notebooks/microglial_expression_analysis_20181211.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RNAseq analysis of microglial cells\n",
    "> Goncalo Rodrigues _et al._ (2018)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# METHODS\n",
    "### RNASeq Quality check and alignment\n",
    "RNASeq fastq files were analyzed with FastQC[1] v0.11.7 to assess sequence base quality, per-base sequence content, GC content, N content, and the sequence length distribution. Reads were subsequently trimmed using Trimmomatic[2] v0.38, to remove Illumina adapters, leading and trailing bases with score ≤ 3, all bases after the sliding window average ≤ 15, and all edited reads ≤ 36 bp.\n",
    "Reads were aligned to _Mus musculus_ annotation GRCm38.p6 using Salmon[3] v0.10.0. Default parameters were used for building the index and for alignment.\n",
    "\n",
    "\n",
    "### Differential expression analysis\n",
    "DESeq2[4] v1.18.1 was used to assess differential gene expression using the likelihood ratio test, with the model `~ replicate + condition` analyzed against the reduced model `~ replicate`. Variance Stabilized Transformed gene counts were used to identify outliers, using both Principal Component Analysis and sample clustering (using the euclidean distance metric and the complete clustering method). Two samples - wildtype replicate C and PBS replicate C - were removed from further analysis.\n",
    "\n",
    "To identify significant contrasts between treatments, a _post hoc_ analysis of genes differentially expressed according to LRT analysis was performed. DESeq2 was used to perform the nbinomial Wald test for contrasts between PBS, WT, KO1 and KO2. \n",
    "\n",
    "The focal gene set was identified as those genes in which:\n",
    "* the likelihood ratio test was significant (*p* ≤ 0.05)\n",
    "* WT and PBS were significantly differentially expressed (*p* ≤ 0.05)\n",
    "* WT was significantly different from both KO1 and KO2 (*p* ≤ 0.05 in each contrast)\n",
    "* KO1 and KO2 were concordantly up- or down- regulated with regard to WT\n",
    "\n",
    "Log2(Fold change) values and _p_ values are reported according to the Wald tests.\n",
    "\n",
    "Gene names were mapped to entrez gene identifiers using ensembl biomart[5], mouse version GRCm38.p6.\n",
    "\n",
    "All scripts and output of the differential analysis are available at [![DOI](https://zenodo.org/badge/171735540.svg)](https://zenodo.org/badge/latestdoi/171735540)\n",
    ". The R Script used for differential expression analysis can be found in supplementary file differential-expression-analysis.R The python script used for subsequent post hoc analysis can be found in the Jupyter notebooks endothelial_expression_analysis.ipynb and microglial_expression_analysis.ipynb.\n",
    "\n",
    "\n",
    "### Bibliography\n",
    "1. Andrews, S. FastQC: a quality control tool for high throughput sequence data. (2010). at <http://www.bioinformatics.babraham.ac.uk/projects/fastqc>\n",
    "2. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).\n",
    "3. Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).\n",
    "4. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and  dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).\n",
    "5. Zerbino, D. R. et al. Ensembl 2018. Nucleic Acids Res. 46, D754–D761 (2018).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Samples\n",
    "\n",
    "| condition | cell | replicate | date | sample_id | filename |\n",
    "|---|---|---|---|---|---|\n",
    "| Knull | EC | A | 3-7-2018 | S25 | 01-EC---3-7-2018_S25_R1_001.quant |\n",
    "| Knull | MG | A | 3-7-2018 | S26 | 01-MG---3-7-2018_S26_R1_001.quant |\n",
    "| Knull | EC | C | 2-22-2018 | S5 | 10-EC---2-22-2018_S5_R1_001.quant |\n",
    "| Knull | EC | B | 3-2-2018 | S15 | 10-EC--p8--3-2-2018_S15_R1_001.quant |\n",
    "| Knull | MG | C | 2-22-2018 | S6 | 10-MG---2-22-2018_S6_R1_001.quant |\n",
    "| Knull | MG | B | 3-2-2018 | S16 | 10-MG--p12--3-2-2018_S16_R1_001.quant |\n",
    "| KO1 | EC | C | 2-22-2018 | S7 | 57-1-EC---2-22-2018_S7_R1_001.quant |\n",
    "| KO1 | EC | B | 3-2-2018 | S17 | 57-1-EC--p8--3-2-2018_S17_R1_001.quant |\n",
    "| KO1 | MG | C | 2-22-2018 | S8 | 57-1-MG---2-22-2018_S8_R1_001.quant |\n",
    "| KO1 | MG | B | 3-2-2018 | S18 | 57-1-MG--p12--3-2-2018_S18_R1_001.quant |\n",
    "| KO1 | EC | A | 3-7-2018 | S27 | 57-EC---3-7-2018_S27_R1_001.quant |\n",
    "| KO1 | MG | A | 3-7-2018 | S28 | 57-MG---3-7-2018_S28_R1_001.quant |\n",
    "| KO2 | EC | C | 2-22-2018 | S9 | 59-1-EC---2-22-2018_S9_R1_001.quant |\n",
    "| KO2 | EC | B | 3-2-2018 | S19 | 59-1-EC--p8--3-2-2018_S19_R1_001.quant |\n",
    "| KO2 | MG | C | 2-22-2018 | S10 | 59-1-MG---2-22-2018_S10_R1_001.quant |\n",
    "| KO2 | MG | B | 3-2-2018 | S20 | 59-1-MG--p12--3-2-2018_S20_R1_001.quant |\n",
    "| KO2 | EC | A | 3-7-2018 | S29 | 59-EC---3-7-2018_S29_R1_001.quant |\n",
    "| KO2 | MG | A | 3-7-2018 | S30 | 59-MG---3-7-2018_S30_R1_001.quant |\n",
    "| PBS | EC | A | 3-7-2018 | S21 | PBS-EC---3-7-2018_S21_R1_001.quant |\n",
    "| PBS | EC | B | 3-2-2018 | S11 | PBSg-EC---3-2-2018_S11_R1_001.quant |\n",
    "| PBS | EC | C | 2-22-2018 | S1 | PBSg-EC--p8--2-22-2018_S1_R1_001.quant |\n",
    "| PBS | MG | B | 3-2-2018 | S12 | PBSg-MG---3-2-2018_S12_R1_001.quant |\n",
    "| PBS | MG | C | 2-22-2018 | S2 | PBSg-MG--p12--2-22-2018_S2_R1_001.quant |\n",
    "| PBS | MG | A | 3-7-2018 | S22 | PBS-MG---3-7-2018_S22_R1_001.quant |\n",
    "| WT | EC | C | 2-22-2018 | S3 | WT-EC---2-22-2018_S3_R1_001.quant |\n",
    "| WT | EC | A | 3-7-2018 | S23 | WT-EC---3-7-2018_S23_R1_001.quant |\n",
    "| WT | EC | B | 3-2-2018 | S13 | WT-EC--p8--3-2-2018_S13_R1_001.quant |\n",
    "| WT | MG | C | 2-22-2018 | S4 | WT-MG---2-22-2018_S4_R1_001.quant |\n",
    "| WT | MG | A | 3-7-2018 | S24 | WT-MG---3-7-2018_S24_R1_001.quant |\n",
    "| WT | MG | B | 3-2-2018 | S14 | WT-MG--p12--3-2-2018_S14_R1_001.quant |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NOTEBOOK ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.spatial.distance import pdist, squareform, warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.simplefilter('ignore',)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data\n",
    "this includes the DESeq2 LRT analysis results, and the variance stabilized transformed (VST) expression values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mg = pd.read_csv(\"../DESeq2_R-project/res_mg-results.csv\", sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead 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>Unnamed: 0</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>0</th>\n",
       "      <td>11287</td>\n",
       "      <td>1.934590</td>\n",
       "      <td>-0.593551</td>\n",
       "      <td>4.247045</td>\n",
       "      <td>1.276578</td>\n",
       "      <td>0.734702</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11298</td>\n",
       "      <td>0.316304</td>\n",
       "      <td>2.098142</td>\n",
       "      <td>4.211749</td>\n",
       "      <td>0.641457</td>\n",
       "      <td>0.886880</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11302</td>\n",
       "      <td>12.814439</td>\n",
       "      <td>-3.345539</td>\n",
       "      <td>3.946821</td>\n",
       "      <td>2.067634</td>\n",
       "      <td>0.558488</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11303</td>\n",
       "      <td>2981.665707</td>\n",
       "      <td>0.299286</td>\n",
       "      <td>0.285279</td>\n",
       "      <td>3.675644</td>\n",
       "      <td>0.298686</td>\n",
       "      <td>0.903654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11304</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     baseMean  log2FoldChange     lfcSE      stat    pvalue  \\\n",
       "0       11287     1.934590       -0.593551  4.247045  1.276578  0.734702   \n",
       "1       11298     0.316304        2.098142  4.211749  0.641457  0.886880   \n",
       "2       11302    12.814439       -3.345539  3.946821  2.067634  0.558488   \n",
       "3       11303  2981.665707        0.299286  0.285279  3.675644  0.298686   \n",
       "4       11304     0.000000             NaN       NaN       NaN       NaN   \n",
       "\n",
       "       padj  \n",
       "0       NaN  \n",
       "1       NaN  \n",
       "2       NaN  \n",
       "3  0.903654  \n",
       "4       NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NB: NaN values arise when the LRT statistic cannot be calculated (if all values are 0), or when the gene is filtered due to low basemean and high variance (padj only)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "238"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view the number of differentially expressed genes:\n",
    "df_mg[df_mg.padj <= 0.05].shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Load the gene name table**\n",
    "\n",
    "This table allows conversion between isoform, gene, uniprot gene id and ncbi gene name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        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>refseq_mrna</th>\n",
       "      <th>entrezgene</th>\n",
       "      <th>uniprot_gn</th>\n",
       "      <th>external_gene_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NM_001001130</td>\n",
       "      <td>22746</td>\n",
       "      <td>Q7TMC9</td>\n",
       "      <td>Zfp85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NM_001001144</td>\n",
       "      <td>235623</td>\n",
       "      <td>Q6GQT6</td>\n",
       "      <td>Scap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NM_001001152</td>\n",
       "      <td>238690</td>\n",
       "      <td>Q7M6X6</td>\n",
       "      <td>Zfp458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NM_001001152</td>\n",
       "      <td>238690</td>\n",
       "      <td>Q6P5C7</td>\n",
       "      <td>Zfp458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NM_001001160</td>\n",
       "      <td>330369</td>\n",
       "      <td>Q6NS60</td>\n",
       "      <td>Fbxo41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    refseq_mrna  entrezgene uniprot_gn external_gene_name\n",
       "1  NM_001001130       22746     Q7TMC9              Zfp85\n",
       "2  NM_001001144      235623     Q6GQT6               Scap\n",
       "3  NM_001001152      238690     Q7M6X6             Zfp458\n",
       "4  NM_001001152      238690     Q6P5C7             Zfp458\n",
       "5  NM_001001160      330369     Q6NS60             Fbxo41"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_names = pd.read_csv(\"../scripts/transcript-name-conversion.csv\")\n",
    "\n",
    "# convert entrez geneid to integer\n",
    "df_names['entrezgene'] = df_names.entrezgene.apply(lambda x: 0 if pd.isnull(x) else int(x))\n",
    "df_names.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Load the expression data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mg_exp = pd.read_csv(\"../DESeq2_R-project/counts-mg-vst.csv\", \n",
    "                        delim_whitespace=True\n",
    "                       )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mg_merge = pd.merge(left=df_mg, right=df_names.iloc[:, 1:], \n",
    "                        left_on=\"Unnamed: 0\", right_on=\"entrezgene\",\n",
    "                        how=\"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mg_merge[df_mg_merge.padj <= 0.05].to_csv(\"MG_LRT_DEGs.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "238 differentially expressed genes:\n",
      "Acod1,Ccl5,Ifit1,Ccl2,Cxcl2,Cxcl1,Usp18,Sod2,Cxcl3,Irgm2,Lgals3bp,Map3k2,Ccrl2,Ptgs2,Fcgr1,Ifitm3,Cxcl10,Cybb,Bst2,Tnfrsf1b,Gbp9,Rtp4,Siglec1,Stat2,Zbp1,Wdr35,Tnf,Gsap,Slc31a2,Il1b,Mfsd8,Mfap3l,B2m,Ifit3b,Sbno1,Syk,Ints14,H2-Q4,Grap,Smug1,Atp10a,Pros1,Ctsc,Rnaset2b,Rsad2,Skiv2l2,Mgat4a,Ifi209,Tsc1,Rsbn1,Chchd10,Plau,F2r,Uba7,Irgm1,Poc5,Glrx,Tmem87a,Tlr2,Kctd12,Tnfaip3,Laptm5,Sp100,Ttll4,Aldh18a1,Mdm4,Gabpb2,Flna,Ifi27l2a,Zc3hav1,Ifnb1,Tjp2,Phf11d,Rab43,Siglech,Milr1,Mfsd9,Wars,Clec4e,Parp14,Atp6v0d2,Ifi205,Heatr3,Irf9,Zcwpw1,Nrp2,Pla2g7,Itgb1bp1,Virma,Shisa5,Cd53,Ipo13,Wdr92,Cdk5rap2,Nkiras2,Parp12,H2-T24,Mif,Psen1,Zfp646,Gzf1,Rab11fip1,Utp20,Zdhhc7,Pcdhgc3,Zfp248,Atf7,Echs1,Map2k3,Trp53inp2,Lilr4b,Slfn2,Ndst2,Gng2,Pld4,Ier3,Trex1,Slfn1,Traf3ip1,Aldh2,Abcf3,Mmp14,Iqgap1,Synj2bp,Ap3d1,Lta4h,Tpcn2,Ifit3,H2-T23,Sgk1,Rab3gap2,Adar,3830406C13Rik,Pim1,Sart3,Plpp3,Tmem147,Rhob,Flvcr2,Smpd4,U2surp,Pkn2,Fam134b,Ampd2,Spp1,Pole2,Sfxn1,Capzb,Plagl2,Ms4a7,Fbxo11,Ctsd,Fbxo9,Zfyve1,Bclaf1,Ccdc88a,Scoc,Plxdc2,Rp2,Prkab1,Ccng1,Baz2b,Hk1,Atp6v1c1,Mt2,Atp6v0a2,Mesd,Hyou1,Aacs,Nfkbid,Poldip3,Dtx3l,Cxcl16,Faf2,Zfp119b,Hbs1l,Arpc5l,Golga3,Mettl21a,Xk,Phgdh,Lrch3,Sec24b,Ptpre,H2-Q7,Il1a,Herc6,Smg1,Nfkbia,Cog1,Atp13a1,Nmt1,Fig4,Sik3,Trpc4ap,Tmem87b,Dhx38,Herc3,Cep57,Ccdc93,Abtb1,Micall1,Gdf15,Cd52,Slc35a2,Cox6a2,Gosr1,Slc25a36,Slu7,Zbtb2,Slc2a10,Tsg101,Irf7,Ddx24,Tspan17,Keap1,Psat1,Numa1,Ap1g2,Phf11b,Tmem260,Uqcrc1,Lpcat2,Rassf5,Desi2,Nod1,Pard6b,B430306N03Rik,Gbp10,Ankfy1,Slfn8,Gm11127,Nfkb2,Sept11,Fam207a,Tbcd,Akap6,C5ar1\n"
     ]
    }
   ],
   "source": [
    "deg_gene_names = df_mg_merge[df_mg_merge.padj <= 0.05\n",
    "                            ].sort_values(\"padj\"\n",
    "                            ).external_gene_name.unique()\n",
    "print(len(deg_gene_names), \"differentially expressed genes:\")\n",
    "print(\",\".join(deg_gene_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "238 differentially expressed genes (entrez id):\n",
      "16365,20304,15957,20296,20310,14825,24110,20656,330122,54396,19039,26405,54199,19225,14129,66141,15945,13058,69550,21938,236573,67775,20612,20847,58203,74682,21926,212167,20530,16176,72175,71306,12010,667370,243272,20963,69882,15015,71520,71726,11982,19128,13032,68195,58185,72198,269181,236312,64930,229675,103172,18792,14062,74153,15944,67463,93692,211499,24088,239217,21929,16792,20684,67534,56454,17248,213054,192176,76933,78781,15977,21873,219132,69834,233274,380732,211798,22375,56619,547253,242341,226695,234549,16391,381678,18187,27226,16413,66185,66940,12508,230673,103784,214444,71966,243771,15042,17319,19164,233905,74533,75767,70683,102193,93706,72720,223922,93747,26397,68728,14727,20556,17423,14702,104759,15937,22040,20555,74019,11669,27406,17387,29875,24071,11776,16993,233979,15959,15040,20393,98732,56417,218734,18712,53890,67916,69804,11852,217721,77626,67958,109333,66270,109674,20750,18974,14057,12345,54711,109225,225055,13033,71538,217695,72567,108686,56367,67448,19889,19079,12450,407823,15275,66335,17750,21871,67943,12282,78894,243910,73826,209200,66102,76577,240120,56422,74192,269682,67099,22439,236539,70144,99683,19267,15018,16175,67138,233789,18035,16834,170759,18107,103199,70661,56407,72477,64340,73998,74360,70829,80283,27008,23886,23833,22232,12862,53334,192287,193116,381990,170441,22088,54123,27225,74257,50868,107272,101706,11766,236451,218989,22273,270084,54354,78825,107607,58220,320148,626578,11736,276950,100529082,18034,52398,108707,108903,238161,12273\n"
     ]
    }
   ],
   "source": [
    "deg_gene_names = df_mg_merge[df_mg_merge.padj <= 0.05\n",
    "                            ].sort_values(\"padj\").entrezgene.unique()\n",
    "print(len(deg_gene_names), \"differentially expressed genes (entrez id):\")\n",
    "print(\",\".join([str(i) for i in deg_gene_names]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_name_conversion = df_mg_merge.set_index(\"entrezgene\").external_gene_name.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "degs_mg = df_mg[df_mg.padj <= 0.05][\"Unnamed: 0\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mg_degs_exp = df_mg_exp[df_mg_exp.index.isin(degs_mg)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1080 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(method='complete', metric='euclidean',\n",
    "                data=df_mg_degs_exp, z_score=0, \n",
    "                yticklabels=[s_name_conversion.loc[idx] for idx in df_mg_degs_exp.index],\n",
    "               figsize=(14,15)\n",
    "              )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# _post hoc_ analysis of DEGs\n",
    "This is to identify, from among the DEGs already found above with the LRT model, those contrasts for which there are significant differences between conditions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## load _post hoc_ data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# suffix indicates the contrast analysed\n",
    "fp_pw = \"../DESeq2_R-project/res_mg_wald_PW-results.csv\"\n",
    "fp_pk1 = \"../DESeq2_R-project/res_mg_wald_PK1-results.csv\"\n",
    "fp_pk2 = \"../DESeq2_R-project/res_mg_wald_PK2-results.csv\"\n",
    "fp_k1k2 = \"../DESeq2_R-project/res_mg_wald_K1K2-results.csv\"\n",
    "fp_wk1 = \"../DESeq2_R-project/res_mg_wald_WK1-results.csv\"\n",
    "fp_wk2 = \"../DESeq2_R-project/res_mg_wald_WK2-results.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set header names\n",
    "names_base = [\"entrezgene\", \"baseMean\", \"log2FoldChange\", \n",
    "              \"lfcSE\", \"stat\", \"pvalue\", \"padj\"]\n",
    "conditions_base = [\"pw\", \"pk1\", \"pk2\", \"k1k2\", \"wk1\", \"wk2\"]\n",
    "names_pw = [ \"{}_pw\".format(x) for x in names_base ]\n",
    "names_pk1 = [ \"{}_pk1\".format(x) for x in names_base ]\n",
    "names_pk2 = [ \"{}_pk2\".format(x) for x in names_base ]\n",
    "names_k1k2 = [ \"{}_k1k2\".format(x) for x in names_base ]\n",
    "names_wk1 = [ \"{}_wk1\".format(x) for x in names_base ]\n",
    "names_wk2 = [ \"{}_wk2\".format(x) for x in names_base ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dataframes\n",
    "df_pw = pd.read_csv(fp_pw, header=0, names=names_pw).set_index(\"entrezgene_pw\")\n",
    "df_pk1 = pd.read_csv(fp_pk1, header=0, names=names_pk1).set_index(\"entrezgene_pk1\")\n",
    "df_pk2 = pd.read_csv(fp_pk2, header=0, names=names_pk2).set_index(\"entrezgene_pk2\")\n",
    "df_k1k2 = pd.read_csv(fp_k1k2, header=0, names=names_k1k2).set_index(\"entrezgene_k1k2\")\n",
    "df_wk1 = pd.read_csv(fp_wk1, header=0, names=names_wk1).set_index(\"entrezgene_wk1\")\n",
    "df_wk2 = pd.read_csv(fp_wk2, header=0, names=names_wk2).set_index(\"entrezgene_wk2\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge dataframes together:\n",
    "df_posthoc = pd.concat([df_pw, df_pk1, df_pk2, df_k1k2, df_wk1, df_wk2], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>baseMean_pw</th>\n",
       "      <th>log2FoldChange_pw</th>\n",
       "      <th>lfcSE_pw</th>\n",
       "      <th>stat_pw</th>\n",
       "      <th>pvalue_pw</th>\n",
       "      <th>padj_pw</th>\n",
       "      <th>baseMean_pk1</th>\n",
       "      <th>log2FoldChange_pk1</th>\n",
       "      <th>lfcSE_pk1</th>\n",
       "      <th>stat_pk1</th>\n",
       "      <th>...</th>\n",
       "      <th>lfcSE_wk1</th>\n",
       "      <th>stat_wk1</th>\n",
       "      <th>pvalue_wk1</th>\n",
       "      <th>padj_wk1</th>\n",
       "      <th>baseMean_wk2</th>\n",
       "      <th>log2FoldChange_wk2</th>\n",
       "      <th>lfcSE_wk2</th>\n",
       "      <th>stat_wk2</th>\n",
       "      <th>pvalue_wk2</th>\n",
       "      <th>padj_wk2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11287</th>\n",
       "      <td>1.934590</td>\n",
       "      <td>4.768127</td>\n",
       "      <td>4.697422</td>\n",
       "      <td>1.015052</td>\n",
       "      <td>0.310081</td>\n",
       "      <td>0.819093</td>\n",
       "      <td>1.934590</td>\n",
       "      <td>5.921332</td>\n",
       "      <td>4.697530</td>\n",
       "      <td>1.260520</td>\n",
       "      <td>...</td>\n",
       "      <td>4.247045</td>\n",
       "      <td>0.271531</td>\n",
       "      <td>7.859826e-01</td>\n",
       "      <td>9.992019e-01</td>\n",
       "      <td>1.934590</td>\n",
       "      <td>0.593551</td>\n",
       "      <td>4.247045</td>\n",
       "      <td>0.139756</td>\n",
       "      <td>0.888853</td>\n",
       "      <td>0.997184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11298</th>\n",
       "      <td>0.316304</td>\n",
       "      <td>-0.270229</td>\n",
       "      <td>4.903197</td>\n",
       "      <td>-0.055113</td>\n",
       "      <td>0.956049</td>\n",
       "      <td>0.998468</td>\n",
       "      <td>0.316304</td>\n",
       "      <td>-0.079014</td>\n",
       "      <td>4.903307</td>\n",
       "      <td>-0.016114</td>\n",
       "      <td>...</td>\n",
       "      <td>4.247045</td>\n",
       "      <td>0.045023</td>\n",
       "      <td>9.640890e-01</td>\n",
       "      <td>9.992019e-01</td>\n",
       "      <td>0.316304</td>\n",
       "      <td>-2.098142</td>\n",
       "      <td>4.211749</td>\n",
       "      <td>-0.498164</td>\n",
       "      <td>0.618368</td>\n",
       "      <td>0.997184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11302</th>\n",
       "      <td>12.814439</td>\n",
       "      <td>3.962726</td>\n",
       "      <td>4.484222</td>\n",
       "      <td>0.883704</td>\n",
       "      <td>0.376856</td>\n",
       "      <td>0.869637</td>\n",
       "      <td>12.814439</td>\n",
       "      <td>27.788118</td>\n",
       "      <td>4.570384</td>\n",
       "      <td>6.080040</td>\n",
       "      <td>...</td>\n",
       "      <td>4.026470</td>\n",
       "      <td>5.917191</td>\n",
       "      <td>3.274860e-09</td>\n",
       "      <td>4.912291e-07</td>\n",
       "      <td>12.814439</td>\n",
       "      <td>3.345539</td>\n",
       "      <td>3.946821</td>\n",
       "      <td>0.847654</td>\n",
       "      <td>0.396631</td>\n",
       "      <td>0.997184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11303</th>\n",
       "      <td>2981.665707</td>\n",
       "      <td>0.513416</td>\n",
       "      <td>0.329137</td>\n",
       "      <td>1.559885</td>\n",
       "      <td>0.118787</td>\n",
       "      <td>0.545990</td>\n",
       "      <td>2981.665707</td>\n",
       "      <td>0.535819</td>\n",
       "      <td>0.329112</td>\n",
       "      <td>1.628077</td>\n",
       "      <td>...</td>\n",
       "      <td>0.285218</td>\n",
       "      <td>0.078547</td>\n",
       "      <td>9.373928e-01</td>\n",
       "      <td>9.992019e-01</td>\n",
       "      <td>2981.665707</td>\n",
       "      <td>-0.299286</td>\n",
       "      <td>0.285279</td>\n",
       "      <td>-1.049098</td>\n",
       "      <td>0.294133</td>\n",
       "      <td>0.997184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11304</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       baseMean_pw  log2FoldChange_pw  lfcSE_pw   stat_pw  pvalue_pw  \\\n",
       "11287     1.934590           4.768127  4.697422  1.015052   0.310081   \n",
       "11298     0.316304          -0.270229  4.903197 -0.055113   0.956049   \n",
       "11302    12.814439           3.962726  4.484222  0.883704   0.376856   \n",
       "11303  2981.665707           0.513416  0.329137  1.559885   0.118787   \n",
       "11304     0.000000                NaN       NaN       NaN        NaN   \n",
       "\n",
       "        padj_pw  baseMean_pk1  log2FoldChange_pk1  lfcSE_pk1  stat_pk1  ...  \\\n",
       "11287  0.819093      1.934590            5.921332   4.697530  1.260520  ...   \n",
       "11298  0.998468      0.316304           -0.079014   4.903307 -0.016114  ...   \n",
       "11302  0.869637     12.814439           27.788118   4.570384  6.080040  ...   \n",
       "11303  0.545990   2981.665707            0.535819   0.329112  1.628077  ...   \n",
       "11304       NaN      0.000000                 NaN        NaN       NaN  ...   \n",
       "\n",
       "       lfcSE_wk1  stat_wk1    pvalue_wk1      padj_wk1  baseMean_wk2  \\\n",
       "11287   4.247045  0.271531  7.859826e-01  9.992019e-01      1.934590   \n",
       "11298   4.247045  0.045023  9.640890e-01  9.992019e-01      0.316304   \n",
       "11302   4.026470  5.917191  3.274860e-09  4.912291e-07     12.814439   \n",
       "11303   0.285218  0.078547  9.373928e-01  9.992019e-01   2981.665707   \n",
       "11304        NaN       NaN           NaN           NaN      0.000000   \n",
       "\n",
       "       log2FoldChange_wk2  lfcSE_wk2  stat_wk2  pvalue_wk2  padj_wk2  \n",
       "11287            0.593551   4.247045  0.139756    0.888853  0.997184  \n",
       "11298           -2.098142   4.211749 -0.498164    0.618368  0.997184  \n",
       "11302            3.345539   3.946821  0.847654    0.396631  0.997184  \n",
       "11303           -0.299286   0.285279 -1.049098    0.294133  0.997184  \n",
       "11304                 NaN        NaN       NaN         NaN       NaN  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_posthoc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identify focal geneset\n",
    "### create filters to identify each of the following subsets of genes:\n",
    "* the likelihood ratio test was significant (*p* ≤ 0.05)\n",
    "* WT and PBS were significantly differentially expressed (*p* ≤ 0.05)\n",
    "* WT was significantly different from both KO1 and KO2 (*p* ≤ 0.05 in each contrast)\n",
    "* KO1 and KO2 were concordantly up- or down- regulated with regard to WT\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# significant in LRT test\n",
    "mask_degs = df_posthoc.index.isin(degs_mg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove non sig for PBS vs WT P>0.05\n",
    "mask_pw_sig = (df_posthoc.pvalue_pw <= 0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change PBS/WT to WT/PBS, for clearer interpretation of direction of change\n",
    "df_posthoc.loc[:, \"log2FoldChange_wp\"] = df_posthoc.loc[:, \"log2FoldChange_pw\"] * -1\n",
    "df_posthoc.loc[:, \"stat_wp\"] = df_posthoc.loc[:, \"stat_pw\"] * -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove non significant in wt vs k1 and/or k2\n",
    "mask_wk_sig = (df_posthoc.pvalue_wk1 <= 0.05) & (df_posthoc.pvalue_wk2 <= 0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tag significant in k1 vs k2\n",
    "mask_k1k2_sig = (df_posthoc.pvalue_k1k2 <= 0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove the ones changing significantly between WT/K1 and WT/K2 but in different senses\n",
    "mask_concordant = ((df_posthoc.log2FoldChange_wk1 / df_posthoc.log2FoldChange_wk2) > 0) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### combine filters to identify focal geneset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = df_posthoc[mask_degs &\n",
    "                      mask_pw_sig & \n",
    "                      mask_wk_sig &\n",
    "                      mask_concordant\n",
    "                     ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 38)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Print list of 25 focal genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get gene names:\n",
    "final_list = pd.merge(left=df_final.reset_index(), \n",
    "                     right=df_names.drop_duplicates(subset=['entrezgene', 'external_gene_name']),\n",
    "                     left_on='index', right_on='entrezgene',\n",
    "                     how='inner'\n",
    "                    )['external_gene_name'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cxcl1,Cxcl10,Acod1,Mdm4,Ptgs2,Ccl5,Syk,Tnf,Tnfrsf1b,Xk,Map3k2,Sart3,Ccrl2,Hbs1l,Aldh18a1,Trp53inp2,Bst2,Rab43,Bclaf1,Zfp248,Echs1,Fam207a,Fbxo11,Zfp119b,Parp14\n"
     ]
    }
   ],
   "source": [
    "print(\",\".join(final_list))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## view correlation in expression differences between conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_final.plot.scatter(x='log2FoldChange_wp',\n",
    "                      y='log2FoldChange_wk1'\n",
    "                     )\n",
    "plt.title(\"Correlation between wildtype:knockout 1 vs wildtype:PBS\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_final.plot.scatter(x='log2FoldChange_pk1',\n",
    "                      y='log2FoldChange_wk1'\n",
    "                     )\n",
    "plt.title(\"Correlation between wildtype:knockout 1 vs PBS:knockout 1\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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