{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Jupyter Notebook and data supplemental to the manuscript: \"_Up in arms: Immune and nervous system response to sea star wasting disease_\"\n",
    "\n",
    "<!---\n",
    "INSERT LINKS HERE\n",
    "--->\n",
    "\n",
    "---\n",
    "\n",
    "The code in the IPython notebook will process data such that figures in the manuscript are reproduced (in theory). The intent is to download the github repository locally and run so accompaning scripts and data are available. Detailed Instructions are provided in GitHub Repo Readme. \n",
    "\n",
    "---\n",
    "To execute the Jupyter (IPython) Notebook in its entirety you will need:   \n",
    "\n",
    "* IPython - [install instructions](http://ipython.org/install.html)    \n",
    "* NBCI Blast -  [install instructions](http://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=Web&PAGE_TYPE=BlastDocs&DOC_TYPE=Download)  \n",
    "* R - [install instructions](http://www.r-project.org/)  \n",
    "* rpy2 (interface to R from Python) - [install instructions](http://rpy.sourceforge.net/)  \n",
    "* SQLShare-pythonclient [install instructions](https://github.com/uwescience/sqlshare-pythonclient)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Instructions for use.**   \n",
    "\n",
    "Identify the location (local) of the blast database you would like to use and indicate path in the cell below. **{db}**\n",
    "\n",
    "Identify the location of your `sqlshare-pythonclient/tools`  and indicate path in the cell below. **{sqls}**\n",
    "\n",
    "Change the input to the **{usr}** variable to reflect your SQLShare user account.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Variables user needs to modify accordingly\n",
    "db=\"~/blastdb/uniprot_sprot\"\n",
    "\n",
    "sqls=\"~/sqlshare-pythonclient/tools/\"\n",
    "\n",
    "usr=\"sr320@washington.edu\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examining _Pycnopodia helianthoides_ coelomocyte _de novo_ transcriptome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">Phel_contig_1\r\n",
      "CAAATATATGAACGGTTGATTGTCAACGATTAGTACATGTTTTCATTGTTCCCCACGCCCGCCCCCCCCCACTCAAACATTTAAAGTGTGAAATATTATTTATCCACAAATTTCCTTAAACCTGCAAACTTGTCTGCTGTCTCTTATTGGAAGTTATGAAAAAGAACAACGGGTTTTCTTTAAAGGGTCTGCGTGCGATTTTCAACCTTTTGAGTAATAGCAGTTATTTTGATAACCGATTTTTTTCAAAGCTCAACAGCTTTTTAAAATAAGGAATCCTATAATGGCCAAACGAATACTATAAAAATAAGGGTTCTCTTAATTGTATAAAACGTATAATTTTATCAATTTTGGGACCGTGTAATTTTTTAAAGACCACAAGAATGTTACATACAACAAATAGACGAAACTCGTAGCTTTGGAAACTACGTCATGGGCGTTTGGTCAAAAGCTGGAGAGAAAGAGAGGTGGGGTGCCAGACTTAAGTAGTCACGTGATCTGACCAACGCACATCGGAAGCTCGATCGGATGAAATCTTCTCTATCGTTCTTGCGTCTATACGTGCTACGAAGAGCTGACAGAAGTTTGGACTTGTTTACTTCTTGCACCTGTTGATGGAACGGCCACGGACCTTGTCGCACGCACACCTGGAGCCAGTGCTCGGATCGACGCAACGGATGTACTGTCTTCCCCTTCCGCGTTTCTCAAGTAGGTACTCAAAGTCGTCCGCGTCGAAGTTGGCCTCGGCGTCCCTCTTCTCCAGCTCCTCCATGTCCTCCTCTGTGTAGTACGGGGTGACGAGCACCACCAGGGCGGCCACAATGGCCAGTGCTAGAAGACACTTCGTATTCATTCTGCTGGTGGTTGGATGTGCGCAAACAAGACAGGAGAGACTTATTAGAATC\r\n"
     ]
    }
   ],
   "source": [
    "#Look at first 2 lines\n",
    "!head -2 ./data/Phel_transcriptome.fasta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "0:999 \t15505\r\n",
      "1000:1999 \t8559\r\n",
      "2000:2999 \t3003\r\n",
      "3000:3999 \t1240\r\n",
      "4000:4999 \t580\r\n",
      "5000:5999 \t312\r\n",
      "6000:6999 \t129\r\n",
      "7000:7999 \t72\r\n",
      "8000:8999 \t31\r\n",
      "9000:9999 \t19\r\n",
      "10000:10999 \t9\r\n",
      "11000:11999 \t7\r\n",
      "12000:12999 \t2\r\n",
      "13000:13999 \t4\r\n",
      "14000:14999 \t3\r\n",
      "15000:15999 \t0\r\n",
      "16000:16999 \t1\r\n",
      "\r\n",
      "Total length of sequence:\t40747496 bp\r\n",
      "Total number of sequences:\t29476\r\n",
      "N25 stats:\t\t\t25% of total sequence length is contained in the 2260 sequences >= 3085 bp\r\n",
      "N50 stats:\t\t\t50% of total sequence length is contained in the 6715 sequences >= 1757 bp\r\n",
      "N75 stats:\t\t\t75% of total sequence length is contained in the 14612 sequences >= 959 bp\r\n",
      "Total GC count:\t\t\t16459121 bp\r\n",
      "GC %:\t\t\t\t40.39 %\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "#Run perl script cont_fasta.pl - Perl script\n",
    "#Author: Joseph Fass (modified from script by Brad Sickler) \n",
    "#last revised: November 2010 - http://bioinformatics.ucdavis.edu.\n",
    "#-i can be modified to change bin size\n",
    "!perl ./scripts/count_fasta.pl -i 1000 ./data/Phel_transcriptome.fasta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Annotation of transcriptome"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to annoate the P. hel transcriptome `blastx` was used to compare sequences to the UniProtKB/Swiss_Prot database.    \n",
    "The specific code is \n",
    "\n",
    "```\n",
    "blastx \\\n",
    "-query Phel_transcriptome.fasta \\\n",
    "-db /your/databaselocation/uniprot_sprot \\\n",
    "-out Phel_uniprot_sprot.tab \\\n",
    "-evalue 1E-05 \\\n",
    "-max_target_seqs 1 \\\n",
    "-max_hsps 1 \\\n",
    "-outfmt 6 \\\n",
    "-num_threads 16\n",
    "```\n",
    "\n",
    "This analysis could be run within notebook assuming user has `blast` installed and access to the protein database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Run Blastx against local copy of UniProt/SwissProt Database\n",
    "!blastx \\\n",
    "-query ./data/Phel_transcriptome.fasta \\\n",
    "-db {db} \\\n",
    "-out ./wd/Phel_uniprot_sprot.tab \\\n",
    "-evalue 1E-05 \\\n",
    "-max_target_seqs 1 \\\n",
    "-max_hsps 1 \\\n",
    "-outfmt 6 \\\n",
    "-num_threads 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Phel_contig_100\tsp|Q16513|PKN2_HUMAN\t81.33\t332\t61\t1\t7935\t6940\t653\t983\t5e-162\t  537\r\n",
      "Phel_contig_1000\tsp|Q8R4U2|PDIA1_CRIGR\t53.62\t442\t201\t2\t199\t1512\t31\t472\t5e-146\t  464\r\n",
      "Phel_contig_10006\tsp|Q9Y2H9|MAST1_HUMAN\t70.93\t289\t82\t1\t861\t1\t434\t722\t6e-132\t  415\r\n",
      "Phel_contig_10021\tsp|Q96MU7|YTDC1_HUMAN\t60.85\t212\t82\t1\t1115\t1750\t294\t504\t1e-73\t  258\r\n"
     ]
    }
   ],
   "source": [
    "# blastx output\n",
    "!head -4 ./wd/Phel_uniprot_sprot.tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   10513 ./wd/Phel_uniprot_sprot.tab\r\n"
     ]
    }
   ],
   "source": [
    "#determining how many sequences have blast hits\n",
    "!wc -l ./wd/Phel_uniprot_sprot.tab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SQLShare will be used to join with additional information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Phel_contig_100\tsp|Q16513|PKN2_HUMAN\t81.33\t332\t61\t1\t7935\t6940\t653\t983\t5e-162\t  537\n",
      "SQLShare ready version has Pipes converted to Tabs ....\n",
      "Phel_contig_100\tsp\tQ16513\tPKN2_HUMAN\t81.33\t332\t61\t1\t7935\t6940\t653\t983\t5e-162\t  537\n"
     ]
    }
   ],
   "source": [
    "#convert pipes for upload to SQLShare where blast results will be joined with Gene Ontology information\n",
    "!tr '|' \"\\t\" <./wd/Phel_uniprot_sprot.tab> ./wd/Phel_uniprot_sprot_sql.tab\n",
    "!head -1 ./wd/Phel_uniprot_sprot.tab\n",
    "!echo SQLShare ready version has Pipes converted to Tabs ....\n",
    "!head -1 ./wd/Phel_uniprot_sprot_sql.tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing chunk line 0 to 10513 (0.0074200630188 s elapsed)\n",
      "pushing ./wd/Phel_uniprot_sprot_sql.tab...\n",
      "parsing DC465013...\n",
      "finished Phel_uniprot_sprot_sql.tab\n",
      "upload timestamp...\n",
      "Mon Dec 22 15:29:54 PST 2014\n"
     ]
    }
   ],
   "source": [
    "!python {sqls}singleupload.py \\\n",
    "./wd/Phel_uniprot_sprot_sql.tab\n",
    "!echo upload timestamp...\n",
    "!date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Joining with UniProt information in SQLShare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "!python {sqls}fetchdata.py \\\n",
    "-s \"SELECT * FROM [{usr}].[Phel_uniprot_sprot_sql.tab]phel left join [sr320@washington.edu].[uniprot-reviewed_wGO_010714]des on phel.Column3=des.Entry\" \\\n",
    "-f tsv \\\n",
    "-o ./wd/Phel_uniprot_info.tab\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column1\tColumn2\tColumn3\tColumn4\tColumn5\tColumn6\tColumn7\tColumn8\tColumn9\tColumn10\tColumn11\tColumn12\tColumn13\tColumn14\tEntry\tEntry name\tGene ontology IDs\tInteracts with\tCross-reference (GO)\tGene ontology (GO)\tStatus\tInterPro\tPathway\tProtein names\tGene names\tOrganism\tLength\r",
      "\r\n",
      "Phel_contig_100\tsp\tQ16513\tPKN2_HUMAN\t81.33\t332\t61\t1\t7935\t6940\t653\t983\t5E-162\t537\tQ16513\tPKN2_HUMAN\tGO:0005524; GO:0043297; GO:0043296; GO:0006915; GO:0007155; GO:0007049; GO:0051301; GO:0032154; GO:0005737; GO:0005856; GO:0010631; GO:0042826; GO:0030027; GO:0016020; GO:0030496; GO:0005634; GO:0032467; GO:0045931; GO:0004697; GO:0004674; GO:2000145; GO:0006355; GO:0007165; GO:0006351\tQ15118\t\tATP binding; apical junction assembly; apical junction complex; apoptotic process; cell adhesion; cell cycle; cell division; cleavage furrow; cytoplasm; cytoskeleton; epithelial cell migration; histone deacetylase binding; lamellipodium; membrane; midbody; nucleus; positive regulation of cytokinesis; positive regulation of mitotic cell cycle; protein kinase C activity; protein serine/threonine kinase activity; regulation of cell motility; regulation of transcription, DNA-dependent; signal transduction; transcription, DNA-dependent\treviewed\tIPR000961; IPR000008; IPR011072; IPR011009; IPR017892; IPR000719; IPR017441; IPR002290; IPR008271;\t\tSerine/threonine-protein kinase N2 (EC 2.7.11.13) (PKN gamma) (Protein kinase C-like 2) (Protein-kinase C-related kinase 2)\tPKN2 PRK2 PRKCL2\tHomo sapiens (Human)\t984\r",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!head -2 ./wd/Phel_uniprot_info.tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#cleaning up table to only show certain columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "!awk -F\\t '{print $1, $3, $24, $13, $26}' ./wd/Phel_uniprot_info.tab | tail -n +2 > ./wd/Phel_uniprot_ID.tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Phel_contig_100 Q16513 Serine/threonine-protein kinase N2 (EC 2.7.11.13) (PKN gamma) (Protein kinase C-like 2) (Protein-kinase C-related kinase 2) 5E-162 Homo sapiens (Human)\r\n",
      "Phel_contig_1000 Q8R4U2 Protein disulfide-isomerase (PDI) (EC 5.3.4.1) (Prolyl 4-hydroxylase subunit beta) (p58) 5E-146 Cricetulus griseus (Chinese hamster) (Cricetulus barabensis griseus)\r\n",
      "Phel_contig_10006 Q9Y2H9 Microtubule-associated serine/threonine-protein kinase 1 (EC 2.7.11.1) (Syntrophin-associated serine/threonine-protein kinase) 6E-132 Homo sapiens (Human)\r\n"
     ]
    }
   ],
   "source": [
    "!head -3 ./wd/Phel_uniprot_ID.tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Differential Expression Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Count data from CLC..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature ID\tTreated_FHL - Total gene reads\tTreated_PH - Total gene reads\tTreated_L - Total gene reads\tControl_FHL - Total gene reads\tControl_DB - Total gene reads\tControl_PH - Total gene reads\r\n",
      "Phel_contig_1\t168\t37\t8\t89\t28\t38\r\n",
      "Phel_contig_10\t9518\t2752\t839\t22\t42\t180\r\n",
      "Phel_contig_100\t260\t565\t413\t616\t1234\t6104\r\n",
      "Phel_contig_1000\t2043\t3842\t3070\t4311\t8527\t31946\r\n",
      "Phel_contig_10000\t9\t12\t13\t32\t21\t211\r\n",
      "Phel_contig_10001\t44\t225\t89\t90\t54\t365\r\n",
      "Phel_contig_10002\t38\t61\t80\t185\t478\t1267\r\n",
      "Phel_contig_10003\t9\t29\t20\t17\t29\t186\r\n",
      "Phel_contig_10004\t8\t25\t6\t4\t19\t92\r\n"
     ]
    }
   ],
   "source": [
    "!head ./data/Phel_countdata.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   29476 ./data/Phel_countdata.txt\r\n"
     ]
    }
   ],
   "source": [
    "!wc -l ./data/Phel_countdata.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%load_ext rpy2.ipython\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NULL\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R \n",
    "#source(\"http://bioconductor.org/biocLite.R\")\n",
    "#biocLite(\"DESeq2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loading required package: GenomicRanges\n",
       "Loading required package: BiocGenerics\n",
       "Loading required package: parallel\n",
       "\n",
       "Attaching package: ‘BiocGenerics’\n",
       "\n",
       "The following objects are masked from ‘package:parallel’:\n",
       "\n",
       "    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,\n",
       "    clusterExport, clusterMap, parApply, parCapply, parLapply,\n",
       "    parLapplyLB, parRapply, parSapply, parSapplyLB\n",
       "\n",
       "The following object is masked from ‘package:stats’:\n",
       "\n",
       "    xtabs\n",
       "\n",
       "The following objects are masked from ‘package:base’:\n",
       "\n",
       "    anyDuplicated, append, as.data.frame, as.vector, cbind, colnames,\n",
       "    do.call, duplicated, eval, evalq, Filter, Find, get, intersect,\n",
       "    is.unsorted, lapply, Map, mapply, match, mget, order, paste, pmax,\n",
       "    pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce, rep.int,\n",
       "    rownames, sapply, setdiff, sort, table, tapply, union, unique,\n",
       "    unlist\n",
       "\n",
       "Loading required package: IRanges\n",
       "Loading required package: GenomeInfoDb\n",
       "Loading required package: Rcpp\n",
       "Loading required package: RcppArmadillo\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "library(DESeq2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%R\n",
    "data <- read.table(\"./data/Phel_countdata.txt\", header = T, sep = \"\\t\")\n",
    "rownames(data) <- data$Feature\n",
    "data <- data[,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%R\n",
    "# Build Objects\n",
    "# Specify which columns are in which groups\n",
    "deseq2.colData <- data.frame(condition=factor(c(rep(\"Treated\", 3), rep(\"Control\", 3))), \n",
    "                             type=factor(rep(\"single-read\", 6)))\n",
    "rownames(deseq2.colData) <- colnames(data)\n",
    "deseq2.dds <- DESeqDataSetFromMatrix(countData = data,\n",
    "                                     colData = deseq2.colData, \n",
    "                                     design = ~ condition)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "estimating size factors\n",
       "estimating dispersions\n",
       "gene-wise dispersion estimates\n",
       "mean-dispersion relationship\n",
       "final dispersion estimates\n",
       "fitting model and testing\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "# Run Analysis\n",
    "deseq2.dds <- DESeq(deseq2.dds)\n",
    "deseq2.res <- results(deseq2.dds)\n",
    "deseq2.res <- deseq2.res[order(rownames(deseq2.res)), ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "log2 fold change (MAP): condition Treated vs Control \n",
       "Wald test p-value: condition Treated vs Control \n",
       "DataFrame with 6 rows and 6 columns\n",
       "                    baseMean log2FoldChange     lfcSE        stat       pvalue\n",
       "                   <numeric>      <numeric> <numeric>   <numeric>    <numeric>\n",
       "Phel_contig_1      114.07829     1.62009683 0.9832754  1.64765323 9.942385e-02\n",
       "Phel_contig_10    5492.70960     7.62981964 0.7912097  9.64323332 5.250751e-22\n",
       "Phel_contig_100    786.08744     0.01618812 0.3188107  0.05077657 9.595036e-01\n",
       "Phel_contig_1000  5406.89996     0.21136426 0.3459447  0.61097705 5.412148e-01\n",
       "Phel_contig_10000   24.76992    -0.20013559 0.6269094 -0.31924166 7.495433e-01\n",
       "Phel_contig_10001  136.22441     1.60180411 0.5966195  2.68480001 7.257322e-03\n",
       "                          padj\n",
       "                     <numeric>\n",
       "Phel_contig_1     2.778325e-01\n",
       "Phel_contig_10    2.773165e-19\n",
       "Phel_contig_100   9.834211e-01\n",
       "Phel_contig_1000  7.554609e-01\n",
       "Phel_contig_10000 8.842128e-01\n",
       "Phel_contig_10001 4.354393e-02\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "head(deseq2.res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 3773    6\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "# Count number of hits with adjusted p-value less then 0.05\n",
    "dim(deseq2.res[!is.na(deseq2.res$padj) & deseq2.res$padj <= 0.05, ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "tmp <- deseq2.res\n",
    "# The main plot\n",
    "plot(tmp$baseMean, tmp$log2FoldChange, pch=20, cex=0.45, ylim=c(-3, 3), log=\"x\", col=\"darkgray\",\n",
    "     main=\"DEG Virus Exposure  (pval <= 0.05)\",\n",
    "     xlab=\"mean of normalized counts\",\n",
    "     ylab=\"Log2 Fold Change\")\n",
    "# Getting the significant points and plotting them again so they're a different color\n",
    "tmp.sig <- deseq2.res[!is.na(deseq2.res$padj) & deseq2.res$padj <= 0.05, ]\n",
    "points(tmp.sig$baseMean, tmp.sig$log2FoldChange, pch=20, cex=0.45, col=\"red\")\n",
    "# 2 FC lines\n",
    "abline(h=c(-1,1), col=\"blue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%R\n",
    "write.table(tmp.sig, \"./wd/Phel_DEGlist.tab\", row.names = T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"baseMean\" \"log2FoldChange\" \"lfcSE\" \"stat\" \"pvalue\" \"padj\"\r\n",
      "\"Phel_contig_10\" 5492.70960072414 7.62981963801218 0.791209689281713 9.64323331901912 5.250750567205e-22 2.77316470200627e-19\r\n",
      "\"Phel_contig_10001\" 136.224412089297 1.6018041057117 0.596619525301856 2.68480000700827 0.00725732180374353 0.0435439308224612\r\n",
      "\"Phel_contig_10005\" 51.5511935035151 -2.6900828854296 0.967063782856695 -2.78170161380993 0.00540747316717733 0.0349741409683566\r\n",
      "\"Phel_contig_10010\" 8.7342056518699 2.79633731397416 0.8776643866179 3.18611231879866 0.00144198603335619 0.0126467256242587\r\n",
      "\"Phel_contig_10013\" 189.785994443343 -2.05500342602883 0.505265919047074 -4.06717205447885 4.75871086619138e-05 0.000815875891500461\r\n",
      "\"Phel_contig_10045\" 50.2388038133868 3.34055697406 0.788340247567732 4.23745582490127 2.26066880151196e-05 0.000436296989553832\r\n",
      "\"Phel_contig_1005\" 3425.16142786886 -1.72144787339153 0.420615456798411 -4.09268809685368 4.26401104553472e-05 0.000745221107183283\r\n",
      "\"Phel_contig_10050\" 281.308679616738 -1.59285231061128 0.389992050597291 -4.08431994491107 4.42060880358314e-05 0.000766081995510385\r\n",
      "\"Phel_contig_10058\" 24.604598291592 2.76328758366774 1.00797592479059 2.74142220633081 0.00611738448267447 0.0381226705570531\r\n"
     ]
    }
   ],
   "source": [
    "!head ./wd/Phel_DEGlist.tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
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