emp_nb_Barnacles.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notebook 8: \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": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    }
   ],
   "source": [
    "### Notebook 8\n",
    "### Data set 8: Barnacles\n",
    "### Authors: Herrera et al. 2015\n",
    "### Data Location: SRP051026"
   ]
  },
  {
   "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 SRA: SRP051026 \n",
    "+ BioProject ID: PRJNA269631\n",
    "+ SRA link: http://trace.ncbi.nlm.nih.gov/Traces/study/?acc=SRP051026"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "## make a new directory for this analysis\n",
    "mkdir -p empirical_8/fastq/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For each ERS (individuals) get all of the ERR (sequence file accessions)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    BioSample_s  MBases_l  MBytes_l                   Organism_s  \\\n",
      "0  SAMN03256053        39        32    Ashinkailepas seepiophila   \n",
      "1  SAMN03256054        87        72  Ashinkailepas kermadecensis   \n",
      "2  SAMN03256055        17        14             Leucolepas longa   \n",
      "3  SAMN03256056        78        65        Neobrachylepas relica   \n",
      "4  SAMN03256057         7         6         Neolepas sp. SH-2014   \n",
      "\n",
      "  ReleaseDate_s       Run_s SRA_Sample_s Sample_Name_s    collected_by_s  \\\n",
      "0  Dec 10, 2014  SRR1702480    SRS785645         AsOK3  HOV Shinkai 2000   \n",
      "1  Dec 11, 2014  SRR1702481    SRS785647        AsNiN2    ROV Quest 4000   \n",
      "2  Dec 11, 2014  SRR1702482    SRS785649         VuTO2       HROV Nereus   \n",
      "3  Dec 11, 2014  SRR1702484    SRS785650         bar06       ROV Jason 2   \n",
      "4  Dec 11, 2014  SRR1702485    SRS785651         NeIn2  HOV Shinkai 6500   \n",
      "\n",
      "   collection_date_s    ...     Platform_s SRA_Study_s         breed_s  \\\n",
      "0               1997    ...       ILLUMINA   SRP051026  not applicable   \n",
      "1               2012    ...       ILLUMINA   SRP051026  not applicable   \n",
      "2               2009    ...       ILLUMINA   SRP051026  not applicable   \n",
      "3               2009    ...       ILLUMINA   SRP051026  not applicable   \n",
      "4               2009    ...       ILLUMINA   SRP051026  not applicable   \n",
      "\n",
      "  g1k_analysis_group_s  g1k_pop_code_s identified_by_s       isolate_s  \\\n",
      "0       <not provided>  <not provided>      S. Herrera  not applicable   \n",
      "1       <not provided>  <not provided>      S. Herrera  not applicable   \n",
      "2       <not provided>  <not provided>      S. Herrera  not applicable   \n",
      "3       <not provided>  <not provided>      S. Herrera  not applicable   \n",
      "4       <not provided>  <not provided>      S. Herrera  not applicable   \n",
      "\n",
      "  isolation_source_s        source_s  tissue_s  \n",
      "0  Hydrothermal vent  <not provided>    muscle  \n",
      "1  Hydrothermal vent  <not provided>    muscle  \n",
      "2  Hydrothermal vent  <not provided>    muscle  \n",
      "3  Hydrothermal vent  <not provided>    muscle  \n",
      "4  Hydrothermal vent  <not provided>    muscle  \n",
      "\n",
      "[5 rows x 33 columns]\n"
     ]
    }
   ],
   "source": [
    "## IPython code\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import urllib2\n",
    "import os\n",
    "\n",
    "## open the SRA run table from github url\n",
    "url = \"https://raw.githubusercontent.com/\"+\\\n",
    "      \"dereneaton/RADmissing/master/empirical_8_SraRunTable.txt\"\n",
    "intable = urllib2.urlopen(url)\n",
    "indata = pd.read_table(intable, sep=\"\\t\")\n",
    "\n",
    "## print first few rows\n",
    "print indata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def wget_download(SRR, outdir, outname):\n",
    "    \"\"\" Python function to get sra data from ncbi and write to\n",
    "    outdir with a new name using bash call wget \"\"\"\n",
    "    \n",
    "    ## get output name\n",
    "    output = os.path.join(outdir, outname+\".sra\")\n",
    "    \n",
    "    ## create a call string \n",
    "    call = \"wget -q -r -nH --cut-dirs=9 -O \"+output+\" \"+\\\n",
    "           \"ftp://ftp-trace.ncbi.nlm.nih.gov/\"+\\\n",
    "           \"sra/sra-instant/reads/ByRun/sra/SRR/\"+\\\n",
    "           \"{}/{}/{}.sra;\".format(SRR[:6], SRR, SRR)\n",
    "        \n",
    "    ## call bash script\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 with their sample names. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for ID, SRR in zip(indata.Sample_Name_s, indata.Run_s):\n",
    "    wget_download(SRR, \"empirical_8/fastq/\", ID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read 1012150 spots for empirical_8/fastq/166_40.sra\n",
      "Written 1012150 spots for empirical_8/fastq/166_40.sra\n",
      "Read 1581225 spots for empirical_8/fastq/72638_22.sra\n",
      "Written 1581225 spots for empirical_8/fastq/72638_22.sra\n",
      "Read 2024006 spots for empirical_8/fastq/82121_15.sra\n",
      "Written 2024006 spots for empirical_8/fastq/82121_15.sra\n",
      "Read 1020530 spots for empirical_8/fastq/AsNiN2.sra\n",
      "Written 1020530 spots for empirical_8/fastq/AsNiN2.sra\n",
      "Read 458811 spots for empirical_8/fastq/AsOK3.sra\n",
      "Written 458811 spots for empirical_8/fastq/AsOK3.sra\n",
      "Read 909109 spots for empirical_8/fastq/bar06.sra\n",
      "Written 909109 spots for empirical_8/fastq/bar06.sra\n",
      "Read 570988 spots for empirical_8/fastq/bar22.sra\n",
      "Written 570988 spots for empirical_8/fastq/bar22.sra\n",
      "Read 898436 spots for empirical_8/fastq/barJC6731B1_1.sra\n",
      "Written 898436 spots for empirical_8/fastq/barJC6731B1_1.sra\n",
      "Read 84629 spots for empirical_8/fastq/NeIn2.sra\n",
      "Written 84629 spots for empirical_8/fastq/NeIn2.sra\n",
      "Read 77842 spots for empirical_8/fastq/NeOg1.sra\n",
      "Written 77842 spots for empirical_8/fastq/NeOg1.sra\n",
      "Read 139189 spots for empirical_8/fastq/SEPR_3.sra\n",
      "Written 139189 spots for empirical_8/fastq/SEPR_3.sra\n",
      "Read 845743 spots for empirical_8/fastq/VuMaU1.sra\n",
      "Written 845743 spots for empirical_8/fastq/VuMaU1.sra\n",
      "Read 208597 spots for empirical_8/fastq/VuTO2.sra\n",
      "Written 208597 spots for empirical_8/fastq/VuTO2.sra\n",
      "Read 9831255 spots total\n",
      "Written 9831255 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_8/fastq/ empirical_8/fastq/*.sra\n",
    "\n",
    "## remove .sra files\n",
    "rm empirical_8/fastq/*.sra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 724M\n",
      "-rw-rw-r-- 1 deren deren  75M Nov 24 12:49 166_40.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren 117M Nov 24 12:49 72638_22.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren 151M Nov 24 12:49 82121_15.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren  75M Nov 24 12:49 AsNiN2.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren  34M Nov 24 12:49 AsOK3.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren  67M Nov 24 12:49 bar06.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren  42M Nov 24 12:49 bar22.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren  66M Nov 24 12:49 barJC6731B1_1.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren 6.3M Nov 24 12:49 NeIn2.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren 5.8M Nov 24 12:49 NeOg1.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren  11M Nov 24 12:49 SEPR_3.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren  63M Nov 24 12:49 VuMaU1.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren  15M Nov 24 12:49 VuTO2.fastq.gz\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "ls -lh empirical_8/fastq/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make a params file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\tnew params.txt file created\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "## remove old params file if it exists\n",
    "rm params.txt \n",
    "\n",
    "## create a new default params file\n",
    "pyrad -n "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Note: \n",
    "The data here are from Illumina Casava <1.8, so the phred scores are offset by 64 instead of 33, so we use that in the params file below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "## substitute new parameters into file\n",
    "sed -i '/## 1. /c\\empirical_8/           ## 1. working directory ' params.txt\n",
    "sed -i '/## 6. /c\\TGCAGG                 ## 6. cutters ' params.txt\n",
    "sed -i '/## 7. /c\\20                     ## 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_8_m4          ## 14. output name      ' params.txt\n",
    "sed -i '/## 18./c\\empirical_8/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": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==** parameter inputs for pyRAD version 3.0.63  **======================== affected step ==\r\n",
      "empirical_8/           ## 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",
      "20                     ## 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_8_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_8/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_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "pyrad -p params.txt -s 234567 >> log.txt 2>&1  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "sed -i '/## 12./c\\2                    ## 12. MinCov           ' params.txt\n",
    "sed -i '/## 14./c\\empirical_8_m2       ## 14. output name      ' params.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "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": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count         13.000000\n",
      "mean      702926.615385\n",
      "std       544381.441228\n",
      "min        72418.000000\n",
      "25%       197210.000000\n",
      "50%       775823.000000\n",
      "75%       945017.000000\n",
      "max      1854154.000000\n",
      "Name: passed.total, dtype: float64\n",
      "\n",
      "most raw data in sample:\n",
      "2    82121_15\n",
      "Name: sample , dtype: object\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "## read in the data\n",
    "s2dat = pd.read_table(\"empirical_8/stats/s2.rawedit.txt\", header=0, nrows=42)\n",
    "\n",
    "## print summary stats\n",
    "print s2dat[\"passed.total\"].describe()\n",
    "\n",
    "## find which sample has the most raw data\n",
    "maxraw = s2dat[\"passed.total\"].max()\n",
    "print \"\\nmost raw data in sample:\"\n",
    "print s2dat['sample '][s2dat['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. The std of coverages is pretty low in this data set compared to several others. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "summary of means\n",
      "==================\n",
      "count    13.000000\n",
      "mean     33.165923\n",
      "std      17.043971\n",
      "min      12.552000\n",
      "25%      18.726000\n",
      "50%      35.771000\n",
      "75%      40.778000\n",
      "max      67.946000\n",
      "Name: dpt.me, dtype: float64\n",
      "\n",
      "summary of std\n",
      "==================\n",
      "count     13.000000\n",
      "mean     109.360538\n",
      "std       76.550954\n",
      "min       19.386000\n",
      "25%       57.572000\n",
      "50%       94.316000\n",
      "75%      154.275000\n",
      "max      256.698000\n",
      "Name: dpt.sd, dtype: float64\n",
      "\n",
      "summary of proportion lowdepth\n",
      "==================\n",
      "count    13.000000\n",
      "mean      0.241166\n",
      "std       0.100536\n",
      "min       0.147948\n",
      "25%       0.164743\n",
      "50%       0.182743\n",
      "75%       0.310341\n",
      "max       0.422289\n",
      "dtype: float64\n",
      "\n",
      "highest coverage in sample:\n",
      "2    82121_15\n",
      "Name: taxa, dtype: object\n"
     ]
    }
   ],
   "source": [
    "## read in the s3 results\n",
    "s8dat = pd.read_table(\"empirical_8/stats/s3.clusters.txt\", header=0, nrows=14)\n",
    "\n",
    "## print summary stats\n",
    "print \"summary of means\\n==================\"\n",
    "print s8dat['dpt.me'].describe()\n",
    "\n",
    "## print summary stats\n",
    "print \"\\nsummary of std\\n==================\"\n",
    "print s8dat['dpt.sd'].describe()\n",
    "\n",
    "## print summary stats\n",
    "print \"\\nsummary of proportion lowdepth\\n==================\"\n",
    "print pd.Series(1-s8dat['d>5.tot']/s8dat[\"total\"]).describe()\n",
    "\n",
    "## find which sample has the greatest depth of retained loci\n",
    "max_hiprop = (s8dat[\"d>5.tot\"]/s8dat[\"total\"]).max()\n",
    "print \"\\nhighest coverage in sample:\"\n",
    "print s8dat['taxa'][s8dat['d>5.tot']/s8dat[\"total\"]==max_hiprop]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "highest prop coverage in sample:\n",
      "2    82121_15\n",
      "Name: taxa, dtype: object\n"
     ]
    }
   ],
   "source": [
    "maxprop =(s8dat['d>5.tot']/s8dat['total']).max()\n",
    "print \"\\nhighest prop coverage in sample:\"\n",
    "print s8dat['taxa'][s8dat['d>5.tot']/s8dat['total']==maxprop]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Means for sample 82121_15\n",
      "67.9461337257 256.697997144\n",
      "79.3792210284 276.499000416\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "## print mean and std of coverage for the highest coverage sample\n",
    "with open(\"empirical_8/clust.85/82121_15.depths\", 'rb') as indat:\n",
    "    depths = np.array(indat.read().strip().split(\",\"), dtype=int)\n",
    "    \n",
    "print \"Means for sample 82121_15\"\n",
    "print depths.mean(), depths.std()\n",
    "print depths[depths>5].mean(), depths[depths>5].std()"
   ]
  },
  {
   "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": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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     },
     "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_8/clust.85/82121_15.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=\"dataset8/sample=82121_15\")\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_8_depthplot.svg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Print final stats table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\r\n",
      "15502       ## loci with > minsp containing data\r\n",
      "15502       ## loci with > minsp containing data & paralogs removed\r\n",
      "15502       ## 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",
      "166_40       \t12509\r\n",
      "72638_22     \t12551\r\n",
      "82121_15     \t12957\r\n",
      "AsNiN2       \t8260\r\n",
      "AsOK3        \t5942\r\n",
      "NeIn2        \t2368\r\n",
      "NeOg1        \t636\r\n",
      "SEPR_3       \t2401\r\n",
      "VuMaU1       \t10889\r\n",
      "VuTO2        \t4745\r\n",
      "bar06        \t2308\r\n",
      "bar22        \t9924\r\n",
      "barJC6731B1_1\t12071\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\t2793\t*\t15502\r\n",
      "5\t2943\t*\t12709\r\n",
      "6\t3110\t*\t9766\r\n",
      "7\t2763\t*\t6656\r\n",
      "8\t2107\t*\t3893\r\n",
      "9\t1187\t*\t1786\r\n",
      "10\t469\t*\t599\r\n",
      "11\t116\t*\t130\r\n",
      "12\t14\t*\t14\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\t356\t0\t3376\t0\r\n",
      "1\t692\t692\t3028\t3028\r\n",
      "2\t1043\t2778\t2719\t8466\r\n",
      "3\t1249\t6525\t1967\t14367\r\n",
      "4\t1419\t12201\t1495\t20347\r\n",
      "5\t1502\t19711\t1012\t25407\r\n",
      "6\t1441\t28357\t714\t29691\r\n",
      "7\t1356\t37849\t426\t32673\r\n",
      "8\t1225\t47649\t319\t35225\r\n",
      "9\t1065\t57234\t169\t36746\r\n",
      "10\t843\t65664\t120\t37946\r\n",
      "11\t722\t73606\t76\t38782\r\n",
      "12\t642\t81310\t47\t39346\r\n",
      "13\t511\t87953\t17\t39567\r\n",
      "14\t396\t93497\t7\t39665\r\n",
      "15\t302\t98027\t3\t39710\r\n",
      "16\t205\t101307\t7\t39822\r\n",
      "17\t152\t103891\t0\t39822\r\n",
      "18\t148\t106555\t0\t39822\r\n",
      "19\t74\t107961\t0\t39822\r\n",
      "20\t56\t109081\t0\t39822\r\n",
      "21\t31\t109732\t0\t39822\r\n",
      "22\t33\t110458\t0\t39822\r\n",
      "23\t17\t110849\t0\t39822\r\n",
      "24\t12\t111137\t0\t39822\r\n",
      "25\t5\t111262\t0\t39822\r\n",
      "26\t1\t111288\t0\t39822\r\n",
      "27\t2\t111342\t0\t39822\r\n",
      "28\t2\t111398\t0\t39822\r\n",
      "total var= 111398\r\n",
      "total pis= 39822\r\n",
      "sampled unlinked SNPs= 15146\r\n",
      "sampled unlinked bi-allelic SNPs= 14830\r\n"
     ]
    }
   ],
   "source": [
    "cat empirical_8/stats/empirical_8_m4.stats\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\r\n",
      "26325       ## loci with > minsp containing data\r\n",
      "26325       ## loci with > minsp containing data & paralogs removed\r\n",
      "26325       ## 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",
      "166_40       \t14910\r\n",
      "72638_22     \t16286\r\n",
      "82121_15     \t16879\r\n",
      "AsNiN2       \t12110\r\n",
      "AsOK3        \t9262\r\n",
      "NeIn2        \t2676\r\n",
      "NeOg1        \t832\r\n",
      "SEPR_3       \t2729\r\n",
      "VuMaU1       \t12796\r\n",
      "VuTO2        \t5385\r\n",
      "bar06        \t2969\r\n",
      "bar22        \t11523\r\n",
      "barJC6731B1_1\t14297\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\t7376\t*\t26325\r\n",
      "3\t3447\t*\t18949\r\n",
      "4\t2793\t*\t15502\r\n",
      "5\t2943\t*\t12709\r\n",
      "6\t3110\t*\t9766\r\n",
      "7\t2763\t*\t6656\r\n",
      "8\t2107\t*\t3893\r\n",
      "9\t1187\t*\t1786\r\n",
      "10\t469\t*\t599\r\n",
      "11\t116\t*\t130\r\n",
      "12\t14\t*\t14\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\t3730\t0\t13784\t0\r\n",
      "1\t2679\t2679\t3314\t3314\r\n",
      "2\t2320\t7319\t2799\t8912\r\n",
      "3\t2157\t13790\t1998\t14906\r\n",
      "4\t2056\t22014\t1510\t20946\r\n",
      "5\t2029\t32159\t1015\t26021\r\n",
      "6\t1857\t43301\t714\t30305\r\n",
      "7\t1653\t54872\t426\t33287\r\n",
      "8\t1542\t67208\t319\t35839\r\n",
      "9\t1320\t79088\t169\t37360\r\n",
      "10\t1061\t89698\t120\t38560\r\n",
      "11\t921\t99829\t76\t39396\r\n",
      "12\t805\t109489\t47\t39960\r\n",
      "13\t632\t117705\t17\t40181\r\n",
      "14\t457\t124103\t7\t40279\r\n",
      "15\t320\t128903\t3\t40324\r\n",
      "16\t221\t132439\t7\t40436\r\n",
      "17\t167\t135278\t0\t40436\r\n",
      "18\t154\t138050\t0\t40436\r\n",
      "19\t80\t139570\t0\t40436\r\n",
      "20\t58\t140730\t0\t40436\r\n",
      "21\t33\t141423\t0\t40436\r\n",
      "22\t34\t142171\t0\t40436\r\n",
      "23\t17\t142562\t0\t40436\r\n",
      "24\t12\t142850\t0\t40436\r\n",
      "25\t5\t142975\t0\t40436\r\n",
      "26\t1\t143001\t0\t40436\r\n",
      "27\t2\t143055\t0\t40436\r\n",
      "28\t2\t143111\t0\t40436\r\n",
      "total var= 143111\r\n",
      "total pis= 40436\r\n",
      "sampled unlinked SNPs= 22595\r\n",
      "sampled unlinked bi-allelic SNPs= 22162\r\n"
     ]
    }
   ],
   "source": [
    "cat empirical_8/stats/empirical_8_m2.stats"
   ]
  },
  {
   "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_8/ \\\n",
    "                      -n empirical_8_m4 -s empirical_8/outfiles/empirical_8_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_8/ \\\n",
    "                      -n empirical_8_m2 -s empirical_8/outfiles/empirical_8_m2.phy\n",
    "                      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": 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 63494 distinct alignment patterns\n",
      "\n",
      "Proportion of gaps and completely undetermined characters in this alignment: 51.86%\n",
      "\n",
      "RAxML rapid bootstrapping and subsequent ML search\n"
     ]
    }
   ],
   "source": [
    "%%bash \n",
    "head -n 20 empirical_8/RAxML_info.empirical_8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the tree in R using `ape`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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LAAAAgGUIWAAAAAAsQ8ACAAAAYBkCFgAAAADLELAAAAAAWIaABQAAAMAyBCwAAAAAliFg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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -h 800 -w 800\n",
    "library(ape)\n",
    "tre <- read.tree(\"empirical_8/RAxML_bipartitions.empirical_8\")\n",
    "ltre <- ladderize(tre)\n",
    "\n",
    "par(mfrow=c(1,2))\n",
    "plot(ltre, use.edge.length=F)\n",
    "nodelabels(ltre$node.label)\n",
    "\n",
    "plot(ltre, type='u')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get phylo distances (GTRgamma dist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 0.05226094\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "mean(cophenetic.phylo(ltre))\n"
   ]
  }
 ],
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