comparative_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COMPARATIVE ANALYSIS\n",
    "This notebook takes the DESeq2 outputs for each of the seven focal ant species in our screen, as well as the table of ortholog groups from OrthoMCL. It calculates the observed number of concordant differentially expressed genes across all combinations of species. It also identifies the genes that are concordantly differentially expressed in any subset of five species. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "1.  Create data frames of p_values for all species\n",
    "2.  Identify single copy orthologs\n",
    "3.  Create new columns to allow filtering to single copy orthologs\n",
    "4.  Join the p-value files together\n",
    "5.  Identify all concordant differentially expressed genes\n",
    "6.  Check number of orthologous DEGs per species\n",
    "7.  Find number of concordant DEGs\n",
    "8.  Generate heatmap of genes that are differentially expressed in any subset of five species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from itertools import combinations\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create table of single copy ant orthologs and their differential expression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) Create data frames of p_values for all species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create name lists for readability of tables:\n",
    "species =   ['Acromyrmex',\n",
    "             'Camponotus',\n",
    "             'Dinoponera',\n",
    "             'Harpegnathos',\n",
    "             'Odontomachus',\n",
    "             'Ooceraea',\n",
    "             'Solenopsis'\n",
    "            ]\n",
    "sp_short = ['Aech', 'Cpla', 'Dqua', 'Hsal', 'Orug', 'Obir', 'Sinv']\n",
    "\n",
    "# define the file names of all DESeq2 output files:\n",
    "all_pvalues =   ['Acromyrmex_echinatior_deseq.txt',\n",
    "                 'Camponotus_planatus_deseq.txt',\n",
    "                 'Dinoponera_quadriceps_deseq.txt',\n",
    "                 'Harpegnathos_saltator_deseq.txt',\n",
    "                 'Odontomachus_ruginodis_deseq.txt',\n",
    "                 'Ooceraea_biroi_deseq.txt',\n",
    "                 'Solenopsis_invicta_deseq.txt'\n",
    "                ]\n",
    "\n",
    "# specify the orthomcl output file:\n",
    "orthologs = os.path.join('orthomcl_output', 'mclGroups.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create a dictionary of dataframes, with key being the genus.\n",
    "df_pval_dic = { sp:pd.read_csv(os.path.join('DESeq2_output',fp), \n",
    "                               sep=' ', \n",
    "                               index_col=0\n",
    "                              ) for sp, fp in zip(species,all_pvalues) \n",
    "              }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acromyrmex   :12176 genes, 6 columns\n",
      "Camponotus   :17993 genes, 6 columns\n",
      "Dinoponera   :11544 genes, 6 columns\n",
      "Harpegnathos :12302 genes, 6 columns\n",
      "Odontomachus :25458 genes, 6 columns\n",
      "Ooceraea     :13640 genes, 6 columns\n",
      "Solenopsis   :15103 genes, 6 columns\n"
     ]
    }
   ],
   "source": [
    "for sp in species:\n",
    "    print(\"{:<13s}:{:5} genes, {} columns\".format(sp, df_pval_dic[sp].shape[0], df_pval_dic[sp].shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>baseMean</th>\n",
       "      <th>log2FoldChange</th>\n",
       "      <th>lfcSE</th>\n",
       "      <th>stat</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>padj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aech|LOC105144162</th>\n",
       "      <td>5933.902081</td>\n",
       "      <td>4.016824</td>\n",
       "      <td>0.181735</td>\n",
       "      <td>-22.102647</td>\n",
       "      <td>2.980722e-108</td>\n",
       "      <td>3.172383e-104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aech|LOC105148307</th>\n",
       "      <td>351.575222</td>\n",
       "      <td>3.927635</td>\n",
       "      <td>0.217894</td>\n",
       "      <td>-18.025465</td>\n",
       "      <td>1.229748e-72</td>\n",
       "      <td>6.544105e-69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aech|LOC105149897</th>\n",
       "      <td>84.006564</td>\n",
       "      <td>6.540987</td>\n",
       "      <td>0.414533</td>\n",
       "      <td>-15.779186</td>\n",
       "      <td>4.327312e-56</td>\n",
       "      <td>1.535186e-52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aech|LOC105152929</th>\n",
       "      <td>85.273274</td>\n",
       "      <td>5.542425</td>\n",
       "      <td>0.364529</td>\n",
       "      <td>-15.204351</td>\n",
       "      <td>3.308932e-52</td>\n",
       "      <td>8.804242e-49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aech|LOC105151872</th>\n",
       "      <td>1400.529397</td>\n",
       "      <td>4.216169</td>\n",
       "      <td>0.317197</td>\n",
       "      <td>-13.291948</td>\n",
       "      <td>2.577796e-40</td>\n",
       "      <td>5.487098e-37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      baseMean  log2FoldChange     lfcSE       stat  \\\n",
       "Aech|LOC105144162  5933.902081        4.016824  0.181735 -22.102647   \n",
       "Aech|LOC105148307   351.575222        3.927635  0.217894 -18.025465   \n",
       "Aech|LOC105149897    84.006564        6.540987  0.414533 -15.779186   \n",
       "Aech|LOC105152929    85.273274        5.542425  0.364529 -15.204351   \n",
       "Aech|LOC105151872  1400.529397        4.216169  0.317197 -13.291948   \n",
       "\n",
       "                          pvalue           padj  \n",
       "Aech|LOC105144162  2.980722e-108  3.172383e-104  \n",
       "Aech|LOC105148307   1.229748e-72   6.544105e-69  \n",
       "Aech|LOC105149897   4.327312e-56   1.535186e-52  \n",
       "Aech|LOC105152929   3.308932e-52   8.804242e-49  \n",
       "Aech|LOC105151872   2.577796e-40   5.487098e-37  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display first 5 rows of dataframe to check content\n",
    "df_pval_dic['Acromyrmex'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) Identify single copy orthologs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>orthologs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ILPpaperGroups1000</td>\n",
       "      <td>Aech|LOC105155080 Obir|LOC105287483 Sinv|LOC1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ILPpaperGroups1001</td>\n",
       "      <td>Acep|LOC105617370 Acep|LOC105617724 Acep|LOC1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ILPpaperGroups1002</td>\n",
       "      <td>Acep|LOC105617602 Acep|LOC105621803 Acep|LOC1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ILPpaperGroups1003</td>\n",
       "      <td>Acep|LOC105619009 Acep|LOC105619010 Acep|LOC1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ILPpaperGroups1004</td>\n",
       "      <td>Acep|LOC105619475 Dqua|LOC106746452 Lhum|LOC1...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                group                                          orthologs\n",
       "0  ILPpaperGroups1000   Aech|LOC105155080 Obir|LOC105287483 Sinv|LOC1...\n",
       "1  ILPpaperGroups1001   Acep|LOC105617370 Acep|LOC105617724 Acep|LOC1...\n",
       "2  ILPpaperGroups1002   Acep|LOC105617602 Acep|LOC105621803 Acep|LOC1...\n",
       "3  ILPpaperGroups1003   Acep|LOC105619009 Acep|LOC105619010 Acep|LOC1...\n",
       "4  ILPpaperGroups1004   Acep|LOC105619475 Dqua|LOC106746452 Lhum|LOC1..."
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create dataframe of orthologs from orthomcl output\n",
    "df_orthologs = pd.read_csv(orthologs, \n",
    "                           sep=':', \n",
    "                           header=None, \n",
    "                           names=[\"group\",\"orthologs\"])\n",
    "df_orthologs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3) Create new columns to allow filtering to single copy orthologs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create column with number of orthologs in group:\n",
    "df_orthologs[\"group_size\"] = df_orthologs.apply(lambda s: len(s[\"orthologs\"].split()), axis=1)\n",
    "\n",
    "# group size only counting the 7 ant species of interest:\n",
    "df_orthologs[\"group_size_7\"] = df_orthologs.apply(lambda s: len([o for o in s[\"orthologs\"].split() if o[:4] in sp_short]), axis=1)\n",
    "\n",
    "# create column with number of unique species present:\n",
    "df_orthologs[\"group_size_unique\"] = df_orthologs.apply(lambda s: len(set([g[:4] for g in s['orthologs'].split()])), axis=1)\n",
    "\n",
    "# unique set of 7 ant species:\n",
    "df_orthologs[\"group_size_7_unique\"] = df_orthologs.apply(lambda s: len(set([g[:4] for g in s['orthologs'].split() if g[:4] in sp_short])), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>orthologs</th>\n",
       "      <th>group_size</th>\n",
       "      <th>group_size_7</th>\n",
       "      <th>group_size_unique</th>\n",
       "      <th>group_size_7_unique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ILPpaperGroups1000</td>\n",
       "      <td>Aech|LOC105155080 Obir|LOC105287483 Sinv|LOC1...</td>\n",
       "      <td>1084</td>\n",
       "      <td>295</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ILPpaperGroups1001</td>\n",
       "      <td>Acep|LOC105617370 Acep|LOC105617724 Acep|LOC1...</td>\n",
       "      <td>277</td>\n",
       "      <td>112</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ILPpaperGroups1002</td>\n",
       "      <td>Acep|LOC105617602 Acep|LOC105621803 Acep|LOC1...</td>\n",
       "      <td>260</td>\n",
       "      <td>99</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ILPpaperGroups1003</td>\n",
       "      <td>Acep|LOC105619009 Acep|LOC105619010 Acep|LOC1...</td>\n",
       "      <td>241</td>\n",
       "      <td>106</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ILPpaperGroups1004</td>\n",
       "      <td>Acep|LOC105619475 Dqua|LOC106746452 Lhum|LOC1...</td>\n",
       "      <td>215</td>\n",
       "      <td>76</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                group                                          orthologs  \\\n",
       "0  ILPpaperGroups1000   Aech|LOC105155080 Obir|LOC105287483 Sinv|LOC1...   \n",
       "1  ILPpaperGroups1001   Acep|LOC105617370 Acep|LOC105617724 Acep|LOC1...   \n",
       "2  ILPpaperGroups1002   Acep|LOC105617602 Acep|LOC105621803 Acep|LOC1...   \n",
       "3  ILPpaperGroups1003   Acep|LOC105619009 Acep|LOC105619010 Acep|LOC1...   \n",
       "4  ILPpaperGroups1004   Acep|LOC105619475 Dqua|LOC106746452 Lhum|LOC1...   \n",
       "\n",
       "   group_size  group_size_7  group_size_unique  group_size_7_unique  \n",
       "0        1084           295                 16                    7  \n",
       "1         277           112                 18                    7  \n",
       "2         260            99                 18                    7  \n",
       "3         241           106                 18                    7  \n",
       "4         215            76                 15                    7  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orthologs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a subset of the table above, which includes only single copy orthologs across the seven ant species being analyzed. (SCAOs = \"Single Copy Ant Orthologs\"). Orthologs were determined by orthomcl analysis of the following Insect genomes:\n",
    "\n",
    "**Ants**\n",
    "* Acromyrmex echinatior    \n",
    "* Atta cephalotes\n",
    "* Camponotus floridanus  \n",
    "* Camponotus planatus  \n",
    "* Dinoponera quadriceps  \n",
    "* Harpegnathos saltator  \n",
    "* Linepithema humile  \n",
    "* Monomorium pharaonis  \n",
    "* Odonotomachus ruginodis  \n",
    "* Ooceraea biroi  \n",
    "* Pogonomyrmex barbatum  \n",
    "* Solenopsis invicta  \n",
    "* Vollenhovia emeryii\n",
    "* Wasmannia auropunctata\n",
    "\n",
    "**Bees**\n",
    "* Apis mellifera\n",
    "\n",
    "**Wasps**\n",
    "* Nasonia vitripennis  \n",
    "* Polistes canadensis  \n",
    "\n",
    "**Diptera**\n",
    "* Drosophila melanogaster  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scaos = df_orthologs[(df_orthologs.group_size_7 == 7) & (df_orthologs.group_size_7_unique == 7)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5581, 6)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaos.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4) Join the p-value files together\n",
    "All files will be indexed by ortholog group. First, we separate the single copy ant orthologs into columns for each of their respective species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_combined = scaos.apply(lambda s: pd.Series(sorted([o for o in s[\"orthologs\"].split() if o[:4] in sp_short])), axis=1)\n",
    "df_combined.columns = sorted(sp_short)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Aech</th>\n",
       "      <th>Cpla</th>\n",
       "      <th>Dqua</th>\n",
       "      <th>Hsal</th>\n",
       "      <th>Obir</th>\n",
       "      <th>Orug</th>\n",
       "      <th>Sinv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>Aech|LOC105153097</td>\n",
       "      <td>Cpla|SGF61405</td>\n",
       "      <td>Dqua|LOC106749891</td>\n",
       "      <td>Hsal|LOC105181067</td>\n",
       "      <td>Obir|LOC105281216</td>\n",
       "      <td>Orug|SGF68562</td>\n",
       "      <td>Sinv|LOC105201217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>Aech|LOC105144379</td>\n",
       "      <td>Cpla|SGF47786</td>\n",
       "      <td>Dqua|LOC106743402</td>\n",
       "      <td>Hsal|LOC105181117</td>\n",
       "      <td>Obir|LOC105282301</td>\n",
       "      <td>Orug|SGF65534</td>\n",
       "      <td>Sinv|LOC105194815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>Aech|LOC105154742</td>\n",
       "      <td>Cpla|SGF57622</td>\n",
       "      <td>Dqua|LOC106742675</td>\n",
       "      <td>Hsal|LOC105184475</td>\n",
       "      <td>Obir|LOC105281047</td>\n",
       "      <td>Orug|SGF78634</td>\n",
       "      <td>Sinv|LOC105203057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>Aech|LOC105151083</td>\n",
       "      <td>Cpla|SGF65369</td>\n",
       "      <td>Dqua|LOC106742289</td>\n",
       "      <td>Hsal|LOC105180743</td>\n",
       "      <td>Obir|LOC105287676</td>\n",
       "      <td>Orug|SGF86030</td>\n",
       "      <td>Sinv|LOC105198509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>Aech|LOC105149040</td>\n",
       "      <td>Cpla|SGF52740</td>\n",
       "      <td>Dqua|LOC106748414</td>\n",
       "      <td>Hsal|LOC105185999</td>\n",
       "      <td>Obir|LOC105277964</td>\n",
       "      <td>Orug|SGF81562</td>\n",
       "      <td>Sinv|LOC105200782</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Aech           Cpla               Dqua               Hsal  \\\n",
       "305  Aech|LOC105153097  Cpla|SGF61405  Dqua|LOC106749891  Hsal|LOC105181067   \n",
       "331  Aech|LOC105144379  Cpla|SGF47786  Dqua|LOC106743402  Hsal|LOC105181117   \n",
       "367  Aech|LOC105154742  Cpla|SGF57622  Dqua|LOC106742675  Hsal|LOC105184475   \n",
       "413  Aech|LOC105151083  Cpla|SGF65369  Dqua|LOC106742289  Hsal|LOC105180743   \n",
       "423  Aech|LOC105149040  Cpla|SGF52740  Dqua|LOC106748414  Hsal|LOC105185999   \n",
       "\n",
       "                  Obir           Orug               Sinv  \n",
       "305  Obir|LOC105281216  Orug|SGF68562  Sinv|LOC105201217  \n",
       "331  Obir|LOC105282301  Orug|SGF65534  Sinv|LOC105194815  \n",
       "367  Obir|LOC105281047  Orug|SGF78634  Sinv|LOC105203057  \n",
       "413  Obir|LOC105287676  Orug|SGF86030  Sinv|LOC105198509  \n",
       "423  Obir|LOC105277964  Orug|SGF81562  Sinv|LOC105200782  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combined.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we append the mean expression, the log2 fold-change between reproductive and non-reproductive castes, the p-value and FDR-corrected p-values from the DESeq2 analyses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cols_to_join = [\"baseMean\",\"log2FoldChange\",\"pvalue\",\"padj\"]   #note that pvalue is the unadjusted pval, and padj is p after fdr correction\n",
    "for sp,gen in zip(sp_short,species):\n",
    "    p_to_join = df_pval_dic[gen][cols_to_join]\n",
    "    p_to_join.columns = [ \"{}_{}\".format(sp,c) for c in cols_to_join]\n",
    "    df_combined = df_combined.join(p_to_join, how='left', on=sp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Aech</th>\n",
       "      <th>Cpla</th>\n",
       "      <th>Dqua</th>\n",
       "      <th>Hsal</th>\n",
       "      <th>Obir</th>\n",
       "      <th>Orug</th>\n",
       "      <th>Sinv</th>\n",
       "      <th>Aech_baseMean</th>\n",
       "      <th>Aech_log2FoldChange</th>\n",
       "      <th>Aech_pvalue</th>\n",
       "      <th>...</th>\n",
       "      <th>Orug_pvalue</th>\n",
       "      <th>Orug_padj</th>\n",
       "      <th>Obir_baseMean</th>\n",
       "      <th>Obir_log2FoldChange</th>\n",
       "      <th>Obir_pvalue</th>\n",
       "      <th>Obir_padj</th>\n",
       "      <th>Sinv_baseMean</th>\n",
       "      <th>Sinv_log2FoldChange</th>\n",
       "      <th>Sinv_pvalue</th>\n",
       "      <th>Sinv_padj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>Aech|LOC105153097</td>\n",
       "      <td>Cpla|SGF61405</td>\n",
       "      <td>Dqua|LOC106749891</td>\n",
       "      <td>Hsal|LOC105181067</td>\n",
       "      <td>Obir|LOC105281216</td>\n",
       "      <td>Orug|SGF68562</td>\n",
       "      <td>Sinv|LOC105201217</td>\n",
       "      <td>839.829550</td>\n",
       "      <td>-0.005591</td>\n",
       "      <td>9.749647e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.323920</td>\n",
       "      <td>0.471735</td>\n",
       "      <td>2227.754133</td>\n",
       "      <td>0.432653</td>\n",
       "      <td>0.002878</td>\n",
       "      <td>0.041655</td>\n",
       "      <td>391.232256</td>\n",
       "      <td>-0.051518</td>\n",
       "      <td>0.421668</td>\n",
       "      <td>0.585876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>Aech|LOC105144379</td>\n",
       "      <td>Cpla|SGF47786</td>\n",
       "      <td>Dqua|LOC106743402</td>\n",
       "      <td>Hsal|LOC105181117</td>\n",
       "      <td>Obir|LOC105282301</td>\n",
       "      <td>Orug|SGF65534</td>\n",
       "      <td>Sinv|LOC105194815</td>\n",
       "      <td>244.784487</td>\n",
       "      <td>1.460857</td>\n",
       "      <td>2.037000e-08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.836298</td>\n",
       "      <td>0.891570</td>\n",
       "      <td>793.978290</td>\n",
       "      <td>-0.007736</td>\n",
       "      <td>0.963926</td>\n",
       "      <td>0.992221</td>\n",
       "      <td>226.248598</td>\n",
       "      <td>0.423662</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>0.000781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>Aech|LOC105154742</td>\n",
       "      <td>Cpla|SGF57622</td>\n",
       "      <td>Dqua|LOC106742675</td>\n",
       "      <td>Hsal|LOC105184475</td>\n",
       "      <td>Obir|LOC105281047</td>\n",
       "      <td>Orug|SGF78634</td>\n",
       "      <td>Sinv|LOC105203057</td>\n",
       "      <td>306.311466</td>\n",
       "      <td>-0.077285</td>\n",
       "      <td>6.244275e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>2189.024661</td>\n",
       "      <td>-0.103909</td>\n",
       "      <td>0.607203</td>\n",
       "      <td>0.871456</td>\n",
       "      <td>407.541439</td>\n",
       "      <td>0.260069</td>\n",
       "      <td>0.005161</td>\n",
       "      <td>0.019039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>Aech|LOC105151083</td>\n",
       "      <td>Cpla|SGF65369</td>\n",
       "      <td>Dqua|LOC106742289</td>\n",
       "      <td>Hsal|LOC105180743</td>\n",
       "      <td>Obir|LOC105287676</td>\n",
       "      <td>Orug|SGF86030</td>\n",
       "      <td>Sinv|LOC105198509</td>\n",
       "      <td>92.576057</td>\n",
       "      <td>0.841557</td>\n",
       "      <td>8.401890e-03</td>\n",
       "      <td>...</td>\n",
       "      <td>0.463778</td>\n",
       "      <td>0.605464</td>\n",
       "      <td>83.684642</td>\n",
       "      <td>-0.166003</td>\n",
       "      <td>0.461690</td>\n",
       "      <td>0.789415</td>\n",
       "      <td>45.880269</td>\n",
       "      <td>0.219733</td>\n",
       "      <td>0.386031</td>\n",
       "      <td>0.551058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>Aech|LOC105149040</td>\n",
       "      <td>Cpla|SGF52740</td>\n",
       "      <td>Dqua|LOC106748414</td>\n",
       "      <td>Hsal|LOC105185999</td>\n",
       "      <td>Obir|LOC105277964</td>\n",
       "      <td>Orug|SGF81562</td>\n",
       "      <td>Sinv|LOC105200782</td>\n",
       "      <td>102.323539</td>\n",
       "      <td>1.464132</td>\n",
       "      <td>3.364315e-10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.471751</td>\n",
       "      <td>0.612367</td>\n",
       "      <td>1136.701183</td>\n",
       "      <td>0.179205</td>\n",
       "      <td>0.234553</td>\n",
       "      <td>0.608615</td>\n",
       "      <td>867.414114</td>\n",
       "      <td>0.762431</td>\n",
       "      <td>0.000251</td>\n",
       "      <td>0.001467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Aech           Cpla               Dqua               Hsal  \\\n",
       "305  Aech|LOC105153097  Cpla|SGF61405  Dqua|LOC106749891  Hsal|LOC105181067   \n",
       "331  Aech|LOC105144379  Cpla|SGF47786  Dqua|LOC106743402  Hsal|LOC105181117   \n",
       "367  Aech|LOC105154742  Cpla|SGF57622  Dqua|LOC106742675  Hsal|LOC105184475   \n",
       "413  Aech|LOC105151083  Cpla|SGF65369  Dqua|LOC106742289  Hsal|LOC105180743   \n",
       "423  Aech|LOC105149040  Cpla|SGF52740  Dqua|LOC106748414  Hsal|LOC105185999   \n",
       "\n",
       "                  Obir           Orug               Sinv  Aech_baseMean  \\\n",
       "305  Obir|LOC105281216  Orug|SGF68562  Sinv|LOC105201217     839.829550   \n",
       "331  Obir|LOC105282301  Orug|SGF65534  Sinv|LOC105194815     244.784487   \n",
       "367  Obir|LOC105281047  Orug|SGF78634  Sinv|LOC105203057     306.311466   \n",
       "413  Obir|LOC105287676  Orug|SGF86030  Sinv|LOC105198509      92.576057   \n",
       "423  Obir|LOC105277964  Orug|SGF81562  Sinv|LOC105200782     102.323539   \n",
       "\n",
       "     Aech_log2FoldChange   Aech_pvalue    ...      Orug_pvalue  Orug_padj  \\\n",
       "305            -0.005591  9.749647e-01    ...         0.323920   0.471735   \n",
       "331             1.460857  2.037000e-08    ...         0.836298   0.891570   \n",
       "367            -0.077285  6.244275e-01    ...         0.000001   0.000016   \n",
       "413             0.841557  8.401890e-03    ...         0.463778   0.605464   \n",
       "423             1.464132  3.364315e-10    ...         0.471751   0.612367   \n",
       "\n",
       "     Obir_baseMean  Obir_log2FoldChange  Obir_pvalue  Obir_padj  \\\n",
       "305    2227.754133             0.432653     0.002878   0.041655   \n",
       "331     793.978290            -0.007736     0.963926   0.992221   \n",
       "367    2189.024661            -0.103909     0.607203   0.871456   \n",
       "413      83.684642            -0.166003     0.461690   0.789415   \n",
       "423    1136.701183             0.179205     0.234553   0.608615   \n",
       "\n",
       "     Sinv_baseMean  Sinv_log2FoldChange  Sinv_pvalue  Sinv_padj  \n",
       "305     391.232256            -0.051518     0.421668   0.585876  \n",
       "331     226.248598             0.423662     0.000122   0.000781  \n",
       "367     407.541439             0.260069     0.005161   0.019039  \n",
       "413      45.880269             0.219733     0.386031   0.551058  \n",
       "423     867.414114             0.762431     0.000251   0.001467  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# save to file. This table constitutes Supplementary Table 2\n",
    "df_combined.to_csv(\"ortholog_pvalue_table.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5) Identify all concordant differentially expressed genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 orthologs found with all p-values (adjusted) <= 0.05\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Aech</th>\n",
       "      <th>Cpla</th>\n",
       "      <th>Dqua</th>\n",
       "      <th>Hsal</th>\n",
       "      <th>Obir</th>\n",
       "      <th>Orug</th>\n",
       "      <th>Sinv</th>\n",
       "      <th>Aech_baseMean</th>\n",
       "      <th>Aech_log2FoldChange</th>\n",
       "      <th>Aech_pvalue</th>\n",
       "      <th>...</th>\n",
       "      <th>Orug_pvalue</th>\n",
       "      <th>Orug_padj</th>\n",
       "      <th>Obir_baseMean</th>\n",
       "      <th>Obir_log2FoldChange</th>\n",
       "      <th>Obir_pvalue</th>\n",
       "      <th>Obir_padj</th>\n",
       "      <th>Sinv_baseMean</th>\n",
       "      <th>Sinv_log2FoldChange</th>\n",
       "      <th>Sinv_pvalue</th>\n",
       "      <th>Sinv_padj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2059</th>\n",
       "      <td>Aech|LOC105147653</td>\n",
       "      <td>Cpla|SGF53173</td>\n",
       "      <td>Dqua|LOC106750697</td>\n",
       "      <td>Hsal|LOC105188195</td>\n",
       "      <td>Obir|LOC105278524</td>\n",
       "      <td>Orug|SGF66168</td>\n",
       "      <td>Sinv|LOC105200753</td>\n",
       "      <td>68.272121</td>\n",
       "      <td>2.711926</td>\n",
       "      <td>6.766716e-12</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000927</td>\n",
       "      <td>0.004607</td>\n",
       "      <td>1228.728806</td>\n",
       "      <td>1.381716</td>\n",
       "      <td>4.212394e-18</td>\n",
       "      <td>2.963560e-15</td>\n",
       "      <td>626.264624</td>\n",
       "      <td>2.441594</td>\n",
       "      <td>4.785615e-18</td>\n",
       "      <td>3.473843e-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Aech           Cpla               Dqua               Hsal  \\\n",
       "2059  Aech|LOC105147653  Cpla|SGF53173  Dqua|LOC106750697  Hsal|LOC105188195   \n",
       "\n",
       "                   Obir           Orug               Sinv  Aech_baseMean  \\\n",
       "2059  Obir|LOC105278524  Orug|SGF66168  Sinv|LOC105200753      68.272121   \n",
       "\n",
       "      Aech_log2FoldChange   Aech_pvalue      ...       Orug_pvalue  Orug_padj  \\\n",
       "2059             2.711926  6.766716e-12      ...          0.000927   0.004607   \n",
       "\n",
       "      Obir_baseMean  Obir_log2FoldChange   Obir_pvalue     Obir_padj  \\\n",
       "2059    1228.728806             1.381716  4.212394e-18  2.963560e-15   \n",
       "\n",
       "      Sinv_baseMean  Sinv_log2FoldChange   Sinv_pvalue     Sinv_padj  \n",
       "2059     626.264624             2.441594  4.785615e-18  3.473843e-16  \n",
       "\n",
       "[1 rows x 35 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pcols = [\"{}_{}\".format(sp,\"padj\") for sp in sp_short] # a list of all the pvalue columns\n",
    "lfc_cols = [ \"{}_log2FoldChange\".format(s) for s in sp_short ] # a list of all the LFC columns\n",
    "\n",
    "mask = (df_combined[pcols] <= 0.05).all(axis=1)\n",
    "\n",
    "print(\"{} orthologs found with all p-values (adjusted) <= 0.05\".format(mask.sum()))\n",
    "df_combined[mask]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This last cell shows that there is only one gene that has a p-value (FDR adjusted) ≤ 0.05 in all seven species. This gene is also over-expressed in reproductives in all seven species (ie, it is concordantly expressed in all seven species)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactively check table for all significant genes\n",
    "This section allows you to interactively view the effect of different significance thresholds and their effect on the number of genes significant in all species. You will need to have enabled ipywidgets in your notebook to use this section (see [here](http://ipywidgets.readthedocs.io/en/latest/user_install.html) for details)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipywidgets import interact, interact_manual\n",
    "import ipywidgets as widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "pcols = [\"{}_{}\".format(sp,\"padj\") for sp in sp_short]         # a list of all the pvalue columns\n",
    "lfc_cols = [ \"{}_log2FoldChange\".format(s) for s in sp_short ] # a list of all the LFC columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_pvalue(thresh=0.05):\n",
    "    mask = (df_combined[pcols] <= thresh).all(axis=1)\n",
    "    print(\"{} ortholog(s) found with all p-values (adjusted) <= {}\".format(mask.sum(), thresh))\n",
    "    return df_combined[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Note that this changes the adjusted pvalue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ac0a1a1758ca4f49991409f412dbd302",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.05, description='thresh', max=1.0, step=0.01), Output()), _dom_class…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(set_pvalue, thresh=(0.0,1.0,0.01) );  # widgets.FloatSlider(min=0.,max=0.5,step=0.01,value=0.05));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6) Check number of orthologous DEGs per species\n",
    "The number of orthologous DEGs will be smaller than the total number of DEGs for each individual species, since only the single-copy orthologs are represented."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SPECIES       DEGs   ODEGs (%) \n",
      "Acromyrmex     2262  1050  46.4\n",
      "Camponotus     5304  2579  48.6\n",
      "Dinoponera      408   171  41.9\n",
      "Harpegnathos     35    16  45.7\n",
      "Odontomachus   4987  2270  45.5\n",
      "Ooceraea        784   363  46.3\n",
      "Solenopsis     4116  1925  46.8\n"
     ]
    }
   ],
   "source": [
    "pvs = [ sp + \"_padj\" for sp in sp_short]\n",
    "species_specific_deg_num = []  # this variable is used later for the simulations\n",
    "print(\"{0:<13s} {2:<6s} {1:<4s} {3:<4s}\".format(\"SPECIES\", \"ODEGs\", \"DEGs\", \"(%)\"))\n",
    "for p,sp in zip(pvs, species):\n",
    "    species_specific_deg_num.append((df_combined[p] <= 0.05).sum())\n",
    "    print(\"{0:<13s} {2:-5d} {1:-5d}  {3:.1f}\".format(sp, \n",
    "                                         (df_combined[p] <= 0.05).sum(), \n",
    "                                         (df_pval_dic[sp][\"padj\"] <= 0.05).sum(),\n",
    "                                         100*(df_combined[p] <= 0.05).sum() / (df_pval_dic[sp][\"padj\"] <= 0.05).sum()\n",
    "                                        )\n",
    "         )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display fold-change and p values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "show values for Insulin-like peptides 1 and 2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Aech_padj</th>\n",
       "      <th>Cpla_padj</th>\n",
       "      <th>Dqua_padj</th>\n",
       "      <th>Hsal_padj</th>\n",
       "      <th>Orug_padj</th>\n",
       "      <th>Obir_padj</th>\n",
       "      <th>Sinv_padj</th>\n",
       "      <th>Aech_log2FoldChange</th>\n",
       "      <th>Cpla_log2FoldChange</th>\n",
       "      <th>Dqua_log2FoldChange</th>\n",
       "      <th>Hsal_log2FoldChange</th>\n",
       "      <th>Orug_log2FoldChange</th>\n",
       "      <th>Obir_log2FoldChange</th>\n",
       "      <th>Sinv_log2FoldChange</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8648</th>\n",
       "      <td>0.022555</td>\n",
       "      <td>0.006219</td>\n",
       "      <td>0.58577</td>\n",
       "      <td>0.000114</td>\n",
       "      <td>0.120454</td>\n",
       "      <td>0.02609</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>-1.193374</td>\n",
       "      <td>-0.758144</td>\n",
       "      <td>-0.483432</td>\n",
       "      <td>-1.719643</td>\n",
       "      <td>-0.750206</td>\n",
       "      <td>-0.759376</td>\n",
       "      <td>-1.308077</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Aech_padj  Cpla_padj  Dqua_padj  Hsal_padj  Orug_padj  Obir_padj  \\\n",
       "8648   0.022555   0.006219    0.58577   0.000114   0.120454    0.02609   \n",
       "\n",
       "      Sinv_padj  Aech_log2FoldChange  Cpla_log2FoldChange  \\\n",
       "8648   0.000009            -1.193374            -0.758144   \n",
       "\n",
       "      Dqua_log2FoldChange  Hsal_log2FoldChange  Orug_log2FoldChange  \\\n",
       "8648            -0.483432            -1.719643            -0.750206   \n",
       "\n",
       "      Obir_log2FoldChange  Sinv_log2FoldChange  \n",
       "8648            -0.759376            -1.308077  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combined[df_combined.Obir == \"Obir|LOC105283606\"][pvs + lfc_cols]  # ILP1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Aech_padj</th>\n",
       "      <th>Cpla_padj</th>\n",
       "      <th>Dqua_padj</th>\n",
       "      <th>Hsal_padj</th>\n",
       "      <th>Orug_padj</th>\n",
       "      <th>Obir_padj</th>\n",
       "      <th>Sinv_padj</th>\n",
       "      <th>Aech_log2FoldChange</th>\n",
       "      <th>Cpla_log2FoldChange</th>\n",
       "      <th>Dqua_log2FoldChange</th>\n",
       "      <th>Hsal_log2FoldChange</th>\n",
       "      <th>Orug_log2FoldChange</th>\n",
       "      <th>Obir_log2FoldChange</th>\n",
       "      <th>Sinv_log2FoldChange</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2059</th>\n",
       "      <td>4.391351e-10</td>\n",
       "      <td>0.019167</td>\n",
       "      <td>0.037204</td>\n",
       "      <td>0.013436</td>\n",
       "      <td>0.004607</td>\n",
       "      <td>2.963560e-15</td>\n",
       "      <td>3.473843e-16</td>\n",
       "      <td>2.711926</td>\n",
       "      <td>1.081158</td>\n",
       "      <td>1.086033</td>\n",
       "      <td>0.898978</td>\n",
       "      <td>1.123209</td>\n",
       "      <td>1.381716</td>\n",
       "      <td>2.441594</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Aech_padj  Cpla_padj  Dqua_padj  Hsal_padj  Orug_padj     Obir_padj  \\\n",
       "2059  4.391351e-10   0.019167   0.037204   0.013436   0.004607  2.963560e-15   \n",
       "\n",
       "         Sinv_padj  Aech_log2FoldChange  Cpla_log2FoldChange  \\\n",
       "2059  3.473843e-16             2.711926             1.081158   \n",
       "\n",
       "      Dqua_log2FoldChange  Hsal_log2FoldChange  Orug_log2FoldChange  \\\n",
       "2059             1.086033             0.898978             1.123209   \n",
       "\n",
       "      Obir_log2FoldChange  Sinv_log2FoldChange  \n",
       "2059             1.381716             2.441594  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combined[df_combined.Obir == \"Obir|LOC105278524\"][pvs + lfc_cols]  # ILP2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def view_gene(choice=\"Obir|LOC105278524\"):   # default is ILP2\n",
    "    # plot the log2FoldChange of all genes of interest\n",
    "    df = df_combined[df_combined.Obir == choice]\n",
    "    if len(df) == 0:\n",
    "        return \"No gene found\"\n",
    "    \n",
    "    ax = df[[\"Obir\"] + lfc_cols].set_index(\"Obir\").plot.bar(figsize=(10,5))\n",
    "\n",
    "    # get the pvalues of these genes:\n",
    "    df_cpvals = df[[\"Obir\"] + pvs].set_index(\"Obir\")\n",
    "\n",
    "    # annotate the signicantly differentially expressed genes with an asterisk\n",
    "    x_pos = [rect.get_x() + rect.get_width()/2. for rect in ax.patches]\n",
    "    y_pos = [rect.get_y() + rect.get_height() -0.35 + 0.2*(rect.get_height())/abs(rect.get_height()) for rect in ax.patches]\n",
    "    scores = [{1:\"*\",0:\"\"}[p<=0.05] for p in df_cpvals.values.flatten(\"F\")]\n",
    "    for rect, x, y, s in zip(ax.patches, x_pos, y_pos, scores):\n",
    "            ax.text(x, \n",
    "                    y,\n",
    "                    s,\n",
    "                    ha='center', va='bottom',\n",
    "                    size=32,\n",
    "                    color='r'\n",
    "                   )\n",
    "\n",
    "    # add axes labels\n",
    "    plt.ylabel(\"log2(fold-change)\", size=16)\n",
    "    plt.yticks(size=14)\n",
    "    plt.xlabel(ax.get_xticklabels()[0].get_text(), size=16)\n",
    "    plt.xticks([], [])\n",
    "    \n",
    "    plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0, prop={'size': 16});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### interactively view results for a single ortholog group\n",
    "In the text box below, enter the 4-letter species code plus gene ID for any gene in the single-copy ortholog table, and it will display the log-fold changes and indicate whether the gene is significantly differentially expressed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "25060f4d23c2424e87f38aaae3629f0b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Text(value='Obir|LOC105278524', description='choice'), Button(description='Run Interact'…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact_manual(view_gene, choice=\"Obir|LOC105278524\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Concordant DEGs analysis\n",
    "While there is only a single gene differentially expressed across all seven species, there are varied numbers of genes significant and concordant among all the combinations of those seven species. Attempting to draw a Venn diagram would be of little use, as it becomes difficult to evaluate each comparison and see trends among so many sets.\n",
    "Thus, here we will identify the number of genes that are signficant and concordant for each possible combination of the seven species, and plot the values against the number of species compared."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "c = {i:[] for i in np.arange(len(species)) + 1}\n",
    "for n in np.arange(len(species)) + 1:\n",
    "    for combo in combinations(zip(pcols,lfc_cols), n):\n",
    "        ps,ls = list(zip(*combo))\n",
    "        ps = list(ps)\n",
    "        ls = list(ls)\n",
    "        concordant_degs = ((\n",
    "                            df_combined[ps] <= 0.05\n",
    "                             ).all(axis=1) & (\n",
    "                            abs((df_combined[ls] / abs(df_combined[ls])).sum(axis=1)) == len(ls)\n",
    "                                        ) \n",
    "                            )\n",
    "        #print(\"# sp = {0} # cdegs = {1}\".format(len(ls), concordant_degs.sum()))\n",
    "        c[n].append(concordant_degs.sum())\n",
    "df_cdegs = pd.DataFrame({ k:pd.Series(v) for k,v in c.items()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "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>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1050.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2579.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>171.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>197</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>41</td>\n",
       "      <td>7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2270.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>249</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>363.0</td>\n",
       "      <td>542.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1925.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1346.0</td>\n",
       "      <td>18</td>\n",
       "      <td>130</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>123.0</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>787.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>58.0</td>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>175</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>52.0</td>\n",
       "      <td>62</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>105.0</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>606.0</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>449</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76</td>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26</td>\n",
       "      <td>44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         1       2    3    4     5    6    7\n",
       "0   1050.0   386.0   11    2   2.0  1.0  1.0\n",
       "1   2579.0    31.0    5    5   1.0  2.0  NaN\n",
       "2    171.0     7.0  197    3   2.0  1.0  NaN\n",
       "3     16.0   276.0   41    7   2.0  2.0  NaN\n",
       "4   2270.0    88.0  249    2   4.0  1.0  NaN\n",
       "5    363.0   542.0    2    2   2.0  1.0  NaN\n",
       "6   1925.0    57.0   13    5   1.0  1.0  NaN\n",
       "7      NaN     7.0    3   23   2.0  NaN  NaN\n",
       "8      NaN  1346.0   18  130   2.0  NaN  NaN\n",
       "9      NaN   123.0    2   32  18.0  NaN  NaN\n",
       "10     NaN   787.0    2    2   1.0  NaN  NaN\n",
       "11     NaN     2.0    6    1   2.0  NaN  NaN\n",
       "12     NaN    58.0   33    2   1.0  NaN  NaN\n",
       "13     NaN    12.0  175    2   2.0  NaN  NaN\n",
       "14     NaN    52.0   62    9   1.0  NaN  NaN\n",
       "15     NaN     2.0    2    2   1.0  NaN  NaN\n",
       "16     NaN     2.0   38    1   2.0  NaN  NaN\n",
       "17     NaN     7.0    7    2   1.0  NaN  NaN\n",
       "18     NaN   105.0   25    2   5.0  NaN  NaN\n",
       "19     NaN   606.0    2   27   1.0  NaN  NaN\n",
       "20     NaN   149.0    2    2   1.0  NaN  NaN\n",
       "21     NaN     NaN    5    1   NaN  NaN  NaN\n",
       "22     NaN     NaN   63    2   NaN  NaN  NaN\n",
       "23     NaN     NaN  449    5   NaN  NaN  NaN\n",
       "24     NaN     NaN   76   17   NaN  NaN  NaN\n",
       "25     NaN     NaN    2    5   NaN  NaN  NaN\n",
       "26     NaN     NaN    1    1   NaN  NaN  NaN\n",
       "27     NaN     NaN    2    2   NaN  NaN  NaN\n",
       "28     NaN     NaN    6    2   NaN  NaN  NaN\n",
       "29     NaN     NaN   26   44   NaN  NaN  NaN\n",
       "30     NaN     NaN    7    1   NaN  NaN  NaN\n",
       "31     NaN     NaN    1    2   NaN  NaN  NaN\n",
       "32     NaN     NaN    2    1   NaN  NaN  NaN\n",
       "33     NaN     NaN    2    6   NaN  NaN  NaN\n",
       "34     NaN     NaN   62    1   NaN  NaN  NaN"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cdegs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "ax = sns.boxplot(data=df_cdegs, color=\"skyblue\", ax=ax)\n",
    "\n",
    "medians = df_cdegs.median().values\n",
    "median_labels = [str(x) for x in medians]\n",
    "\n",
    "pos = np.arange(len(medians)) \n",
    "for tick,label in zip(pos,ax.get_xticklabels()):\n",
    "    ax.text(pos[tick], \n",
    "            medians[tick] + 40, \n",
    "            median_labels[tick], \n",
    "            horizontalalignment='center', size=12, color='k', weight='semibold')\n",
    "    \n",
    "    \n",
    "plt.title(\"Number of DEGs concordant across all combinations of six ant species\")\n",
    "plt.xlabel(\"Number of species compared\")\n",
    "plt.ylabel(\"Number of concordant DEGs\")\n",
    "plt.xticks(size=14)\n",
    "plt.yticks(size=14)\n",
    "plt.savefig(\"concordant_degs_observed.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7) Calculate average concordance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.75% of genes are concordant in all 7 species\n"
     ]
    }
   ],
   "source": [
    "lfc = [ sp + \"_log2FoldChange\" for sp in sp_short ]\n",
    "# convert log fold changes into the direction:\n",
    "df_direction = df_combined[lfc] / abs(df_combined[lfc])\n",
    "\n",
    "# count number of genes concordant across all spp:\n",
    "print(\"{:.2f}% of genes are concordant in all {:d} species\".format(\n",
    "            100 * (abs(df_direction.sum(axis=1)) == len(species)).sum() / len(df_direction),\n",
    "            len(species))\n",
    "     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aech|Cpla 60.54%\n",
      "Aech|Dqua 52.41%\n",
      "Aech|Hsal 41.46%\n",
      "Aech|Orug 58.75%\n",
      "Aech|Obir 55.37%\n",
      "Aech|Sinv 66.42%\n",
      "Cpla|Dqua 54.38%\n",
      "Cpla|Hsal 36.95%\n",
      "Cpla|Orug 77.98%\n",
      "Cpla|Obir 56.94%\n",
      "Cpla|Sinv 59.13%\n",
      "Dqua|Hsal 52.95%\n",
      "Dqua|Orug 53.81%\n",
      "Dqua|Obir 50.82%\n",
      "Dqua|Sinv 53.06%\n",
      "Hsal|Orug 37.66%\n",
      "Hsal|Obir 49.27%\n",
      "Hsal|Sinv 46.01%\n",
      "Orug|Obir 56.84%\n",
      "Orug|Sinv 57.55%\n",
      "Obir|Sinv 60.63%\n",
      "54.24% average concordance between any two genes\n"
     ]
    }
   ],
   "source": [
    "conc_list = []\n",
    "for sp1,sp2 in combinations(lfc,2):\n",
    "    conc_list.append((abs(df_direction[[sp1,sp2]].sum(axis=1)) == 2).sum() / len(df_direction))\n",
    "    print(\"{}|{} {:.2f}%\".format(sp1[:4],sp2[:4],100*conc_list[-1]))\n",
    "print(\"{:.2f}% average concordance between any two genes\".format(np.mean(conc_list) * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8) Generate heatmap of genes that are differentially expressed in any subset of five species\n",
    "To investigate the impact of individual species on the comparative analysis, we can systematically remove two species from our list of seven, and look at the resultant lists of concordant DEGs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#set (adjusted) pvalue threshold\n",
    "pthresh = 0.05\n",
    "\n",
    "#set number of species [between 1 and 7]\n",
    "spnum = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_subset(s, spnum=5, pthresh=0.05):\n",
    "    \"\"\"\n",
    "    This function operates on a series of pvals and log fold changes (in \n",
    "    that order). It     finds all pvals less than the threshold, then \n",
    "    looks to see if there are     at least as many concordant genes as \n",
    "    in the subset number. If so, it returns true - ie, there are at \n",
    "    least spnum species concordant and signficantly differentially \n",
    "    expressed for this gene.\n",
    "    \"\"\"\n",
    "    mask = s[:7]<=0.05\n",
    "    mask.set_axis(lfc_cols, inplace=True)\n",
    "    num_upreg = (s[7:][mask]>0).sum()\n",
    "    num_downreg = (s[7:][mask]<0).sum()\n",
    "    \n",
    "    if num_downreg >= spnum or num_upreg >= spnum:  # can be concordant up or down\n",
    "        return True\n",
    "    else:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "subset_mask = df_combined[pcols+lfc_cols].apply(filter_subset, args=(spnum,pthresh), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 24 unique genes that are differentially expressed among subsets of 5 species\n"
     ]
    }
   ],
   "source": [
    "print(\"There are {} unique genes that are differentially expressed among subsets of {} species\".format(subset_mask.sum(), spnum)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.clustermap(df_combined[subset_mask][lfc_cols], \n",
    "                    figsize=(8,10), \n",
    "                    yticklabels=df_combined[subset_mask]['Obir'].values,\n",
    "                    xticklabels=species,\n",
    "                    cmap='RdBu',\n",
    "                    center=0,\n",
    "                   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Aech_padj</th>\n",
       "      <th>Cpla_padj</th>\n",
       "      <th>Dqua_padj</th>\n",
       "      <th>Hsal_padj</th>\n",
       "      <th>Orug_padj</th>\n",
       "      <th>Obir_padj</th>\n",
       "      <th>Sinv_padj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2273</th>\n",
       "      <td>0.000185</td>\n",
       "      <td>0.040483</td>\n",
       "      <td>0.001509</td>\n",
       "      <td>0.947267</td>\n",
       "      <td>1.205808e-12</td>\n",
       "      <td>0.567974</td>\n",
       "      <td>8.383073e-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Aech_padj  Cpla_padj  Dqua_padj  Hsal_padj     Orug_padj  Obir_padj  \\\n",
       "2273   0.000185   0.040483   0.001509   0.947267  1.205808e-12   0.567974   \n",
       "\n",
       "         Sinv_padj  \n",
       "2273  8.383073e-07  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combined[df_combined.Obir == \"Obir|LOC105282526\"][pcols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
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 "nbformat_minor": 2
}