\n",
       "      <td>Brazil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31426</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>i3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SAMEA1465982</td>\n",
       "      <td>4.2.1.2.1.1.i3.1</td>\n",
       "      <td>4B</td>\n",
       "      <td>Malawi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31427</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>i1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SAMEA1523377</td>\n",
       "      <td>4.2.1.1.1.1.1.1.i1</td>\n",
       "      <td>4C</td>\n",
       "      <td>South Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      lineage_1 lineage_2 lineage_3 lineage_4 lineage_5 lineage_6 lineage_7  \\\n",
       "31422         1         2         2         1       NaN       NaN       NaN   \n",
       "31423         4         2         1         2         1         1        i3   \n",
       "31424         4         2         1         2         1         1        i4   \n",
       "31426         4         2         1         2         1         1        i3   \n",
       "31427         4         2         1         1         1         1         1   \n",
       "\n",
       "      lineage_8 lineage_9 lineage_10 lineage_11      isolate_ID  \\\n",
       "31422       NaN       NaN        NaN        NaN    SAMEA1485358   \n",
       "31423         1       NaN        NaN        NaN    SAMEA1530754   \n",
       "31424         1       NaN        NaN        NaN  SAMEA104708028   \n",
       "31426         1       NaN        NaN        NaN    SAMEA1465982   \n",
       "31427         1        i1        NaN        NaN    SAMEA1523377   \n",
       "\n",
       "             lineage_call group       country  \n",
       "31422             1.2.2.1     1        Malawi  \n",
       "31423    4.2.1.2.1.1.i3.1    4B        Malawi  \n",
       "31424    4.2.1.2.1.1.i4.1    4B        Brazil  \n",
       "31426    4.2.1.2.1.1.i3.1    4B        Malawi  \n",
       "31427  4.2.1.1.1.1.1.1.i1    4C  South Africa  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isolate_annotation_DF[isolate_annotation_DF.country != '#'].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31428, 15)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(isolate_annotation_DF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21500, 15)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(isolate_annotation_DF[isolate_annotation_DF.country != '#']) #number of isolates WITH country info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9928, 15)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(isolate_annotation_DF[isolate_annotation_DF.country == '#']) #number of isolates WITHOUT country info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####################################################################################################################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [4] Examine geographical distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####################################################################################################################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "#                 9928\n",
       "South Africa      3340\n",
       "United Kingdom    2987\n",
       "Peru              1593\n",
       "Vietnam           1391\n",
       "Malawi            1362\n",
       "Canada            1352\n",
       "Russia             995\n",
       "Ukraine            995\n",
       "Azerbaijan         667\n",
       "China              638\n",
       "Netherlands        523\n",
       "Bangladesh         518\n",
       "USA                376\n",
       "Japan              369\n",
       "Pakistan           346\n",
       "Moldova            316\n",
       "Germany            305\n",
       "Thailand           288\n",
       "Denmark            284\n",
       "Italy              230\n",
       "India              212\n",
       "Brazil             190\n",
       "DR Congo           189\n",
       "Argentina          169\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_isolates_per_country = pd.Series( Counter( isolate_annotation_DF.country ) )\n",
    "num_isolates_per_country.sort_values(ascending = False, inplace = True)\n",
    "num_isolates_per_country.head(n=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "230"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_isolates_per_country['Italy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_isolates_per_country['Belarus']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_isolates_per_country['Iran']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add counts for additional 12 *eis* C-14T mutants with AG MICs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- IT123: Italy\n",
    "- IT184: Italy\n",
    "- IT952: Italy\n",
    "- IT524: Italy\n",
    "- 655-19: Belarus\n",
    "- IT233: Italy\n",
    "- IT77: Italy\n",
    "- 622-19: Belarus\n",
    "- IT1070: Italy\n",
    "- IT947: Italy\n",
    "- 168-19: Iran\n",
    "- IT634: Italy\n",
    "\n",
    "Italy + 9\n",
    "\n",
    "Belarus + 2\n",
    "\n",
    "Iran + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_isolates_per_country['Italy'] = num_isolates_per_country['Italy'] + 9\n",
    "num_isolates_per_country['Belarus'] = num_isolates_per_country['Belarus'] + 2\n",
    "num_isolates_per_country['Iran'] = num_isolates_per_country['Iran'] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "239"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_isolates_per_country['Italy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_isolates_per_country['Belarus']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_isolates_per_country['Iran']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Drop samples count that didn't have country data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_isolates_per_country = num_isolates_per_country[num_isolates_per_country.index != '#']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(80,)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(num_isolates_per_country)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "South Africa      3340\n",
       "United Kingdom    2987\n",
       "Peru              1593\n",
       "Vietnam           1391\n",
       "Malawi            1362\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_isolates_per_country.head(n=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many isolates out of **31,440** did we have geographical information for?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21512.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float(num_isolates_per_country.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('ggplot')\n",
    "plt.rcParams['lines.linewidth']=3\n",
    "plt.rcParams['axes.facecolor']='1.0'\n",
    "plt.rcParams['xtick.color']='black'\n",
    "plt.rcParams['ytick.color']='black'\n",
    "plt.rcParams['axes.grid']=True\n",
    "plt.rcParams['axes.edgecolor']='black'\n",
    "plt.rcParams['grid.color']= '1.0'\n",
    "plt.rcParams.update({'font.size': 12})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 684x468 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "countries_with_many_isolates_series = num_isolates_per_country[num_isolates_per_country >= 50]\n",
    "\n",
    "# data to plot\n",
    "n_groups = np.shape(countries_with_many_isolates_series)[0]\n",
    "\n",
    "# create plot\n",
    "fig, ax = plt.subplots()\n",
    "index = np.arange(n_groups)\n",
    "bar_width = 0.67\n",
    "opacity = 1.0\n",
    "edgecolor = 'k'\n",
    "linewidth = 1.0\n",
    "\n",
    "rects1 = plt.bar(index, countries_with_many_isolates_series, bar_width,\n",
    "alpha=opacity,\n",
    "color='0.75',\n",
    "label='Countries with >= 50 samples',\n",
    "linewidth = linewidth,\n",
    "edgecolor = edgecolor)\n",
    "\n",
    "plt.xlabel('Country\\n(with at least 50 isolates)' , color = 'k' , fontsize = 12)\n",
    "plt.ylabel('Number of Isolates', color = 'k' , fontsize = 12)\n",
    "plt.xticks(index, countries_with_many_isolates_series.index, rotation = 90, fontsize = 11)\n",
    "plt.xlim(min(index)-0.6, max(index)+0.6)\n",
    "#plt.legend()\n",
    "\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "\n",
    "plt.tick_params(axis='y', which='major', labelsize=12 , labelcolor = 'k')\n",
    "plt.tick_params(axis='x', which='major', labelsize=12 , labelcolor = 'k')\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(9.5, 6.5)\n",
    "fig.tight_layout()\n",
    "\n",
    "file_name = '/n/data1/hms/dbmi/farhat/Roger/mmpR_BDQ_mutant_project/figures/sample_breakdown_by_country.pdf'\n",
    "plt.savefig(file_name, bbox_inches='tight', dpi = 300 , transparent = True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38,)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(countries_with_many_isolates_series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "South Africa        3340\n",
       "United Kingdom      2987\n",
       "Peru                1593\n",
       "Vietnam             1391\n",
       "Malawi              1362\n",
       "Canada              1352\n",
       "Russia               995\n",
       "Ukraine              995\n",
       "Azerbaijan           667\n",
       "China                638\n",
       "Netherlands          523\n",
       "Bangladesh           518\n",
       "USA                  376\n",
       "Japan                369\n",
       "Pakistan             346\n",
       "Moldova              316\n",
       "Germany              305\n",
       "Thailand             288\n",
       "Denmark              284\n",
       "Italy                239\n",
       "India                212\n",
       "Brazil               190\n",
       "DR Congo             189\n",
       "Argentina            169\n",
       "Philippines          166\n",
       "Eswatini             160\n",
       "Indonesia            122\n",
       "Belgium              118\n",
       "Norway               107\n",
       "Romania               98\n",
       "Djibouti              90\n",
       "Georgia               86\n",
       "Portugal              85\n",
       "United States         77\n",
       "Australia             75\n",
       "Papua New Guinea      57\n",
       "Uganda                54\n",
       "France                51\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries_with_many_isolates_series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####################################################################################################################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [5] Examine lineage distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####################################################################################################################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lineage_1</th>\n",
       "      <th>lineage_2</th>\n",
       "      <th>lineage_3</th>\n",
       "      <th>lineage_4</th>\n",
       "      <th>lineage_5</th>\n",
       "      <th>lineage_6</th>\n",
       "      <th>lineage_7</th>\n",
       "      <th>lineage_8</th>\n",
       "      <th>lineage_9</th>\n",
       "      <th>lineage_10</th>\n",
       "      <th>lineage_11</th>\n",
       "      <th>isolate_ID</th>\n",
       "      <th>lineage_call</th>\n",
       "      <th>group</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>i3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SAMEA3558733</td>\n",
       "      <td>4.2.1.2.1.1.i3.1</td>\n",
       "      <td>4B</td>\n",
       "      <td>#</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SAMN03648641</td>\n",
       "      <td>4.2.1.2.2.1.1</td>\n",
       "      <td>4B</td>\n",
       "      <td>#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>i1</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>SAMN03647419</td>\n",
       "      <td>3.1.1.i1</td>\n",
       "      <td>3</td>\n",
       "      <td>#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>i1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SAMEA3671418</td>\n",
       "      <td>4.2.1.2.1.1.i1</td>\n",
       "      <td>4B</td>\n",
       "      <td>#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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>SAMN07659096</td>\n",
       "      <td>1.1.1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  lineage_1 lineage_2 lineage_3 lineage_4 lineage_5 lineage_6 lineage_7  \\\n",
       "0         4         2         1         2         1         1        i3   \n",
       "1         4         2         1         2         2         1         1   \n",
       "2         3         1         1        i1       NaN       NaN       NaN   \n",
       "3         4         2         1         2         1         1        i1   \n",
       "4         1         1         1         2       NaN       NaN       NaN   \n",
       "\n",
       "  lineage_8 lineage_9 lineage_10 lineage_11    isolate_ID      lineage_call  \\\n",
       "0         1       NaN        NaN        NaN  SAMEA3558733  4.2.1.2.1.1.i3.1   \n",
       "1       NaN       NaN        NaN        NaN  SAMN03648641     4.2.1.2.2.1.1   \n",
       "2       NaN       NaN        NaN        NaN  SAMN03647419          3.1.1.i1   \n",
       "3       NaN       NaN        NaN        NaN  SAMEA3671418    4.2.1.2.1.1.i1   \n",
       "4       NaN       NaN        NaN        NaN  SAMN07659096           1.1.1.2   \n",
       "\n",
       "  group         country  \n",
       "0    4B               #  \n",
       "1    4B               #  \n",
       "2     3               #  \n",
       "3    4B               #  \n",
       "4     1  United Kingdom  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isolate_annotation_DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_isolates_per_global_lineage = pd.Series( Counter( isolate_annotation_DF.lineage_1 ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     2815\n",
       "2     8090\n",
       "3     3398\n",
       "4    16931\n",
       "5       98\n",
       "6       96\n",
       "dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_isolates_per_global_lineage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add counts for additional 12 *eis* C-14T mutants with AG MICs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "isolate_annotation_DF_additional_isolates = pd.read_pickle('/n/data1/hms/dbmi/farhat/Roger/mmpR_BDQ_mutant_project/eis_promoter_mutant_strains_from_Milan/Genotypes/Genotypes_Filtered/genotypes_isolate_annotation.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8090"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_isolates_per_global_lineage['2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16931"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_isolates_per_global_lineage['4']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lineage_call</th>\n",
       "      <th>isolate_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.2.1.1.1</td>\n",
       "      <td>168-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.2.1.1.1.i3</td>\n",
       "      <td>622-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.2.1.2.1.1.i4.1</td>\n",
       "      <td>655-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.2.1.1.1.i3</td>\n",
       "      <td>IT1070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2.1.1.1</td>\n",
       "      <td>IT123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.2.1.1.1</td>\n",
       "      <td>IT184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.2.1.1.1.i3</td>\n",
       "      <td>IT233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.2.1.1.1.1.1.1.i2</td>\n",
       "      <td>IT524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.2.1.1.1.i3</td>\n",
       "      <td>IT634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.2.1.1.1.i3</td>\n",
       "      <td>IT77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.2.1.1.1.i3</td>\n",
       "      <td>IT947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.2.1.1.1.1.1.1.i2</td>\n",
       "      <td>IT952</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          lineage_call isolate_ID\n",
       "0            2.2.1.1.1     168-19\n",
       "1         2.2.1.1.1.i3     622-19\n",
       "2     4.2.1.2.1.1.i4.1     655-19\n",
       "3         2.2.1.1.1.i3     IT1070\n",
       "4            2.2.1.1.1      IT123\n",
       "5            2.2.1.1.1      IT184\n",
       "6         2.2.1.1.1.i3      IT233\n",
       "7   4.2.1.1.1.1.1.1.i2      IT524\n",
       "8         2.2.1.1.1.i3      IT634\n",
       "9         2.2.1.1.1.i3       IT77\n",
       "10        2.2.1.1.1.i3      IT947\n",
       "11  4.2.1.1.1.1.1.1.i2      IT952"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isolate_annotation_DF_additional_isolates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- IT123: 2\n",
    "- IT184: 2\n",
    "- IT952: 4\n",
    "- IT524: 4\n",
    "- 655-19: 4\n",
    "- IT233: 2\n",
    "- IT77: 2\n",
    "- 622-19: 4\n",
    "- IT1070: 2\n",
    "- IT947: 2\n",
    "- 168-19: 2\n",
    "- IT634: 2\n",
    "\n",
    "L2 + 8\n",
    "\n",
    "L4 + 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_isolates_per_global_lineage['2'] = num_isolates_per_global_lineage['2'] + 8\n",
    "num_isolates_per_global_lineage['4'] = num_isolates_per_global_lineage['4'] + 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8098"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_isolates_per_global_lineage['2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16935"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_isolates_per_global_lineage['4']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pie_slice_proportions = []\n",
    "labels = []\n",
    "colors = []\n",
    "\n",
    "for lineage_call in num_isolates_per_global_lineage.index:\n",
    "    \n",
    "    proportion = float(num_isolates_per_global_lineage[lineage_call]) / float(num_isolates_per_global_lineage.sum())\n",
    "    pie_slice_proportions.append(proportion)\n",
    "    labels.append(lineage_call)\n",
    "    \n",
    "    if lineage_call[0] == '1':\n",
    "        colors.append('xkcd:pink')\n",
    "    if lineage_call[0] == '2':\n",
    "        colors.append('xkcd:bright blue')\n",
    "    if lineage_call[0] == '3':\n",
    "        colors.append('xkcd:purple')\n",
    "    if lineage_call[0] == '4':\n",
    "        colors.append('xkcd:red')\n",
    "    if lineage_call[0] == '5':\n",
    "        colors.append('xkcd:light brown')\n",
    "    if lineage_call[0] == '6':\n",
    "        colors.append('xkcd:light green')\n",
    "        \n",
    "# Make square figures and axes\n",
    "the_grid = GridSpec(1, 1)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "wedges, texts = ax.pie(pie_slice_proportions, shadow=False , colors = colors , wedgeprops = {'linewidth': 12 } , labeldistance = 1.1 , startangle = 23)\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(4, 4)\n",
    "fig.tight_layout()\n",
    "\n",
    "for w in wedges:\n",
    "    w.set_linewidth(1.5)\n",
    "    w.set_edgecolor('black')\n",
    "    \n",
    "#for text in texts:\n",
    "#    text.set_rotation(0)\n",
    "\n",
    "file_name = '/n/data1/hms/dbmi/farhat/Roger/mmpR_BDQ_mutant_project/figures/Global_Lineage_Distribution_across_subjects.pdf'\n",
    "plt.savefig(file_name, bbox_inches='tight', dpi = 300 , transparent = True)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make sure all **31,440** isolates are accounted for"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31440.0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float(num_isolates_per_global_lineage.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}