{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notebook 4: \n",
    "\n",
    "This is an IPython notebook. Most of the code is composed of bash scripts, indicated by %%bash at the top of the cell, otherwise it is IPython code. This notebook includes code to download, assemble and analyze a published RADseq data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "### Notebook 4\n",
    "### Data set 4 (Orestias)\n",
    "### Authors: Takahashi & Moreno (2015)\n",
    "### Data Location: DDBJ DRA DRA003595"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download the sequence data\n",
    "Sequence data for this study are archived on the NCBI sequence read archive (SRA). Below I read in SraRunTable.txt for this project which contains all of the information we need to download the data. \n",
    "\n",
    "+ Project DRA: DRA003595\n",
    "+ Study: DRP002750\n",
    "+ Experiments: DRX033006 -- DRX033069\n",
    "+ Samples: DRS020928 -- DRS020991\n",
    "+ SRA link: http://trace.ddbj.nig.ac.jp/DRASearch/submission?acc=DRA003595\n",
    "+ Publication address: __doi:10.1016/j.ympev.2015.08.012__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "## make a new directory for this analysis\n",
    "mkdir -p empirical_4/fastq/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "def wget_download_ddbj(SRR, outdir):\n",
    "    \"\"\" Python function to get sra data from ncbi and write to\n",
    "    outdir with a new name using bash call wget \"\"\"\n",
    "    \n",
    "    ## create a call string \n",
    "    call = \"wget -q -r -nH --cut-dirs=9 -P \"+outdir+\" \"+\\\n",
    "           \"ftp://ftp.ddbj.nig.ac.jp/ddbj_database/dra/sra/ByExp/\"+\\\n",
    "           \"sra/DRX/DRX033/DRX033{:03d}\".format(SRR)\n",
    "    \n",
    "    ## run wget call \n",
    "    ! $call"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we pass the SRR number and the sample name to the `wget_download` function so that the files are saved. In this case we do not have the sample names, just their SRR IDs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for ID in range(6,70):\n",
    "    wget_download_ddbj(ID, \"empirical_4/fastq/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read 3850280 spots for empirical_4/fastq/DRR036765.sra\n",
      "Written 3850280 spots for empirical_4/fastq/DRR036765.sra\n",
      "Read 5135835 spots for empirical_4/fastq/DRR036766.sra\n",
      "Written 5135835 spots for empirical_4/fastq/DRR036766.sra\n",
      "Read 3812927 spots for empirical_4/fastq/DRR036767.sra\n",
      "Written 3812927 spots for empirical_4/fastq/DRR036767.sra\n",
      "Read 4279789 spots for empirical_4/fastq/DRR036768.sra\n",
      "Written 4279789 spots for empirical_4/fastq/DRR036768.sra\n",
      "Read 4238415 spots for empirical_4/fastq/DRR036769.sra\n",
      "Written 4238415 spots for empirical_4/fastq/DRR036769.sra\n",
      "Read 4897736 spots for empirical_4/fastq/DRR036770.sra\n",
      "Written 4897736 spots for empirical_4/fastq/DRR036770.sra\n",
      "Read 4110217 spots for empirical_4/fastq/DRR036771.sra\n",
      "Written 4110217 spots for empirical_4/fastq/DRR036771.sra\n",
      "Read 4381798 spots for empirical_4/fastq/DRR036772.sra\n",
      "Written 4381798 spots for empirical_4/fastq/DRR036772.sra\n",
      "Read 3692455 spots for empirical_4/fastq/DRR036773.sra\n",
      "Written 3692455 spots for empirical_4/fastq/DRR036773.sra\n",
      "Read 4146148 spots for empirical_4/fastq/DRR036774.sra\n",
      "Written 4146148 spots for empirical_4/fastq/DRR036774.sra\n",
      "Read 5296764 spots for empirical_4/fastq/DRR036775.sra\n",
      "Written 5296764 spots for empirical_4/fastq/DRR036775.sra\n",
      "Read 3916368 spots for empirical_4/fastq/DRR036776.sra\n",
      "Written 3916368 spots for empirical_4/fastq/DRR036776.sra\n",
      "Read 5076822 spots for empirical_4/fastq/DRR036777.sra\n",
      "Written 5076822 spots for empirical_4/fastq/DRR036777.sra\n",
      "Read 3986174 spots for empirical_4/fastq/DRR036778.sra\n",
      "Written 3986174 spots for empirical_4/fastq/DRR036778.sra\n",
      "Read 3652015 spots for empirical_4/fastq/DRR036779.sra\n",
      "Written 3652015 spots for empirical_4/fastq/DRR036779.sra\n",
      "Read 4887011 spots for empirical_4/fastq/DRR036780.sra\n",
      "Written 4887011 spots for empirical_4/fastq/DRR036780.sra\n",
      "Read 4027193 spots for empirical_4/fastq/DRR036781.sra\n",
      "Written 4027193 spots for empirical_4/fastq/DRR036781.sra\n",
      "Read 5385172 spots for empirical_4/fastq/DRR036782.sra\n",
      "Written 5385172 spots for empirical_4/fastq/DRR036782.sra\n",
      "Read 5128952 spots for empirical_4/fastq/DRR036783.sra\n",
      "Written 5128952 spots for empirical_4/fastq/DRR036783.sra\n",
      "Read 4593914 spots for empirical_4/fastq/DRR036784.sra\n",
      "Written 4593914 spots for empirical_4/fastq/DRR036784.sra\n",
      "Read 2334669 spots for empirical_4/fastq/DRR036785.sra\n",
      "Written 2334669 spots for empirical_4/fastq/DRR036785.sra\n",
      "Read 4626890 spots for empirical_4/fastq/DRR036786.sra\n",
      "Written 4626890 spots for empirical_4/fastq/DRR036786.sra\n",
      "Read 3888993 spots for empirical_4/fastq/DRR036787.sra\n",
      "Written 3888993 spots for empirical_4/fastq/DRR036787.sra\n",
      "Read 4512205 spots for empirical_4/fastq/DRR036788.sra\n",
      "Written 4512205 spots for empirical_4/fastq/DRR036788.sra\n",
      "Read 4174220 spots for empirical_4/fastq/DRR036789.sra\n",
      "Written 4174220 spots for empirical_4/fastq/DRR036789.sra\n",
      "Read 767653 spots for empirical_4/fastq/DRR036790.sra\n",
      "Written 767653 spots for empirical_4/fastq/DRR036790.sra\n",
      "Read 4019423 spots for empirical_4/fastq/DRR036791.sra\n",
      "Written 4019423 spots for empirical_4/fastq/DRR036791.sra\n",
      "Read 3286802 spots for empirical_4/fastq/DRR036792.sra\n",
      "Written 3286802 spots for empirical_4/fastq/DRR036792.sra\n",
      "Read 4601860 spots for empirical_4/fastq/DRR036793.sra\n",
      "Written 4601860 spots for empirical_4/fastq/DRR036793.sra\n",
      "Read 5436565 spots for empirical_4/fastq/DRR036794.sra\n",
      "Written 5436565 spots for empirical_4/fastq/DRR036794.sra\n",
      "Read 4528702 spots for empirical_4/fastq/DRR036795.sra\n",
      "Written 4528702 spots for empirical_4/fastq/DRR036795.sra\n",
      "Read 4328453 spots for empirical_4/fastq/DRR036796.sra\n",
      "Written 4328453 spots for empirical_4/fastq/DRR036796.sra\n",
      "Read 4845789 spots for empirical_4/fastq/DRR036797.sra\n",
      "Written 4845789 spots for empirical_4/fastq/DRR036797.sra\n",
      "Read 4231660 spots for empirical_4/fastq/DRR036798.sra\n",
      "Written 4231660 spots for empirical_4/fastq/DRR036798.sra\n",
      "Read 4610513 spots for empirical_4/fastq/DRR036799.sra\n",
      "Written 4610513 spots for empirical_4/fastq/DRR036799.sra\n",
      "Read 5079930 spots for empirical_4/fastq/DRR036800.sra\n",
      "Written 5079930 spots for empirical_4/fastq/DRR036800.sra\n",
      "Read 4435752 spots for empirical_4/fastq/DRR036801.sra\n",
      "Written 4435752 spots for empirical_4/fastq/DRR036801.sra\n",
      "Read 3428835 spots for empirical_4/fastq/DRR036802.sra\n",
      "Written 3428835 spots for empirical_4/fastq/DRR036802.sra\n",
      "Read 1880356 spots for empirical_4/fastq/DRR036803.sra\n",
      "Written 1880356 spots for empirical_4/fastq/DRR036803.sra\n",
      "Read 5300107 spots for empirical_4/fastq/DRR036804.sra\n",
      "Written 5300107 spots for empirical_4/fastq/DRR036804.sra\n",
      "Read 2912768 spots for empirical_4/fastq/DRR036805.sra\n",
      "Written 2912768 spots for empirical_4/fastq/DRR036805.sra\n",
      "Read 4221013 spots for empirical_4/fastq/DRR036806.sra\n",
      "Written 4221013 spots for empirical_4/fastq/DRR036806.sra\n",
      "Read 4787167 spots for empirical_4/fastq/DRR036807.sra\n",
      "Written 4787167 spots for empirical_4/fastq/DRR036807.sra\n",
      "Read 4955937 spots for empirical_4/fastq/DRR036808.sra\n",
      "Written 4955937 spots for empirical_4/fastq/DRR036808.sra\n",
      "Read 4351758 spots for empirical_4/fastq/DRR036809.sra\n",
      "Written 4351758 spots for empirical_4/fastq/DRR036809.sra\n",
      "Read 2199538 spots for empirical_4/fastq/DRR036810.sra\n",
      "Written 2199538 spots for empirical_4/fastq/DRR036810.sra\n",
      "Read 4750069 spots for empirical_4/fastq/DRR036811.sra\n",
      "Written 4750069 spots for empirical_4/fastq/DRR036811.sra\n",
      "Read 4544226 spots for empirical_4/fastq/DRR036812.sra\n",
      "Written 4544226 spots for empirical_4/fastq/DRR036812.sra\n",
      "Read 4825518 spots for empirical_4/fastq/DRR036813.sra\n",
      "Written 4825518 spots for empirical_4/fastq/DRR036813.sra\n",
      "Read 5517492 spots for empirical_4/fastq/DRR036814.sra\n",
      "Written 5517492 spots for empirical_4/fastq/DRR036814.sra\n",
      "Read 3299739 spots for empirical_4/fastq/DRR036815.sra\n",
      "Written 3299739 spots for empirical_4/fastq/DRR036815.sra\n",
      "Read 4464593 spots for empirical_4/fastq/DRR036816.sra\n",
      "Written 4464593 spots for empirical_4/fastq/DRR036816.sra\n",
      "Read 4873706 spots for empirical_4/fastq/DRR036817.sra\n",
      "Written 4873706 spots for empirical_4/fastq/DRR036817.sra\n",
      "Read 3321824 spots for empirical_4/fastq/DRR036818.sra\n",
      "Written 3321824 spots for empirical_4/fastq/DRR036818.sra\n",
      "Read 3625781 spots for empirical_4/fastq/DRR036819.sra\n",
      "Written 3625781 spots for empirical_4/fastq/DRR036819.sra\n",
      "Read 3779872 spots for empirical_4/fastq/DRR036820.sra\n",
      "Written 3779872 spots for empirical_4/fastq/DRR036820.sra\n",
      "Read 5404611 spots for empirical_4/fastq/DRR036821.sra\n",
      "Written 5404611 spots for empirical_4/fastq/DRR036821.sra\n",
      "Read 5276329 spots for empirical_4/fastq/DRR036822.sra\n",
      "Written 5276329 spots for empirical_4/fastq/DRR036822.sra\n",
      "Read 3720213 spots for empirical_4/fastq/DRR036823.sra\n",
      "Written 3720213 spots for empirical_4/fastq/DRR036823.sra\n",
      "Read 4306471 spots for empirical_4/fastq/DRR036824.sra\n",
      "Written 4306471 spots for empirical_4/fastq/DRR036824.sra\n",
      "Read 4862198 spots for empirical_4/fastq/DRR036825.sra\n",
      "Written 4862198 spots for empirical_4/fastq/DRR036825.sra\n",
      "Read 4332795 spots for empirical_4/fastq/DRR036826.sra\n",
      "Written 4332795 spots for empirical_4/fastq/DRR036826.sra\n",
      "Read 4589896 spots for empirical_4/fastq/DRR036827.sra\n",
      "Written 4589896 spots for empirical_4/fastq/DRR036827.sra\n",
      "Read 4980042 spots for empirical_4/fastq/DRR036828.sra\n",
      "Written 4980042 spots for empirical_4/fastq/DRR036828.sra\n",
      "Read 272718918 spots total\n",
      "Written 272718918 spots total\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "## convert sra files to fastq using fastq-dump tool\n",
    "## output as gzipped into the fastq directory\n",
    "fastq-dump --gzip -O empirical_4/fastq/  empirical_4/fastq/*.sra\n",
    "\n",
    "## remove .sra files\n",
    "rm empirical_4/fastq/*.sra"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make a params file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pyRAD 3.0.63\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "pyrad --version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\tnew params.txt file created\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "## delete existing params file if it exits\n",
    "rm params.txt\n",
    "\n",
    "## create a new default params file\n",
    "pyrad -n "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "## substitute new parameters into file\n",
    "sed -i '/## 1. /c\\empirical_4/           ## 1. working directory ' params.txt\n",
    "sed -i '/## 6. /c\\TGCAGG                 ## 6. cutters ' params.txt\n",
    "sed -i '/## 7. /c\\30                     ## 7. N processors      ' params.txt\n",
    "sed -i '/## 9. /c\\6                      ## 9. NQual             ' params.txt\n",
    "sed -i '/## 10./c\\.85                    ## 10. clust threshold  ' params.txt\n",
    "sed -i '/## 12./c\\4                      ## 12. MinCov           ' params.txt\n",
    "sed -i '/## 13./c\\10                     ## 13. maxSH            ' params.txt\n",
    "sed -i '/## 14./c\\empirical_4_m4         ## 14. output name      ' params.txt\n",
    "sed -i '/## 18./c\\empirical_4/fastq/*.gz ## 18. data location    ' params.txt\n",
    "sed -i '/## 29./c\\2,2                    ## 29. trim overhang    ' params.txt\n",
    "sed -i '/## 30./c\\p,n,s                  ## 30. output formats   ' params.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==** parameter inputs for pyRAD version 3.0.63  **======================== affected step ==\r\n",
      "empirical_4/           ## 1. working directory \r\n",
      "./*.fastq.gz              ## 2. Loc. of non-demultiplexed files (if not line 18)  (s1)\r\n",
      "./*.barcodes              ## 3. Loc. of barcode file (if not line 18)             (s1)\r\n",
      "vsearch                   ## 4. command (or path) to call vsearch (or usearch)    (s3,s6)\r\n",
      "muscle                    ## 5. command (or path) to call muscle                  (s3,s7)\r\n",
      "TGCAGG                 ## 6. cutters \r\n",
      "30                     ## 7. N processors      \r\n",
      "6                         ## 8. Mindepth: min coverage for a cluster              (s4,s5)\r\n",
      "6                      ## 9. NQual             \r\n",
      ".85                    ## 10. clust threshold  \r\n",
      "rad                       ## 11. Datatype: rad,gbs,pairgbs,pairddrad,(others:see docs)(all)\r\n",
      "4                      ## 12. MinCov           \r\n",
      "10                     ## 13. maxSH            \r\n",
      "empirical_4_m4         ## 14. output name      \r\n",
      "==== optional params below this line ===================================  affected step ==\r\n",
      "                       ## 15.opt.: select subset (prefix* only selector)            (s2-s7)\r\n",
      "                       ## 16.opt.: add-on (outgroup) taxa (list or prefix*)         (s6,s7)\r\n",
      "                       ## 17.opt.: exclude taxa (list or prefix*)                   (s7)\r\n",
      "empirical_4/fastq/*.gz ## 18. data location    \r\n",
      "                       ## 19.opt.: maxM: N mismatches in barcodes (def= 1)          (s1)\r\n",
      "                       ## 20.opt.: phred Qscore offset (def= 33)                    (s2)\r\n",
      "                       ## 21.opt.: filter: def=0=NQual 1=NQual+adapters. 2=strict   (s2)\r\n",
      "                       ## 22.opt.: a priori E,H (def= 0.001,0.01, if not estimated) (s5)\r\n",
      "                       ## 23.opt.: maxN: max Ns in a cons seq (def=5)               (s5)\r\n",
      "                       ## 24.opt.: maxH: max heterozyg. sites in cons seq (def=5)   (s5)\r\n",
      "                       ## 25.opt.: ploidy: max alleles in cons seq (def=2;see docs) (s4,s5)\r\n",
      "                       ## 26.opt.: maxSNPs: (def=100). Paired (def=100,100)         (s7)\r\n",
      "                       ## 27.opt.: maxIndels: within-clust,across-clust (def. 3,99) (s3,s7)\r\n",
      "                       ## 28.opt.: random number seed (def. 112233)              (s3,s6,s7)\r\n",
      "2,2                    ## 29. trim overhang    \r\n",
      "p,n,s                  ## 30. output formats   \r\n",
      "                       ## 31.opt.: maj. base call at depth>x<mindepth (def.x=mindepth) (s5)\r\n",
      "                       ## 32.opt.: keep trimmed reads (def=0). Enter min length.    (s2)\r\n",
      "                       ## 33.opt.: max stack size (int), def= max(500,mean+2*SD)    (s3)\r\n",
      "                       ## 34.opt.: minDerep: exclude dereps with <= N copies, def=1 (s3)\r\n",
      "                       ## 35.opt.: use hierarchical clustering (def.=0, 1=yes)      (s6)\r\n",
      "                       ## 36.opt.: repeat masking (def.=1='dust' method, 0=no)      (s3,s6)\r\n",
      "                       ## 37.opt.: vsearch max threads per job (def.=6; see docs)   (s3,s6)\r\n",
      "==== optional: list group/clade assignments below this line (see docs) ==================\r\n"
     ]
    }
   ],
   "source": [
    "cat params.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Assemble in _pyrad_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pyrad -p params.txt -s 234567 >> log.txt 2>&1  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "sed -i '/## 12./c\\2                      ## 12. MinCov           ' params.txt\n",
    "sed -i '/## 14./c\\empirical_4_m2         ## 14. output name      ' params.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "pyrad -p params.txt -s 7 >> log.txt 2>&1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results\n",
    "We are interested in the relationship between the amount of input (raw) data between any two samples, the average coverage they recover when clustered together, and the phylogenetic distances separating samples.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Raw data amounts\n",
    "The average number of raw reads per sample is 1.36M."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count         64.000000\n",
      "mean     3959338.812500\n",
      "std       811579.026685\n",
      "min       731712.000000\n",
      "25%      3635737.000000\n",
      "50%      4054918.000000\n",
      "75%      4540098.750000\n",
      "max      5074496.000000\n",
      "Name: passed.total, dtype: float64\n",
      "\n",
      "most raw data in sample:\n",
      "10    DRR036775\n",
      "Name: sample , dtype: object\n"
     ]
    }
   ],
   "source": [
    "## import data frame \n",
    "import pandas as pd\n",
    "\n",
    "## read in the data\n",
    "s4dat = pd.read_table(\"empirical_4/stats/s2.rawedit.txt\", header=0, nrows=65)\n",
    "\n",
    "## print summary stats\n",
    "print s4dat[\"passed.total\"].describe()\n",
    "\n",
    "## find which sample has the most raw data\n",
    "maxraw = s4dat[\"passed.total\"].max()\n",
    "print \"\\nmost raw data in sample:\"\n",
    "print s4dat['sample '][s4dat['passed.total']==maxraw]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Look at distributions of coverage\n",
    "pyrad v.3.0.63 outputs depth information for each sample which I read in here and plot. First let's ask which sample has the highest depth of coverage. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "summary of means\n",
      "==================\n",
      "count     64.000000\n",
      "mean     106.000750\n",
      "std       17.392284\n",
      "min       40.235000\n",
      "25%       98.422750\n",
      "50%      107.650000\n",
      "75%      118.500750\n",
      "max      131.455000\n",
      "Name: dpt.me, dtype: float64\n",
      "\n",
      "summary of std\n",
      "==================\n",
      "count     64.000000\n",
      "mean     289.564375\n",
      "std      140.245059\n",
      "min       95.184000\n",
      "25%      205.293000\n",
      "50%      235.668500\n",
      "75%      325.144000\n",
      "max      757.646000\n",
      "Name: dpt.sd, dtype: float64\n",
      "\n",
      "summary of proportion lowdepth\n",
      "==================\n",
      "count    64.000000\n",
      "mean      0.053360\n",
      "std       0.010158\n",
      "min       0.038625\n",
      "25%       0.046790\n",
      "50%       0.051447\n",
      "75%       0.058441\n",
      "max       0.104888\n",
      "dtype: float64\n",
      "\n",
      "highest coverage in sample:\n",
      "10    DRR036775\n",
      "Name: taxa, dtype: object\n"
     ]
    }
   ],
   "source": [
    "## read in the s3 results\n",
    "s4dat = pd.read_table(\"empirical_4/stats/s3.clusters.txt\", header=0, nrows=65)\n",
    "\n",
    "## print summary stats\n",
    "print \"summary of means\\n==================\"\n",
    "print s4dat['dpt.me'].describe()\n",
    "\n",
    "## print summary stats\n",
    "print \"\\nsummary of std\\n==================\"\n",
    "print s4dat['dpt.sd'].describe()\n",
    "\n",
    "## print summary stats\n",
    "print \"\\nsummary of proportion lowdepth\\n==================\"\n",
    "print pd.Series(1-s4dat['d>5.tot']/s4dat[\"total\"]).describe()\n",
    "\n",
    "## find which sample has the greatest depth of retained loci\n",
    "max_hiprop = (s4dat[\"d>5.tot\"]/s4dat[\"total\"]).max()\n",
    "print \"\\nhighest coverage in sample:\"\n",
    "print s4dat['taxa'][s4dat['d>5.tot']/s4dat[\"total\"]==max_hiprop]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot the coverage for the sample with highest mean coverage\n",
    "Green shows the loci that were discarded and orange the loci that were retained. The majority of data were discarded for being too low of coverage. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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      ],
      "text/plain": [
       "<toyplot.canvas.Canvas at 0x7ff3a41909d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import toyplot\n",
    "import toyplot.svg\n",
    "import numpy as np\n",
    "\n",
    "## read in the depth information for this sample\n",
    "with open(\"empirical_4/clust.85/DRR036775.depths\", 'rb') as indat:\n",
    "    depths = np.array(indat.read().strip().split(\",\"), dtype=int)\n",
    "    \n",
    "## make a barplot in Toyplot\n",
    "canvas = toyplot.Canvas(width=350, height=300)\n",
    "axes = canvas.axes(xlabel=\"Depth of coverage (N reads)\", \n",
    "                   ylabel=\"N loci\", \n",
    "                   label=\"dataset4/sample=DRR036775\")\n",
    "\n",
    "## select the loci with depth > 5 (kept)\n",
    "keeps = depths[depths>5]\n",
    "\n",
    "## plot kept and discarded loci\n",
    "edat = np.histogram(depths, range(30)) # density=True)\n",
    "kdat = np.histogram(keeps, range(30)) #, density=True)\n",
    "axes.bars(edat)\n",
    "axes.bars(kdat)\n",
    "\n",
    "#toyplot.svg.render(canvas, \"empirical_4_depthplot.svg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Print final stats table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\r\n",
      "46277       ## loci with > minsp containing data\r\n",
      "42877       ## loci with > minsp containing data & paralogs removed\r\n",
      "42877       ## loci with > minsp containing data & paralogs removed & final filtering\r\n",
      "\r\n",
      "## number of loci recovered in final data set for each taxon.\r\n",
      "taxon\tnloci\r\n",
      "DRR036765\t27950\r\n",
      "DRR036766\t30223\r\n",
      "DRR036767\t29273\r\n",
      "DRR036768\t28909\r\n",
      "DRR036769\t28879\r\n",
      "DRR036770\t29524\r\n",
      "DRR036771\t28375\r\n",
      "DRR036772\t28756\r\n",
      "DRR036773\t27906\r\n",
      "DRR036774\t28927\r\n",
      "DRR036775\t31633\r\n",
      "DRR036776\t27658\r\n",
      "DRR036777\t30722\r\n",
      "DRR036778\t28349\r\n",
      "DRR036779\t27420\r\n",
      "DRR036780\t29359\r\n",
      "DRR036781\t28123\r\n",
      "DRR036782\t29928\r\n",
      "DRR036783\t29606\r\n",
      "DRR036784\t28864\r\n",
      "DRR036785\t25495\r\n",
      "DRR036786\t28829\r\n",
      "DRR036787\t29177\r\n",
      "DRR036788\t28432\r\n",
      "DRR036789\t28598\r\n",
      "DRR036790\t13855\r\n",
      "DRR036791\t28826\r\n",
      "DRR036792\t27106\r\n",
      "DRR036793\t30117\r\n",
      "DRR036794\t30466\r\n",
      "DRR036795\t29279\r\n",
      "DRR036796\t28378\r\n",
      "DRR036797\t29160\r\n",
      "DRR036798\t28070\r\n",
      "DRR036799\t29059\r\n",
      "DRR036800\t29404\r\n",
      "DRR036801\t28522\r\n",
      "DRR036802\t27407\r\n",
      "DRR036803\t22502\r\n",
      "DRR036804\t29971\r\n",
      "DRR036805\t26894\r\n",
      "DRR036806\t28787\r\n",
      "DRR036807\t30322\r\n",
      "DRR036808\t29587\r\n",
      "DRR036809\t28472\r\n",
      "DRR036810\t24144\r\n",
      "DRR036811\t29457\r\n",
      "DRR036812\t29349\r\n",
      "DRR036813\t28553\r\n",
      "DRR036814\t29899\r\n",
      "DRR036815\t27081\r\n",
      "DRR036816\t27908\r\n",
      "DRR036817\t30888\r\n",
      "DRR036818\t27955\r\n",
      "DRR036819\t28459\r\n",
      "DRR036820\t28671\r\n",
      "DRR036821\t30179\r\n",
      "DRR036822\t30425\r\n",
      "DRR036823\t28337\r\n",
      "DRR036824\t28810\r\n",
      "DRR036825\t29466\r\n",
      "DRR036826\t28469\r\n",
      "DRR036827\t28876\r\n",
      "DRR036828\t29248\r\n",
      "\r\n",
      "\r\n",
      "## nloci = number of loci with data for exactly ntaxa\r\n",
      "## ntotal = number of loci for which at least ntaxa have data\r\n",
      "ntaxa\tnloci\tsaved\tntotal\r\n",
      "1\t-\r\n",
      "2\t-\t\t-\r\n",
      "3\t-\t\t-\r\n",
      "4\t562\t*\t42877\r\n",
      "5\t496\t*\t42315\r\n",
      "6\t485\t*\t41819\r\n",
      "7\t517\t*\t41334\r\n",
      "8\t627\t*\t40817\r\n",
      "9\t910\t*\t40190\r\n",
      "10\t1361\t*\t39280\r\n",
      "11\t1620\t*\t37919\r\n",
      "12\t1031\t*\t36299\r\n",
      "13\t343\t*\t35268\r\n",
      "14\t184\t*\t34925\r\n",
      "15\t160\t*\t34741\r\n",
      "16\t171\t*\t34581\r\n",
      "17\t212\t*\t34410\r\n",
      "18\t192\t*\t34198\r\n",
      "19\t192\t*\t34006\r\n",
      "20\t200\t*\t33814\r\n",
      "21\t187\t*\t33614\r\n",
      "22\t215\t*\t33427\r\n",
      "23\t199\t*\t33212\r\n",
      "24\t221\t*\t33013\r\n",
      "25\t248\t*\t32792\r\n",
      "26\t243\t*\t32544\r\n",
      "27\t225\t*\t32301\r\n",
      "28\t276\t*\t32076\r\n",
      "29\t233\t*\t31800\r\n",
      "30\t227\t*\t31567\r\n",
      "31\t245\t*\t31340\r\n",
      "32\t295\t*\t31095\r\n",
      "33\t273\t*\t30800\r\n",
      "34\t293\t*\t30527\r\n",
      "35\t314\t*\t30234\r\n",
      "36\t254\t*\t29920\r\n",
      "37\t322\t*\t29666\r\n",
      "38\t349\t*\t29344\r\n",
      "39\t352\t*\t28995\r\n",
      "40\t373\t*\t28643\r\n",
      "41\t397\t*\t28270\r\n",
      "42\t416\t*\t27873\r\n",
      "43\t446\t*\t27457\r\n",
      "44\t521\t*\t27011\r\n",
      "45\t558\t*\t26490\r\n",
      "46\t723\t*\t25932\r\n",
      "47\t828\t*\t25209\r\n",
      "48\t875\t*\t24381\r\n",
      "49\t1021\t*\t23506\r\n",
      "50\t1062\t*\t22485\r\n",
      "51\t1072\t*\t21423\r\n",
      "52\t957\t*\t20351\r\n",
      "53\t892\t*\t19394\r\n",
      "54\t1047\t*\t18502\r\n",
      "55\t1189\t*\t17455\r\n",
      "56\t1476\t*\t16266\r\n",
      "57\t1890\t*\t14790\r\n",
      "58\t2200\t*\t12900\r\n",
      "59\t2433\t*\t10700\r\n",
      "60\t2672\t*\t8267\r\n",
      "61\t2464\t*\t5595\r\n",
      "62\t1846\t*\t3131\r\n",
      "63\t1019\t*\t1285\r\n",
      "64\t266\t*\t266\r\n",
      "\r\n",
      "\r\n",
      "## nvar = number of loci containing n variable sites (pis+autapomorphies).\r\n",
      "## sumvar = sum of variable sites (SNPs).\r\n",
      "## pis = number of loci containing n parsimony informative sites.\r\n",
      "## sumpis = sum of parsimony informative sites.\r\n",
      "\tnvar\tsumvar\tPIS\tsumPIS\r\n",
      "0\t4922\t0\t12420\t0\r\n",
      "1\t5533\t5533\t8723\t8723\r\n",
      "2\t5742\t17017\t7273\t23269\r\n",
      "3\t5663\t34006\t5269\t39076\r\n",
      "4\t5184\t54742\t3641\t53640\r\n",
      "5\t4499\t77237\t2239\t64835\r\n",
      "6\t3409\t97691\t1263\t72413\r\n",
      "7\t2451\t114848\t733\t77544\r\n",
      "8\t1691\t128376\t401\t80752\r\n",
      "9\t1177\t138969\t279\t83263\r\n",
      "10\t739\t146359\t199\t85253\r\n",
      "11\t537\t152266\t138\t86771\r\n",
      "12\t358\t156562\t88\t87827\r\n",
      "13\t263\t159981\t70\t88737\r\n",
      "14\t169\t162347\t46\t89381\r\n",
      "15\t135\t164372\t28\t89801\r\n",
      "16\t84\t165716\t24\t90185\r\n",
      "17\t86\t167178\t7\t90304\r\n",
      "18\t47\t168024\t14\t90556\r\n",
      "19\t50\t168974\t3\t90613\r\n",
      "20\t34\t169654\t2\t90653\r\n",
      "21\t23\t170137\t6\t90779\r\n",
      "22\t18\t170533\t8\t90955\r\n",
      "23\t19\t170970\t1\t90978\r\n",
      "24\t13\t171282\t1\t91002\r\n",
      "25\t8\t171482\t0\t91002\r\n",
      "26\t5\t171612\t0\t91002\r\n",
      "27\t7\t171801\t0\t91002\r\n",
      "28\t2\t171857\t0\t91002\r\n",
      "29\t1\t171886\t1\t91031\r\n",
      "30\t2\t171946\t0\t91031\r\n",
      "31\t1\t171977\t0\t91031\r\n",
      "32\t3\t172073\t0\t91031\r\n",
      "33\t0\t172073\t0\t91031\r\n",
      "34\t1\t172107\t0\t91031\r\n",
      "35\t0\t172107\t0\t91031\r\n",
      "36\t0\t172107\t0\t91031\r\n",
      "37\t0\t172107\t0\t91031\r\n",
      "38\t0\t172107\t0\t91031\r\n",
      "39\t0\t172107\t0\t91031\r\n",
      "40\t0\t172107\t0\t91031\r\n",
      "41\t0\t172107\t0\t91031\r\n",
      "42\t0\t172107\t0\t91031\r\n",
      "43\t1\t172150\t0\t91031\r\n",
      "total var= 172150\r\n",
      "total pis= 91031\r\n",
      "sampled unlinked SNPs= 37955\r\n",
      "sampled unlinked bi-allelic SNPs= -30031\r\n"
     ]
    }
   ],
   "source": [
    "cat empirical_4/stats/empirical_4_m4.stats\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "48546       ## loci with > minsp containing data\n",
      "45146       ## loci with > minsp containing data & paralogs removed\n",
      "45146       ## loci with > minsp containing data & paralogs removed & final filtering\n",
      "\n",
      "## number of loci recovered in final data set for each taxon.\n",
      "taxon\tnloci\n",
      "DRR036765\t28070\n",
      "DRR036766\t30376\n",
      "DRR036767\t29411\n",
      "DRR036768\t29051\n",
      "DRR036769\t29022\n",
      "DRR036770\t29587\n",
      "DRR036771\t28435\n",
      "DRR036772\t28812\n",
      "DRR036773\t27965\n",
      "DRR036774\t28996\n",
      "DRR036775\t31778\n",
      "DRR036776\t27701\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "head -n 20 empirical_4/stats/empirical_4_m2.stats\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Infer ML phylogeny in _raxml_ as an unrooted tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "## raxml argumement w/ ...\n",
    "raxmlHPC-PTHREADS-AVX -f a -m GTRGAMMA -N 100 -x 12345 -p 12345 -T 20 \\\n",
    "                      -w /home/deren/Documents/RADmissing/empirical_4/ \\\n",
    "                      -n empirical_4_m4 -s empirical_4/outfiles/empirical_4_m4.phy\n",
    "                      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "## raxml argumement w/ ...\n",
    "raxmlHPC-PTHREADS-AVX -f a -m GTRGAMMA -N 100 -x 12345 -p 12345 -T 20 \\\n",
    "                      -w /home/deren/Documents/RADmissing/empirical_4/ \\\n",
    "                      -n empirical_4_m2 -s empirical_4/outfiles/empirical_4_m2.phy\n",
    "                      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "This is RAxML version 8.0.16 released by Alexandros Stamatakis on March 21 2014.\n",
      "\n",
      "With greatly appreciated code contributions by:\n",
      "Andre Aberer      (HITS)\n",
      "Simon Berger      (HITS)\n",
      "Alexey Kozlov     (HITS)\n",
      "Nick Pattengale   (Sandia)\n",
      "Wayne Pfeiffer    (SDSC)\n",
      "Akifumi S. Tanabe (NRIFS)\n",
      "David Dao         (KIT)\n",
      "Charlie Taylor    (UF)\n",
      "\n",
      "\n",
      "Alignment has 339249 distinct alignment patterns\n",
      "\n",
      "Proportion of gaps and completely undetermined characters in this alignment: 33.82%\n",
      "\n",
      "RAxML rapid bootstrapping and subsequent ML search\n",
      "\n",
      "Using 1 distinct models/data partitions with joint branch length optimization\n",
      "\n",
      "\n",
      "\n",
      "Executing 100 rapid bootstrap inferences and thereafter a thorough ML search \n",
      "\n",
      "All free model parameters will be estimated by RAxML\n",
      "GAMMA model of rate heteorgeneity, ML estimate of alpha-parameter\n",
      "\n",
      "GAMMA Model parameters will be estimated up to an accuracy of 0.1000000000 Log Likelihood units\n",
      "\n",
      "Partition: 0\n",
      "Alignment Patterns: 339249\n",
      "Name: No Name Provided\n",
      "DataType: DNA\n",
      "Substitution Matrix: GTR\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "head -n 40 empirical_4/RAxML_info.empirical_4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the tree in R using `ape`\n",
    "The backbone of the ingroup taxa has very low support across the radiation. The same result was found in the original paper (see Fig. 4 and Supplemental Fig S1 of Takahashi et al). Below we plot the full tree and a zoomed in tree of just the ingroup taxa. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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p0quamoZXr47t2aHXU3+g2bCXz4sa6l7V//nnn1WV187+YmhsIrmtvN1Za2Px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w76OnuCrw9TG33BZ+/L7KeGFjaKnNr/Gnelvr7ewMCgoaGhbVV1dXVubq66urqkpCRv+eTJkz/v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\nomck9K85uwgAAAAASUVORK5CYII=\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 1000 -h 800\n",
    "library(ape)\n",
    "tre <- read.tree(\"empirical_4/RAxML_bipartitions.empirical_4\")\n",
    "ltre <- ladderize(tre)\n",
    "outgroups = c(\"DRR036791\", \"DRR036765\", \"DRR036790\", \"DRR036767\", \"DRR036769\",\n",
    "              \"DRR036766\", \"DRR036777\", \"DRR036793\", \"DRR036778\", \"DRR036778\", \n",
    "              \"DRR036792\", \"DRR036768\", \"DRR036775\")\n",
    "rtre <- root(ltre, outgroups)\n",
    "ingrouptre <- drop.tip(ltre, outgroups)    \n",
    "par(mfrow=c(1,2))\n",
    "\n",
    "plot(ltre, edge.width=2)\n",
    "nodelabels(ltre$node.label, cex=1)\n",
    "plot(ingrouptre, edge.width=2)\n",
    "nodelabels(ingrouptre$node.label, cex=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 0.009676341\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "mean(cophenetic.phylo(ltre))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### An ultrametric tree for plotting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Kf0z424YQvJ79Z3hMW+GPCX/ryBZMWuGPCX/7CAX3mMT+IzwmrfDHhL99hILb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\nwY4DwY4DwY4DwY7z/wt0H402gAmDAAAAAElFTkSuQmCC\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -h 700\n",
    "utre <- ladderize(chronopl(rtre, 0.5, resolve.root=TRUE))\n",
    "plot(utre, edge.width=2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}