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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "RNA-Seq"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Module 3- Comparing counts in libraries; Differential expression with goal of getting a nice plot. There are a few ways to go about this. Essentially you will be mapping reads to scaffold and counting (with a little statistics). I will outline a few ways to about this below."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**HotLink** A primary skillset for this module is making a plot of DEGs in R.\n",
      "A great tutorial can be carried out be simply downloading this zipped directory - <http://eagle.fish.washington.edu/whale/fish546/DESeq.zip>. \n",
      "Inside is a Lake Trout RNA-seq table, and commented out R script. All you have to do is run the commands."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###TOC   \n",
      "- [CLC](#CLC)\n",
      "- [DESeq](#DESeq)\n",
      "- [IPlant Tuxedo](#IPlant)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!sed "
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "CLC"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "CLC is probably the easiest way to get a table with count data.."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#video how-to run RNA-seq on CLC\n",
      "from IPython.display import YouTubeVideo\n",
      "\n",
      "YouTubeVideo('8q60pm8LrIc')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "        <iframe\n",
        "            width=\"400\"\n",
        "            height=300\"\n",
        "            src=\"https://www.youtube.com/embed/8q60pm8LrIc\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
        "        ></iframe>\n",
        "        "
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "<IPython.lib.display.YouTubeVideo at 0x104286b50>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Once you have RNA-seq files you can take a join, but it makes more sense to \"Run an Experiment in CLC\""
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<img src=\"http://eagle.fish.washington.edu/cnidarian/skitch/Set_Up_Experiment_and_CLC_Genomics_Workbench_4.0.3__Current_workspace__Default__18917CDB.png\" alt=\"Set_Up_Experiment_and_CLC_Genomics_Workbench_4.0.3__Current_workspace__Default__18917CDB.png\"/>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "---"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<img src=\"http://eagle.fish.washington.edu/cnidarian/skitch/CLC_Genomics_Workbench_4.0.3__Current_workspace__Default__18917D70.png\" alt=\"CLC_Genomics_Workbench_4.0.3__Current_workspace__Default__18917D70.png\"/>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<img src=\"http://eagle.fish.washington.edu/cnidarian/skitch/greenbird_189AB0DA.png\" alt=\"greenbird_189AB0DA_png\"/>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#exported from version 6 - tab delimne\n",
      "!head -2 /Volumes/web/cnidarian/fish546_0lyMF"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\"Feature ID\"\t\"Experiment - Range (original values)\"\t\"Experiment - IQR (original values)\"\t\"Experiment - Difference (original values)\"\t\"Experiment - Fold Change (original values)\"\t\"Male - filtered_106A_Male_Mix_TAGCTT_L004_R1 (paired) RNA-Seq - Expression values\"\t\"Male - filtered_106A_Male_Mix_TAGCTT_L004_R1 (paired) RNA-Seq - Gene length\"\t\"Male - filtered_106A_Male_Mix_TAGCTT_L004_R1 (paired) RNA-Seq - Unique gene reads\"\t\"Male - filtered_106A_Male_Mix_TAGCTT_L004_R1 (paired) RNA-Seq - Total gene reads\"\t\"Male - filtered_106A_Male_Mix_TAGCTT_L004_R1 (paired) RNA-Seq - RPKM\"\t\"Male - Means\"\t\"Female - filtered_106A_Female_Mix_GATCAG_L004_R1 (paired) RNA-Seq - Expression values\"\t\"Female - filtered_106A_Female_Mix_GATCAG_L004_R1 (paired) RNA-Seq - Gene length\"\t\"Female - filtered_106A_Female_Mix_GATCAG_L004_R1 (paired) RNA-Seq - Unique gene reads\"\t\"Female - filtered_106A_Female_Mix_GATCAG_L004_R1 (paired) RNA-Seq - Total gene reads\"\t\"Female - filtered_106A_Female_Mix_GATCAG_L004_R1 (paired) RNA-Seq - RPKM\"\t\"Female - Means\"\r\n",
        "comp10002_c0_seq1\t5\t5\t-5\t-2.667\t8\t569\t8\t8\t0.513\t8\t3\t569\t3\t3\t0.294\t3\r\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!awk -F '\\t' '{print $1,$9,$15}' </Volumes/web/cnidarian/fish546_0lyMF> /Volumes/web/cnidarian/fish546_0lyMF2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!head /Volumes/web/cnidarian/fish546_0lyMF2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\"Feature ID\" \"Male - filtered_106A_Male_Mix_TAGCTT_L004_R1 (paired) RNA-Seq - Total gene reads\" \"Female - filtered_106A_Female_Mix_GATCAG_L004_R1 (paired) RNA-Seq - Total gene reads\"\r\n",
        "comp10002_c0_seq1 8 3\r\n",
        "comp10005_c0_seq1 6 4\r\n",
        "comp10007_c0_seq1 21431 4484\r\n",
        "comp10008_c0_seq1 8360 4759\r\n",
        "comp10009_c0_seq1 16064 8768\r\n",
        "comp1000_c0_seq1 52 5\r\n",
        "comp10010_c0_seq1 15 8\r\n",
        "comp10012_c0_seq1 4 1\r\n",
        "comp10013_c0_seq1 5 3\r\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!sed 's/\"Feature ID\" \"Male - filtered_106A_Male_Mix_TAGCTT_L004_R1 (paired) RNA-Seq - Total gene reads\" \"Female - filtered_106A_Female_Mix_GATCAG_L004_R1 (paired) RNA-Seq - Total gene reads\"/Feature M F/g' </Volumes/web/cnidarian/fish546_0lyMF2> /Volumes/web/cnidarian/fish546_0lyMF3\n",
      "#sed 's/abc/XYZ/g' <infile> outfile"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!head /Volumes/web/cnidarian/fish546_0lyMF3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Feature M F\r\n",
        "comp10002_c0_seq1 8 3\r\n",
        "comp10005_c0_seq1 6 4\r\n",
        "comp10007_c0_seq1 21431 4484\r\n",
        "comp10008_c0_seq1 8360 4759\r\n",
        "comp10009_c0_seq1 16064 8768\r\n",
        "comp1000_c0_seq1 52 5\r\n",
        "comp10010_c0_seq1 15 8\r\n",
        "comp10012_c0_seq1 4 1\r\n",
        "comp10013_c0_seq1 5 3\r\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Just xy plot?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pandas import *\n",
      "\n",
      "frank = read_table(\"/Volumes/web/cnidarian/fish546_0lyMF3\", # name of the data file\n",
      "            sep=\" \", # what character separates each column?\n",
      "            na_values=[\"\", \" \"]) # what values should be considered \"blank\" values?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frank.tail()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Feature</th>\n",
        "      <th>M</th>\n",
        "      <th>F</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>110062</th>\n",
        "      <td> comp9993_c0_seq2</td>\n",
        "      <td>    42</td>\n",
        "      <td>   32</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>110063</th>\n",
        "      <td> comp9994_c0_seq1</td>\n",
        "      <td>     6</td>\n",
        "      <td>    2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>110064</th>\n",
        "      <td> comp9996_c0_seq1</td>\n",
        "      <td>     3</td>\n",
        "      <td>    1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>110065</th>\n",
        "      <td> comp9998_c0_seq1</td>\n",
        "      <td> 10965</td>\n",
        "      <td> 6158</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>110066</th>\n",
        "      <td>   comp99_c0_seq1</td>\n",
        "      <td>     1</td>\n",
        "      <td>    1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "                 Feature      M     F\n",
        "110062  comp9993_c0_seq2     42    32\n",
        "110063  comp9994_c0_seq1      6     2\n",
        "110064  comp9996_c0_seq1      3     1\n",
        "110065  comp9998_c0_seq1  10965  6158\n",
        "110066    comp99_c0_seq1      1     1"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "WARNING: pylab import has clobbered these variables: ['load', 'info', 'datetime', 'save', 'set_printoptions', 'unique']\n",
        "`%pylab --no-import-all` prevents importing * from pylab and numpy\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "\n",
      "M = frank.M\n",
      "F = frank.F\n",
      "\n",
      "#plot the data\n",
      "plt.plot(M, F, 'bo')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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NBpvNhr17987kFIiISEMzCqFt27YhNTVV/aru2tpaOBwOfPzxx8jNzUVtbS0A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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1088a8490>"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(F, M, 'gs')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "[<matplotlib.lines.Line2D at 0x1088db190>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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RESnDECIiImUYQkREpAxDiIiIlGEIERGRMhGqC6DvVvmGcjT7m0PazQYzql+sDns9RETX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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108375650>"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter(M, F)\n",
      "xlim(0, 2000); ylim(0, 2000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "(0, 2000)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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69eqDyMhICIKArVu3QqcrD6JXktw/w83Nz6LNyZO/AWNuMBhagDEXfPvtApw8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\nWUIhW53uL25TmDx5BjfccAOVlZU89NBDWoz7Ony+S0gk8vjoo4/Snvnxx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X9pouButucR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\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1088cca90>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.loglog(M, F, 'gs')\n",
      "plt.xlabel('Male Expression', fontsize=20)\n",
      "plt.ylabel('Female Expression', fontsize=20)\n",
      "\n",
      "plt.axis([0, 1000000, 0, 1000000])\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "[0, 1000000, 0, 1000000]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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rUVNTgx/96EcYNChR+lKNZuCgqsgYLA7GCy4TwH7At9qHSF7EenpXwf/rJGM6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\nzrLPE3+bXcKpJCcno29f60/tf//73xgxYgQiIiLQo0cPZGRkoKioCE8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ktcjKygIRITc3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\nvms4DcIAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1085db1d0>"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "DESeq"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%reload_ext rmagic  "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%R library( \"DESeq\" )\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "Loading required package: Biobase\n",
        "Loading required package: BiocGenerics\n",
        "\n",
        "Attaching package: \u2018BiocGenerics\u2019\n",
        "\n",
        "The following object(s) are masked from \u2018package:stats\u2019:\n",
        "\n",
        "    xtabs\n",
        "\n",
        "The following object(s) are masked from \u2018package:base\u2019:\n",
        "\n",
        "    anyDuplicated, cbind, colnames, duplicated, eval, Filter, Find,\n",
        "    get, intersect, lapply, Map, mapply, mget, order, paste, pmax,\n",
        "    pmax.int, pmin, pmin.int, Position, rbind, Reduce, rep.int,\n",
        "    rownames, sapply, setdiff, table, tapply, union, unique\n",
        "\n",
        "Welcome to Bioconductor\n",
        "\n",
        "    Vignettes contain introductory material; view with\n",
        "    'browseVignettes()'. To cite Bioconductor, see\n",
        "    'citation(\"Biobase\")', and for packages 'citation(\"pkgname\")'.\n",
        "\n",
        "Loading required package: locfit\n",
        "locfit 1.5-8 \t 2012-04-25\n"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%R CountData<- read.table(\"/Volumes/web/cnidarian/fish546_0lyMF3\",row.names=1, header=TRUE,)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "array([[    8,     6, 21431, ...,     3, 10965,     1],\n",
        "       [    3,     4,  4484, ...,     1,  6158,     1]], dtype=int32)"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%R summary(CountData)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "array(['Min.   :      0.0  ', '1st Qu.:      3.0  ', 'Median :     13.0  ',\n",
        "       'Mean   :    249.1  ', '3rd Qu.:     76.0  ', 'Max.   :1863281.0  ',\n",
        "       'Min.   :      0.0  ', '1st Qu.:      2.0  ', 'Median :      9.0  ',\n",
        "       'Mean   :    162.7  ', '3rd Qu.:     51.0  ', 'Max.   :1602661.0  '], \n",
        "      dtype='|S19')"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# check that it is correct \n",
      "%R head (CountData)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "array([[    8,     6, 21431,  8360, 16064,    52],\n",
        "       [    3,     4,  4484,  4759,  8768,     5]], dtype=int32)"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# assign factors\n",
      "%R Treatment <- factor( c(\"Male\",\"Female\") )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "array([2, 1], dtype=int32)"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# create a new data set\n",
      "%R cds <- newCountDataSet( CountData, Treatment )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "array(<rpy2.rinterface.SexpS4 - Python:0x10e869600 / R:0x114d17958>, dtype=object)"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Estimate effective library size\n",
      "%R LibrarySize <- estimateSizeFactors( cds )\n",
      "%R sizeFactors( LibrarySize )\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "array([ 1.21854359,  0.82065181])"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#estimate Dispersons without replication\n",
      "%R Dispersons <- estimateDispersions( LibrarySize, method=\"blind\", sharingMode=\"fit-only\" )\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "array(<rpy2.rinterface.SexpS4 - Python:0x10e8696d8 / R:0x115688ee0>, dtype=object)"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# calculate differential gene expression \n",
      "%R DE <- nbinomTest( Dispersons, \"Male\", \"Female\" )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "array([['comp10002_c0_seq1', 'comp10005_c0_seq1', 'comp10007_c0_seq1', ...,\n",
        "        'comp9996_c0_seq1', 'comp9998_c0_seq1', 'comp99_c0_seq1'],\n",
        "       ['5.11042261413', '4.89904260332', '11525.6691667', ...,\n",
        "        '1.84024950582', '8251.11924876', '1.01959769917'],\n",
        "       ['6.56521445319', '4.92391083989', '17587.3888683', ...,\n",
        "        '2.46195541994', '8998.4470599', '0.820651806648'],\n",
        "       ..., \n",
        "       ['-0.844721774522', '-0.0146467759644', '-1.68652526171', ...,\n",
        "        '-1.01464677596', '-0.262056314759', '0.570315724757'],\n",
        "       ['0.885218680234', '1.0', '0.147533184865', ..., '0.987554518138',\n",
        "        '0.820202806666', '1.0'],\n",
        "       ['1.0', '1.0', '1.0', ..., '1.0', '1.0', '1.0']], \n",
        "      dtype='|S19')"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#check data\n",
      "\n",
      "%R head (DE)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "array([['comp10002_c0_seq1', 'comp10005_c0_seq1', 'comp10007_c0_seq1',\n",
        "        'comp10008_c0_seq1', 'comp10009_c0_seq1', 'comp1000_c0_seq1'],\n",
        "       ['5.11042261413', '4.89904260332', '11525.6691667', '6329.84902822',\n",
        "        '11933.570417', '24.3833059521'],\n",
        "       ['6.56521445319', '4.92391083989', '17587.3888683', '6860.64910358',\n",
        "        '13182.950622', '42.6738939457'],\n",
        "       ['3.65563077507', '4.87417436676', '5463.94946514', '5799.04895285',\n",
        "        '10684.1902119', '6.09271795845'],\n",
        "       ['0.556818181818', '0.989898989899', '0.310674285197',\n",
        "        '0.845262432942', '0.810455149101', '0.142773892774'],\n",
        "       ['-0.844721774522', '-0.0146467759644', '-1.68652526171',\n",
        "        '-0.242528763051', '-0.303195746233', '-2.8081958985'],\n",
        "       ['0.885218680234', '1.0', '0.147533184865', '0.833689827989',\n",
        "        '0.792097629318', '0.159574074145'],\n",
        "       ['1.0', '1.0', '1.0', '1.0', '1.0', '1.0']], \n",
        "      dtype='|S17')"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%R test.func <- function(x) {x^2}\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "array(<rpy2.rinterface.SexpClosure - Python:0x107469480 / R:0x10244f470>, dtype=object)"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%R test.func(939399339)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "array([  8.82471118e+17])"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%R plot(1:10,1:10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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BAgQINCoggBslsgEBAgQIECi9gAAuvakSCRAgQIBAowICuFEiGxAgQIAAgdILtC59kaUp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\noqY1KPns069//euFI+Bkj54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      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R plotDE <- function (DE)\n",
      "plot(\n",
      "DE$baseMean,DE$log2FoldChange,log=\"x\", pch=20, cex=.3\n",
      ")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "RRuntimeError",
       "evalue": "Error in png(\"/var/folders/28/07h2q2ms7nd609f1ft3mj1240000gn/T/tmpyjbUQB/Rplots%03d.png\",  : \n  too many open devices\n",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mRRuntimeError\u001b[0m                             Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-45-9f148f99388e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_cell_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mu'R'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mu'plotDE <- function (DE)'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mu'plot(\\nDE$baseMean,DE$log2FoldChange,log=\"x\", pch=20, cex=.3\\n)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Users/sr320/anaconda/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc\u001b[0m in \u001b[0;36mrun_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m   2127\u001b[0m             \u001b[0mmagic_arg_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvar_expand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2128\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2129\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2130\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2131\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/sr320/anaconda/lib/python2.7/site-packages/IPython/extensions/rmagic.pyc\u001b[0m in \u001b[0;36mR\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n",
        "\u001b[0;32m/Users/sr320/anaconda/lib/python2.7/site-packages/IPython/core/magic.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    189\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 191\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    192\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    193\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/sr320/anaconda/lib/python2.7/site-packages/IPython/extensions/rmagic.pyc\u001b[0m in \u001b[0;36mR\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m    575\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    576\u001b[0m         \u001b[0mtmpd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtempfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmkdtemp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 577\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'png(\"%s/Rplots%%03d.png\",%s)'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtmpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\\\'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'/'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpng_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    578\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m         \u001b[0mtext_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/sr320/anaconda/lib/python2.7/site-packages/rpy2-2.3.8-py2.7-macosx-10.5-x86_64.egg/rpy2/robjects/__init__.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, string)\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstring\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrinterface\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstring\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/sr320/anaconda/lib/python2.7/site-packages/rpy2-2.3.8-py2.7-macosx-10.5-x86_64.egg/rpy2/robjects/functions.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     84\u001b[0m                 \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mr_k\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 86\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSignatureTranslatedFunction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Users/sr320/anaconda/lib/python2.7/site-packages/rpy2-2.3.8-py2.7-macosx-10.5-x86_64.egg/rpy2/robjects/functions.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     33\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miteritems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m             \u001b[0mnew_kwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconversion\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpy2ri\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFunction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnew_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mnew_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     36\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconversion\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mri2py\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mRRuntimeError\u001b[0m: Error in png(\"/var/folders/28/07h2q2ms7nd609f1ft3mj1240000gn/T/tmpyjbUQB/Rplots%03d.png\",  : \n  too many open devices\n"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%R plotDE <- function(DE) {\n",
      "%R plot(DE$baseMean,DE$log2FoldChange,log=\"x\", pch=20, cex=.3)\n",
      "%R }\t\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "RRuntimeError",
       "evalue": "Error in png(\"/var/folders/28/07h2q2ms7nd609f1ft3mj1240000gn/T/tmpo5lt_R/Rplots%03d.png\",  : \n  too many open devices\n",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mRRuntimeError\u001b[0m                             Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-33-7dfee8f87d37>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mu'R plotDE <- function(DE) {'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mu'R plot(DE$baseMean,DE$log2FoldChange,log=\"x\", pch=20, cex=.3)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mu'R }'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/sr320/anaconda/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc\u001b[0m in \u001b[0;36mmagic\u001b[0;34m(self, arg_s)\u001b[0m\n\u001b[1;32m   2170\u001b[0m         \u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg_s\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartition\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2171\u001b[0m         \u001b[0mmagic_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmagic_name\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprefilter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mESC_MAGIC\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2172\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2173\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2174\u001b[0m     \u001b[0;31m#-------------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/sr320/anaconda/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc\u001b[0m in \u001b[0;36mrun_line_magic\u001b[0;34m(self, magic_name, line)\u001b[0m\n\u001b[1;32m   2091\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'local_ns'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_locals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2092\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2093\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2094\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2095\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/sr320/anaconda/lib/python2.7/site-packages/IPython/extensions/rmagic.pyc\u001b[0m in \u001b[0;36mR\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n",
        "\u001b[0;32m/Users/sr320/anaconda/lib/python2.7/site-packages/IPython/core/magic.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    189\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 191\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    192\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    193\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/sr320/anaconda/lib/python2.7/site-packages/IPython/extensions/rmagic.pyc\u001b[0m in \u001b[0;36mR\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m    575\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    576\u001b[0m         \u001b[0mtmpd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtempfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmkdtemp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 577\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'png(\"%s/Rplots%%03d.png\",%s)'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtmpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\\\'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'/'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpng_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    578\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m         \u001b[0mtext_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/sr320/anaconda/lib/python2.7/site-packages/rpy2-2.3.8-py2.7-macosx-10.5-x86_64.egg/rpy2/robjects/__init__.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, string)\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstring\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrinterface\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstring\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/sr320/anaconda/lib/python2.7/site-packages/rpy2-2.3.8-py2.7-macosx-10.5-x86_64.egg/rpy2/robjects/functions.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     84\u001b[0m                 \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mr_k\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 86\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSignatureTranslatedFunction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Users/sr320/anaconda/lib/python2.7/site-packages/rpy2-2.3.8-py2.7-macosx-10.5-x86_64.egg/rpy2/robjects/functions.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     33\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miteritems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m             \u001b[0mnew_kwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconversion\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpy2ri\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFunction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnew_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mnew_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     36\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconversion\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mri2py\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mRRuntimeError\u001b[0m: Error in png(\"/var/folders/28/07h2q2ms7nd609f1ft3mj1240000gn/T/tmpo5lt_R/Rplots%03d.png\",  : \n  too many open devices\n"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Write out\n",
      "%R write.table(DE, \"/Volumes/web/cnidarian/fish546_0lyMF_DElist\", row.names = FALSE, sep=\"\\t\")\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pandas import *\n",
      "\n",
      "pls2 = read_table(\"/Users/sr320/Desktop/DE.txt\", # name of the data file\n",
      "           sep=\" \", # what character separates each column?\n",
      "           na_values=[\"NA\",\"\", \" \"]) # what values should be considered \"blank\" values?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 117
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pls2.tail()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&ltclass 'pandas.core.frame.DataFrame'&gt\n",
        "Int64Index: 5 entries, 9360 to 9364\n",
        "Data columns (total 8 columns):\n",
        "id                5  non-null values\n",
        "baseMean          5  non-null values\n",
        "baseMeanA         5  non-null values\n",
        "baseMeanB         5  non-null values\n",
        "foldChange        5  non-null values\n",
        "log2FoldChange    5  non-null values\n",
        "pval              5  non-null values\n",
        "padj              5  non-null values\n",
        "dtypes: float64(5), object(3)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 119,
       "text": [
        "                   id   baseMean  baseMeanA  baseMeanB foldChange  \\\n",
        "9360  Snamaycush_9360  56.050275  36.514837  75.585713       2.07   \n",
        "9361  Snamaycush_9361  12.049896  10.954451  13.145341        1.2   \n",
        "9362  Snamaycush_9362  13.054054  11.867322  14.240786        1.2   \n",
        "9363  Snamaycush_9363   6.663958   4.564355   8.763561       1.92   \n",
        "9364  Snamaycush_9364   3.651484   1.825742   5.477226          3   \n",
        "\n",
        "         log2FoldChange      pval  padj  \n",
        "9360    1.0496307677246  0.095633     1  \n",
        "9361  0.263034405833794  0.904282     1  \n",
        "9362  0.263034405833794  0.894099     1  \n",
        "9363  0.941106310946432  0.661405     1  \n",
        "9364   1.58496250072116  0.627976     1  "
       ]
      }
     ],
     "prompt_number": 119
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#import matplotlib.pyplot as plt\n",
      "#import numpy as np\n",
      "#plot log2 fold changes against base means \n",
      "\n",
      "base = pls2.baseMean\n",
      "log = pls2.log2FoldChange\n",
      "\n",
      "#plot the data\n",
      "plt.plot(base, log, 'bo')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7p6FhWY4fP47b7SYxMZHjx483Ol7zPV9//TXg/EKcO3eObt26Wfu5lJeXc++9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\ncZSS4y7BAwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10e31aa90>"
       ]
      }
     ],
     "prompt_number": 120
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.semilogy(base, log, 'rs')\n",
      "plt.xlabel('my label', fontsize=20)\n",
      "plt.axis([0, 10000, 0.0001, 100])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 130,
       "text": [
        "[0, 10000, 0.0001, 100]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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wYVLHfYHIEcxfuFz4fV4exvT3oywYRADASIQHr12yPOdERLnH9qCwePFiLF68\nGD09PXjkkUeSDgrXhH5LUMYtFNxwA35hMh2FFPq5d/x4+Pv6HD8ddkKsV7cerylR0pIOCvX19Whu\nbsa4cePw3nvvaektLS1Yv349gsEgVq9ejQ0bNpge//jjj6OxsTHpjJVCmf/ID+C3Lhe+UlCg9CiK\noTw0H5Btj4+DVLCw7tN6vKZEyUs6KNTV1WHt2rVYtWqVlhYMBtHY2Ii9e/eiqKgIFRUVqK6uxqFD\nh3D48GE8+uijmDhxIjZu3IiqqiqUmy1kAqXg98b4XC8A3HADvKWlgEnvn8HCgoWIckHSQWHevHmQ\nDQ22Bw8eRElJidZPv6amBnv27MHGjRuxcuVKAMD3v/997Nu3D+fPn8fx48exZs0a0/NLJmnqkpsn\nLl2KO+mdxuHVBOwdMXR47XOAw///Z4q02hROnjyJ4uJibdvj8aC9vT1in3Xr1mHdunVxzyMjHBR8\nCE93URZKr+nvj31sYaGylGIm/Oe2uHcEC7oBYM+UrJftf/N2T2+hSisouFwuSzLhg/mYBDn0O9jf\nDzk0mrinoEBrPwAybNZTq7GgI8oZ6vQWKkdOc1FUVITu7m5tu7u7Gx6PZ8DnaUW4XUGGMr1FAcJr\nNEMIrT1BKi/nerpElLPsfmJwp3Pw7NmzcezYMciyjMuXL2Pnzp2orq4e8HlaoQQAKbTdh/gL5xAR\n5SqfzwdJkmw7f9JPCrW1tWhra8PZs2dRXFyMxx57DHV1ddi6dSsqKysRDAbR0NCAmTNnDjgTEsyn\nzjajzX6qyuXqIyLKOY6ZOnvHjh2m6VVVVaiqqko7I62hHy+U8Qmx1mH29vZG1KNLaX/yIGLviKHD\na09ZIiemzm6FEgy8oe1zMFlRLQtY/kTDgi5pfJqkbGH3k4JLCCFsO3syGXC5YMxADcKL6sjq+gW9\nvVrQkHT7+nXrM2tYpUREWc7lcsGO4tsRTwpG/VCqj67m5+Pme+9V+uLHWDDHWJ0E2FOlxDEBRJQL\nHBEUJEQ3NDdBeWKQtm2LbFgeKhwTQEQO4JiGZjtJhu0rod9XE+zXOWwYSoNBO7JERORIOdHQbJQP\nJQCMGD5cS5NM9qvRvWf2PhERDYwjgoIPkb2Pyow7hHrZyIFAxHoJpcEgAwIR5ZScqD5qNUmT9K9D\nDbmSz2dar69Niqdil0wiylI5WX0EKHMg9RQUJLWvdzDmQ+KYACLKAY4IChJMprkoLET53XcPQW7M\nsdspETlDN5rfAAARg0lEQVRBTlQfSYbfGllWuqNyLAAREYAcqT6S5s8HoDQkb1Mbknt7gVD7gaTu\nyCocIiJbOSMohB6FJJ9PCwSm+/FpgYjIVmmtp0BERNnFGU8KkhSxzBwREZnLiVlSNz3wACDLkW0K\nOtL8+VyCMwFO2EeUW7J7ltTQZHMSInsgyeq02GxITowT9hGRBZwRFEIk4/ZgDEojIiING5qJiEjD\noEBERBpHVB+1yjJaYZjmgoiIouTENBc+n08LDBHYwJw8jvYmygl2T3PhiC6pQ5wFIqKMY1fZyTYF\nIiLSMCgQEZHGEW0KA8GRu0RE9rE1KHR2duLZZ5/F2bNnUVlZiYaGhvRPmmDkLoMGEVHqbA0KpaWl\n+Ld/+zf09/ejpqbGmqAQIhm25UAAks8HubMT206fTrg/ERFFSyoo1NfXo7m5GePGjcN7772npbe0\ntGD9+vUIBoNYvXo1NmzYEHXsf/7nf+IHP/gBHnzwQetyHSLpN0KL8vgLC607P586iCjHJBUU6urq\nsHbtWqxatUpLCwaDaGxsxN69e1FUVISKigpUV1fj0KFDOHz4MB599FFMmjQJixYtwqJFi7B48WJ8\n85vftO2L2IKTzBFRjkkqKMybNw+y4Y754MGDKCkpgTc0OKqmpgZ79uzBxo0bsXLlSgBAW1sbdu3a\nhb6+Ptxxxx2WZpyIiKyXcpvCyZMnUVxcrG17PB60t7dH7DN//nzMD62/bJnQyF05EFCqjIiIyDIp\nBwWXy2VZJiRJ0l6rQ7hj7huqy4+1nnNPQQGk8vLoAzndAxFlMLvnPFKlHBSKiorQ3d2tbXd3d8Pj\n8aSckUTBIEqMuX7K2QhMRFlILSMdsxynLMtYtGiR1vvo6tWrmDFjBvbt24dJkybh61//Onbs2IGZ\nM2cOLAMOnvuIvY+IyKmGdDnO2tpatLW14ezZsyguLsZjjz2Guro6bN26FZWVlQgGg2hoaBhwQFBJ\nkjTwJ4VBwIKfiJzGMU8KtmXAwU8KREROldWzpEqSNCgNKEREma61tTWic47VMvZJ4d6SEow8cyYq\nvWfMGOw+ftyKrBEROdaQtik40cgzZ7DNZJyCf/CzQkSUNVh9RESUQVh9FIN/5EjzJ4XCQmzr6bEi\na0REjpXVDc1EROQMjggKrD4iIkoOq49iYO8jIspldlUfZWxQICLKZVndpsDqIyKi5LD6iIiIomT1\nkwIRETkDgwIREWkYFIiISOOIoMCGZiKi5LChmYiIorChmYiIbMegQEREGgYFIiLSMCgQEZGGQYGI\niDSOCArskkpElBx2SSUioijskkpERLZjUCAiIg2DAhERaWwPChcvXkRFRQWam5vt/igiIkqT7UHh\ne9/7HlasWGH3xxARkQWSCgr19fUYP348brnlloj0lpYWlJaWYtq0adiyZUvUcb/+9a9RVlaGsWPH\nWpPbLMduuWG8FmG8FmG8FvZLKijU1dWhpaUlIi0YDKKxsREtLS3o6OjAjh07cOTIEWzfvh0PP/ww\nTp06hba2Nhw4cACvvPIKfvzjH7PraQL8gw/jtQjjtQjjtbBfXjI7zZs3D7IsR6QdPHgQJSUl8Hq9\nAICamhrs2bMHGzduxMqVKwEAjz/+OADgpZdewtixY+FyuazLORERWS6poGDm5MmTKC4u1rY9Hg/a\n29tN933ggQdS/RgiIhpEKQcFK+/6+QQRtnnz5qHOgmPwWoTxWoTxWtgr5aBQVFSE7u5ubbu7uxse\nj2fA52E7AxGRc6TcJXX27Nk4duwYZFnG5cuXsXPnTlRXV1uZNyIiGmRJBYXa2lrMmTMHR48eRXFx\nMV588UXk5eVh69atqKysRFlZGVasWIGZM2fanV8iIrKTGEKvvvqqmDFjhigpKRFPPfXUUGbFFh9/\n/LHw+XyirKxM3HTTTeLZZ58VQghx9uxZceedd4pp06aJhQsXis8//1w75oknnhAlJSVixowZ4n/+\n53+09EOHDombb75ZlJSUiHXr1g36d7HK1atXRXl5ubjnnnuEELl7LT7//HOxdOlSUVpaKmbOnCkO\nHDiQs9fiiSeeEGVlZeLmm28WtbW1oq+vL2euRV1dnRg3bpy4+eabtTQrv3tfX59Yvny5KCkpEbfe\nequQZTlhnoYsKFy9elVMnTpVdHV1icuXL4tZs2aJjo6OocqOLX73u9+Jd955RwghxO9//3sxffp0\n0dHRIR599FGxZcsWIYQQTz31lNiwYYMQQogPPvhAzJo1S1y+fFl0dXWJqVOniv7+fiGEEBUVFaK9\nvV0IIURVVZV49dVXh+Abpe+f/umfxP333y8WLVokhBA5ey1WrVolfvKTnwghhLhy5Yro6enJyWvR\n1dUlpkyZIvr6+oQQQixfvlxs27YtZ67F66+/Lg4fPhwRFKz87v/6r/8qvv3tbwshhGhqahIrVqxI\nmKchCwpvvPGGqKys1LaffPJJ8eSTTw5VdgbF4sWLxa9//WsxY8YM8cknnwghlMAxY8YMIYRyF6B/\nYqqsrBRvvvmmOHXqlCgtLdXSd+zYIdasWTO4mbdAd3e3WLBggdi/f7/2pJCL16Knp0dMmTIlKj0X\nr8XZs2fF9OnTxblz58SVK1fEPffcI371q1/l1LXo6uqKCApWfvfKykpx4MABIYRy8zFmzJiE+Rmy\nWVLNxjmcPHlyqLJjO1mW8c477+DWW2/F6dOnMX78eADA+PHjcfr0aQDAqVOnInpwqdfEmF5UVJSR\n1+rhhx/G008/Dbc7/GeXi9eiq6sLY8eORV1dHf7oj/4IDz74IC5evJiT12LUqFH467/+a0yePBmT\nJk3CyJEjsXDhwpy8Fiorv7u+nM3Ly0NhYSHOnTsX9/OHLCjk0tiECxcuYOnSpXj22Wdx/fXXR7zn\ncrly4lr813/9F8aNG4evfe1rMbsh58q1uHr1Kg4fPoyHHnoIhw8fxnXXXYennnoqYp9cuRYffvgh\nnnnmGciyjFOnTuHChQt4+eWXI/bJlWthZii++5AFBavGOTjdlStXsHTpUqxcuRL33nsvACX6f/LJ\nJwCA3/3udxg3bhyA6Gty4sQJeDweFBUV4cSJExHpRUVFg/gt0vfGG2/gl7/8JaZMmYLa2lrs378f\nK1euzMlr4fF44PF4UFFRAQC47777cPjwYUyYMCHnrsWhQ4cwZ84cjB49Gnl5efjmN7+JN998Myev\nhcqK/xNqWVpUVISPP/4YgHIz0tvbi1GjRsX9/CELCrkwzkEIgYaGBpSVlWH9+vVaenV1NV566SUA\nyrxQarCorq5GU1MTLl++jK6uLhw7dgxf//rXMWHCBNxwww1ob2+HEALbt2/XjskUTzzxBLq7u9HV\n1YWmpiZ84xvfwPbt23PyWkyYMAHFxcU4evQoAGDv3r246aabsGjRopy7FqWlpThw4AD+8Ic/QAiB\nvXv3oqysLCevhcqK/xOLFy+OOtfPf/5zLFiwIHEGUm8eSd9///d/i+nTp4upU6eKJ554YiizYov/\n/d//FS6XS8yaNUuUl5eL8vJy8eqrr4qzZ8+KBQsWmHY5++53vyumTp0qZsyYIVpaWrR0tcvZ1KlT\nxdq1a4fi61imtbVV632Uq9ciEAiI2bNni69+9atiyZIloqenJ2evxZYtW7QuqatWrRKXL1/OmWtR\nU1MjJk6cKPLz84XH4xE//elPLf3ufX19YtmyZVqX1K6uroR5cgnBeSaIiEjBNZqJiEjDoEBERBoG\nBSIi0jAoEBGRhkGByKC1tRVutzvtxVy2bdsGt9utdQm0g1V5JVIxKBDFYNVI0sEYkZqrI37JegwK\nRESkYVAgIiINgwINGVmW4Xa7UVdXhw8//BD33XcfRo8ejRtuuAF33XUX3n//fQDAZ599htWrV2Pi\nxIm49tprUVFRgdbW1ohz/c3f/A3cbjf+/d//3fSz3n77bbjd7rSmUnn77bfxl3/5l5g1axZGjx6N\na6+9FtOnT8cjjzyCnp6emMcJIdDc3Iw5c+ZgxIgRGDVqFJYtW4bjx4+b7n/p0iU8+eSTKC8vx4gR\nI3D99ddjzpw5aGpqSjnvRMliUKAhJ8sybrvtNnz22Weor6/HXXfdhX379sHn86GzsxO33nor3nnn\nHdTW1mL58uX4v//7P1RVVUVMDvatb30LbrcbP/rRj0w/4/nnn9f2S9WPf/xj7Ny5EzNnzkR9fT0e\neughTJw4Ef/8z/+MuXPn4sKFC6bH7dq1C0uWLMHkyZOxfv16/PEf/zH+4z/+A7fddps2/5Gqp6cH\nt99+O/7u7/4O+fn5aGhogN/vx2effYb7778ff//3f59y/omSYt0sHkQD09XVJVwul3C5XFFzX33n\nO98RLpdLFBYWaitHqbZv3y5cLpd4+OGHI9Lvuece4XK5xPvvvx+Rfv78eTFixAjxla98RVupKp7X\nXntNuFwusXnz5oj03/72t6bH/+QnPxEul0tbLUv14osvat+vubk54r1nn31WuFwusWDBgoj0Bx54\nQLhcLvH0009HpPf19Ym7775buN1uEQgEEuaVKFV8UqAhN2XKFGzcuDEi7YEHHgAABINBPP300xHv\n3X///cjLy8O7774bkf7QQw8BCD8VqF555RVcvHgRq1evTquXzuTJk02Pr6urw/XXX49f/epXpsct\nWLAAf/qnfxqR1tjYiBtvvBH79+/XpjY+e/YsXn75ZVRUVOCRRx6J2P+aa67BU089BSEEXnnllZS/\nA1EieUOdAaLy8vKownbixIkAgOnTp+O6666LeM/tdmPcuHERc8gDwN13340pU6Zg+/bt2LJlC669\n9loAwI9+9CPk5+dj9erVaeXzypUreP7559HU1ISOjg6cP38e/f392vuxVvqaP39+VJrb7cbtt9+O\njz76CIFAAJMnT8Zbb72lnU+SJNPPB4AjR46k9T2I4mFQoCFXWFgYlZaXlxfzPfV9tZBUuVwurFmz\nBhs3bsTOnTvh9/vx9ttv45133sGSJUswYcKEtPK5YsUK7N69G1OnTtXOd80110AIgWeeeQZffPGF\n6XHq0opGan56e3sBKE8KAPDWW2/hrbfeMj3G5XLh4sWLaX0PongYFCirNDQ0YNOmTXj++efh9/u1\nqqQ1a9akdd5Dhw5h9+7dWLhwIV599dWIdaaFENiyZUvMY9U1do3U1bXUwKf+/qu/+iv84z/+Y1r5\nJUoV2xQoq4wePRrLli1De3s73njjDezYsQM33ngj7rrrrrTOq3Yfra6ujggIANDe3o6+vr6Yxxq7\nzwJKW8lvfvMbuFwufO1rXwMA3HrrrXC73Xj99dfTyitROhgUKOuoDc4rVqzAxYsX8eCDD6Z9zilT\npgAAXnvttYj0Tz/9FH/xF38R99j9+/ejubk5Im3r1q346KOPcMcdd6C4uBgAMHbsWPz5n/85Dh06\nhMcffzyivUL14YcfQpblNL4JUXysPqKsc9ttt2HWrFl499138aUvfQn19fVpn7OiogJz587Frl27\nMHfuXMydOxenT59GS0sLSktLMWnSJIgYixguWrQIS5YswZIlSzB16lQEAgG0tLRg9OjR+MEPfhCx\n79atW3Hs2DH8wz/8A7Zv3465c+di/PjxOHXqFI4cOYJDhw6hqakJXq837e9EZIZPCpSREnUtraur\nAwAsXrwYY8eOTfvz3G43fvnLX+Lb3/42Tp06heeeew5vvPEGHnzwQbS0tCA/Pz8qTy6XCy6XC0uX\nLsUvfvELdHd34/vf/z4OHDiApUuX4s0338T06dMjjrn++uvR1taG5557DmPGjMGuXbvwL//yL2hr\na0NhYSGeeeYZ3HnnnWl/H6JYuEYzZaVVq1bh5Zdfxr59+3DHHXcMdXaIMgaDAmWdjz/+GNOmTcO0\nadO0+ZOIKDlsU6Cs8corr+Do0aNoamrC1atX8Z3vfGeos0SUcfikQFnjjjvuwOuvv47Jkyfj4Ycf\nxrp164Y6S0QZh0GBiIg07H1EREQaBgUiItIwKBARkYZBgYiINAwKRESkYVAgIiLN/wdaZ2ZLwq/D\n1QAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10e313e10>"
       ]
      }
     ],
     "prompt_number": 130
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.loglog(base, log, 'rs')\n",
      "plt.xlabel('my label', fontsize=20)\n",
      "plt.axis([0, 10000, 0.0001, 100])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 131,
       "text": [
        "[0, 10000, 0.0001, 100]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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4GRQL6vCb5uCg77oYinyuGvcaho/Vgs62P7l/v+fmLz/F7TolXKLX1JoQ/twC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\nIBoQEv4EQRANCAl/giCIBuT/B+t9SEj1MfcjAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x116d7f290>"
       ]
      }
     ],
     "prompt_number": 131
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%R\tplot(\n",
      "%R\t\tDE$baseMean, \n",
      "%R\t\tDE$log2FoldChange,\n",
      "%R\t\tlog=\"x\", pch=20, cex=.3,\n",
      "%R\t\tcol = ifelse (DE$padj < .05, \"red\", \"black\"))\n",
      "\t\t\n",
      "%R plotDE(DE)\t\t"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "# Getting the data and setting up the conditional factor vector\n",
      "deseq.sums.countData_l <- sums.count.data[,c(1:6, 13:18)]\n",
      "deseq.sums.conds_l <- factor(c(rep(\"NPL\", 6), rep(\"PL\", 6)))\n",
      "# Creating the DESeq data object\n",
      "deseq.sums.cds_l <- newCountDataSet(countData = deseq.sums.countData_l, conditions = deseq.sums.conds_l)\n",
      "# Normalizing\n",
      "deseq.sums.cds_l <- estimateSizeFactors(deseq.sums.cds_l)\n",
      "# Doing the variance calculation\n",
      "deseq.sums.cds_l <- estimateDispersions(deseq.sums.cds_l)\n",
      "# Running the test and getting the results\n",
      "deseq.sums.res_l <- nbinomTest(deseq.sums.cds_l, \"NPL\", \"PL\")"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "IPlant"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Running the Tuxedo Suite- Two Trocophore and Two Umbo stage larvae libraries were compared. Modelled after this tutorial <http://www.iplantcollaborative.org/learning-center/discovery-environment/differential-expression-rna-seq-tuxedo-method>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import IFrame\n",
      "IFrame('http://www.iplantcollaborative.org/learning-center/discovery-environment/differential-expression-rna-seq-tuxedo-method', width=700, height=350)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "        <iframe\n",
        "            width=\"700\"\n",
        "            height=350\"\n",
        "            src=\"http://www.iplantcollaborative.org/learning-center/discovery-environment/differential-expression-rna-seq-tuxedo-method\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
        "        ></iframe>\n",
        "        "
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "<IPython.lib.display.IFrame at 0x1059d9450>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!ils"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/iplant/home/sr320:\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "  filtered_400_NoIndex_L006_R1.fastq.gz\r\n",
        "  Galaxy10-[Cut_on_data_7].tabular\r\n",
        "  Gil_1.fq.gz\r\n",
        "  Gil_2.fq.gz\r\n",
        "  oyster.v9.fa\r\n",
        "  oyster.v9.glean.final.rename.CDS.gff\r\n",
        "  solid0078_20091105_BB3.fastq\r\n",
        "  solid0078_20091105_DH3.fastq\r\n",
        "  SPID_and_GO_number.tabular\r\n",
        "  Transcriptome_module1_blastxout_CE_modified.txt\r\n",
        "  Unknown_Transcriptome_31545_contigs.fa\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "  C- /iplant/home/sr320/AltSplice\r\n",
        "  C- /iplant/home/sr320/analyses\r\n",
        "  C- /iplant/home/sr320/bamout\r\n",
        "  C- /iplant/home/sr320/BiGo_larvae\r\n",
        "  C- /iplant/home/sr320/bisque_data\r\n",
        "  C- /iplant/home/sr320/Black_Abalone\r\n",
        "  C- /iplant/home/sr320/cacheServiceTempDir\r\n",
        "  C- /iplant/home/sr320/ce\r\n",
        "  C- /iplant/home/sr320/Cgigas_v9\r\n",
        "  C- /iplant/home/sr320/coge_data\r\n",
        "  C- /iplant/home/sr320/fish546\r\n",
        "  C- /iplant/home/sr320/iplant_ws\r\n",
        "  C- /iplant/home/sr320/OlympiaOyster\r\n",
        "  C- /iplant/home/sr320/OlyO_PacBio\r\n",
        "  C- /iplant/home/sr320/OlyO_SNPhunt\r\n",
        "  C- /iplant/home/sr320/qdod\r\n",
        "  C- /iplant/home/sr320/RNAseq_co_BS\r\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!icd Cgigas_v9"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!ipwd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/iplant/home/sr320/Cgigas_v9\r\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!ils"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/iplant/home/sr320/Cgigas_v9:\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "  BiGillRNA_GACTAAGA_1.fastq\r\n",
        "  BiGillRNA_GACTAAGA_2.fastq\r\n",
        "  BiGoRNA_GTGTCTAC_1.fastq\r\n",
        "  BiGoRNA_GTGTCTAC_2.fastq\r\n",
        "  Crassostrea_gigas.GCA_000297895.1.21.dna_rm.toplevel.fa\r\n",
        "  Crassostrea_gigas.GCA_000297895.1.21.gff3\r\n",
        "  Crassostrea_gigas.GCA_000297895.1.21.gtf\r\n",
        "  oyster.v9.glean.final.rename.gff\r\n",
        "  T1.fq.gz\r\n",
        "  T2.fq.gz\r\n",
        "  U1.fq.gz\r\n",
        "  U2.fq.gz\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "  C- /iplant/home/sr320/Cgigas_v9/Cuffdiff2_analysis1-2014-02-06-10-06-21.400\r\n",
        "  C- /iplant/home/sr320/Cgigas_v9/Cufflinks2_analysis1-2014-02-06-07-11-47.565\r\n",
        "  C- /iplant/home/sr320/Cgigas_v9/Cuffmerge2_analysis1-2014-02-06-09-54-32.913\r\n",
        "  C- /iplant/home/sr320/Cgigas_v9/logs\r\n",
        "  C- /iplant/home/sr320/Cgigas_v9/TopHat2-PE_BiGill_BiGo-2014-02-02-09-12-25.697\r\n",
        "  C- /iplant/home/sr320/Cgigas_v9/TopHat2-PE_BiGill_BiGo-2014-02-02-09-12-31.220\r\n",
        "  C- /iplant/home/sr320/Cgigas_v9/TopHat2-SE_analysis1-2014-02-05-16-28-28.598\r\n",
        "  C- /iplant/home/sr320/Cgigas_v9/Uncompress_files_with_gunzip_analysis1-2014-02-05-15-59-31.208\r\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!iget -f -V -r TopHat2-SE_analysis1-2014-02-05-16-28-28.598 /Volumes/web/whale/fish546/iplant"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "NOTICE: irodsHost=data.iplantcollaborative.org\r\n",
        "NOTICE: irodsPort=1247\r\n",
        "NOTICE: irodsUserName=sr320\r\n",
        "NOTICE: irodsZone=iplant\r\n",
        "NOTICE: created irodsHome=/iplant/home/sr320\r\n",
        "NOTICE: created irodsCwd=/iplant/home/sr320\r\n",
        "NOTICE: irodsCwd=/iplant/home/sr320/Cgigas_v9\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "D- /Volumes/web/whale/fish546/iplant/TopHat2-SE_analysis1-2014-02-05-16-28-28.598 :\r\n"
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      {
       "output_type": "stream",
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       "text": [
        "D- /Volumes/web/whale/fish546/iplant/TopHat2-SE_analysis1-2014-02-05-16-28-28.598/bam :\r\n"
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       "output_type": "stream",
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       "output_type": "stream",
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      {
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       "output_type": "stream",
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       "output_type": "stream",
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       "stream": "stdout",
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       "text": [
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        "D- /Volumes/web/whale/fish546/iplant/TopHat2-SE_analysis1-2014-02-05-16-28-28.598/T2_out :\r\n"
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       "stream": "stdout",
       "text": [
        "   reports.samtools_sort.log       0.000 MB | 0.109 sec | 0 thr |  0.000 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   reports.samtools_sort.log       0.000 MB | 0.091 sec | 0 thr |  0.000 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   reports.samtools_sort.log       0.000 MB | 0.139 sec | 0 thr |  0.000 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   reports.samtools_sort.log       0.000 MB | 0.082 sec | 0 thr |  0.000 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   reports.samtools_sort.log       0.000 MB | 0.091 sec | 0 thr |  0.000 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   run.log                         0.013 MB | 0.090 sec | 0 thr |  0.141 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   segment_juncs.log               0.484 MB | 0.316 sec | 0 thr |  1.535 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   tophat.log                      0.003 MB | 0.100 sec | 0 thr |  0.035 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "From server: NumThreads=16, addr:128.196.172.248, port:20166, cookie=582043986\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   accepted_hits.bam             561.110 MB | 35.878 sec | 16 thr | 15.639 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   align_summary.txt               0.000 MB | 0.121 sec | 0 thr |  0.002 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   deletions.bed                   0.074 MB | 0.263 sec | 0 thr |  0.280 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   insertions.bed                  0.018 MB | 0.103 sec | 0 thr |  0.178 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   junctions.bed                   7.312 MB | 3.096 sec | 0 thr |  2.362 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   prep_reads.info                 0.000 MB | 0.080 sec | 0 thr |  0.001 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "From server: NumThreads=4, addr:128.196.172.248, port:20378, cookie=711356739\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   unmapped.bam                  100.407 MB | 14.381 sec | 4 thr |  6.982 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "D- /Volumes/web/whale/fish546/iplant/TopHat2-SE_analysis1-2014-02-05-16-28-28.598/U1_out :\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "D- /Volumes/web/whale/fish546/iplant/TopHat2-SE_analysis1-2014-02-05-16-28-28.598/U1_out/logs :\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   bam_merge_um.log                0.000 MB | 0.171 sec | 0 thr |  0.001 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   bowtie_build.log                0.012 MB | 0.128 sec | 0 thr |  0.092 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   bowtie.left_kept_reads.lo       0.000 MB | 0.094 sec | 0 thr |  0.002 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   bowtie.left_kept_reads.m2       0.000 MB | 0.093 sec | 0 thr |  0.002 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   bowtie.left_kept_reads.m2       0.000 MB | 0.087 sec | 0 thr |  0.002 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   bowtie.left_kept_reads.m2       0.000 MB | 0.089 sec | 0 thr |  0.002 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   g2f.err                         0.000 MB | 0.098 sec | 0 thr |  0.002 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   g2f.out                         0.000 MB | 0.088 sec | 0 thr |  0.001 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   gtf_juncs.log                   0.000 MB | 0.083 sec | 0 thr |  0.003 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   juncs_db.log                    0.190 MB | 0.334 sec | 0 thr |  0.569 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   long_spanning_reads.segs.       0.116 MB | 0.172 sec | 0 thr |  0.676 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   m2g_left_kept_reads.err         0.004 MB | 0.083 sec | 0 thr |  0.045 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   m2g_left_kept_reads.out         0.000 MB | 0.094 sec | 0 thr |  0.002 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   prep_reads.log                  0.000 MB | 0.207 sec | 0 thr |  0.001 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   reports.log                     0.190 MB | 0.185 sec | 0 thr |  1.025 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   reports.merge_bam.log           0.000 MB | 0.091 sec | 0 thr |  0.000 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   reports.samtools_sort.log       0.000 MB | 0.087 sec | 0 thr |  0.000 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   reports.samtools_sort.log       0.000 MB | 0.089 sec | 0 thr |  0.000 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   reports.samtools_sort.log       0.000 MB | 0.090 sec | 0 thr |  0.000 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   reports.samtools_sort.log       0.000 MB | 0.090 sec | 0 thr |  0.000 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   reports.samtools_sort.log       0.000 MB | 0.090 sec | 0 thr |  0.000 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   reports.samtools_sort.log       0.000 MB | 0.094 sec | 0 thr |  0.000 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   run.log                         0.013 MB | 0.096 sec | 0 thr |  0.133 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   segment_juncs.log               0.504 MB | 0.269 sec | 0 thr |  1.874 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   tophat.log                      0.003 MB | 0.155 sec | 0 thr |  0.022 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "From server: NumThreads=16, addr:128.196.172.248, port:20187, cookie=445338951\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   accepted_hits.bam            1085.350 MB | 148.014 sec | 16 thr |  7.333 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   align_summary.txt               0.000 MB | 0.182 sec | 0 thr |  0.001 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   deletions.bed                   0.071 MB | 0.419 sec | 0 thr |  0.168 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   insertions.bed                  0.030 MB | 0.140 sec | 0 thr |  0.211 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   junctions.bed                  10.573 MB | 5.249 sec | 0 thr |  2.014 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   prep_reads.info                 0.000 MB | 0.083 sec | 0 thr |  0.001 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "From server: NumThreads=7, addr:128.196.172.248, port:20398, cookie=869595600\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   unmapped.bam                  217.499 MB | 21.016 sec | 7 thr | 10.349 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "D- /Volumes/web/whale/fish546/iplant/TopHat2-SE_analysis1-2014-02-05-16-28-28.598/U2_out :\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "D- /Volumes/web/whale/fish546/iplant/TopHat2-SE_analysis1-2014-02-05-16-28-28.598/U2_out/logs :\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   bam_merge_um.log                0.000 MB | 0.093 sec | 0 thr |  0.002 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   bowtie_build.log                0.011 MB | 0.156 sec | 0 thr |  0.072 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   bowtie.left_kept_reads.lo       0.000 MB | 0.087 sec | 0 thr |  0.002 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   bowtie.left_kept_reads.m2       0.000 MB | 0.083 sec | 0 thr |  0.003 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   bowtie.left_kept_reads.m2       0.000 MB | 0.084 sec | 0 thr |  0.002 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   bowtie.left_kept_reads.m2       0.000 MB | 0.110 sec | 0 thr |  0.002 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   g2f.err                         0.000 MB | 0.080 sec | 0 thr |  0.002 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   g2f.out                         0.000 MB | 0.134 sec | 0 thr |  0.000 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   gtf_juncs.log                   0.000 MB | 0.073 sec | 0 thr |  0.004 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   juncs_db.log                    0.190 MB | 0.330 sec | 0 thr |  0.575 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   long_spanning_reads.segs.       0.086 MB | 0.133 sec | 0 thr |  0.643 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   m2g_left_kept_reads.err         0.002 MB | 0.100 sec | 0 thr |  0.023 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   m2g_left_kept_reads.out         0.000 MB | 0.089 sec | 0 thr |  0.002 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   prep_reads.log                  0.000 MB | 0.110 sec | 0 thr |  0.002 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   reports.log                     0.190 MB | 0.189 sec | 0 thr |  1.005 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   reports.merge_bam.log           0.000 MB | 0.075 sec | 0 thr |  0.000 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   reports.samtools_sort.log       0.000 MB | 0.077 sec | 0 thr |  0.000 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   reports.samtools_sort.log       0.000 MB | 0.078 sec | 0 thr |  0.000 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   reports.samtools_sort.log       0.000 MB | 0.097 sec | 0 thr |  0.000 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   reports.samtools_sort.log       0.000 MB | 0.083 sec | 0 thr |  0.000 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   reports.samtools_sort.log       0.000 MB | 0.089 sec | 0 thr |  0.000 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   reports.samtools_sort.log       0.000 MB | 0.084 sec | 0 thr |  0.000 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   run.log                         0.013 MB | 0.094 sec | 0 thr |  0.135 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   segment_juncs.log               0.505 MB | 0.421 sec | 0 thr |  1.201 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   tophat.log                      0.003 MB | 0.093 sec | 0 thr |  0.037 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "From server: NumThreads=16, addr:128.196.172.248, port:20056, cookie=1573138072\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   accepted_hits.bam            1016.442 MB | 129.945 sec | 16 thr |  7.822 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   align_summary.txt               0.000 MB | 0.224 sec | 0 thr |  0.001 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   deletions.bed                   0.049 MB | 0.215 sec | 0 thr |  0.226 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   insertions.bed                  0.016 MB | 0.171 sec | 0 thr |  0.094 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   junctions.bed                  10.519 MB | 4.404 sec | 0 thr |  2.389 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   prep_reads.info                 0.000 MB | 0.079 sec | 0 thr |  0.001 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "From server: NumThreads=10, addr:128.196.172.248, port:20231, cookie=640055026\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   unmapped.bam                  299.451 MB | 24.648 sec | 10 thr | 12.149 MB/s\r\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!iget -f -V -r Cufflinks2_analysis1-2014-02-06-07-11-47.565 /Volumes/web/whale/fish546/iplant"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "NOTICE: irodsHost=data.iplantcollaborative.org\r\n",
        "NOTICE: irodsPort=1247\r\n",
        "NOTICE: irodsUserName=sr320\r\n",
        "NOTICE: irodsZone=iplant\r\n",
        "NOTICE: created irodsHome=/iplant/home/sr320\r\n",
        "NOTICE: created irodsCwd=/iplant/home/sr320\r\n",
        "NOTICE: irodsCwd=/iplant/home/sr320/Cgigas_v9\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "D- /Volumes/web/whale/fish546/iplant/Cufflinks2_analysis1-2014-02-06-07-11-47.565 :\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "D- /Volumes/web/whale/fish546/iplant/Cufflinks2_analysis1-2014-02-06-07-11-47.565/gtf :\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "From server: NumThreads=2, addr:128.196.172.248, port:20052, cookie=1212704161\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   T1_transcripts.gtf             35.832 MB | 7.305 sec | 2 thr |  4.905 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "From server: NumThreads=2, addr:128.196.172.248, port:20283, cookie=180616609\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   T2_transcripts.gtf             33.549 MB | 6.560 sec | 2 thr |  5.114 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "From server: NumThreads=2, addr:128.196.172.248, port:20212, cookie=809785660\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   U1_transcripts.gtf             51.636 MB | 9.592 sec | 2 thr |  5.383 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "From server: NumThreads=2, addr:128.196.172.248, port:20354, cookie=776720173\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   U2_transcripts.gtf             51.753 MB | 9.405 sec | 2 thr |  5.503 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "D- /Volumes/web/whale/fish546/iplant/Cufflinks2_analysis1-2014-02-06-07-11-47.565/logs :\r\n"
       ]
      },
      {
       "output_type": "stream",
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       "output_type": "stream",
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        "NOTICE: irodsPort=1247\r\n",
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       "output_type": "stream",
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       "output_type": "stream",
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       "stream": "stdout",
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      },
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       "output_type": "stream",
       "stream": "stdout",
       "text": [
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       ]
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       "output_type": "stream",
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       "text": [
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   tss_group_exp.diff              5.279 MB | 3.740 sec | 0 thr |  1.411 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   tss_groups.count_tracking       2.620 MB | 2.040 sec | 0 thr |  1.284 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   tss_groups.fpkm_tracking        5.406 MB | 3.736 sec | 0 thr |  1.447 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   tss_groups.read_group_tra       8.435 MB | 5.813 sec | 0 thr |  1.451 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   var_model.info                  4.365 MB | 3.146 sec | 0 thr |  1.387 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "D- /Volumes/web/whale/fish546/iplant/Cuffdiff2_analysis1-2014-02-06-10-06-21.400/graphs :\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   density_plot.png                0.016 MB | 0.171 sec | 0 thr |  0.095 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   Troco_Umbo_scatter_plot.p       0.043 MB | 0.130 sec | 0 thr |  0.331 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   Troco_Umbo_volcano_plot.p       0.021 MB | 0.089 sec | 0 thr |  0.232 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "D- /Volumes/web/whale/fish546/iplant/Cuffdiff2_analysis1-2014-02-06-10-06-21.400/logs :\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   condor-0-input-0-stderr         0.000 MB | 0.161 sec | 0 thr |  0.000 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   condor-0-input-0-stdout         0.034 MB | 0.119 sec | 0 thr |  0.290 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   condor-0-input-1-stderr         0.000 MB | 0.089 sec | 0 thr |  0.000 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   condor-0-input-1-stdout         0.034 MB | 0.090 sec | 0 thr |  0.382 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   condor-0-input-2-stderr         0.000 MB | 0.088 sec | 0 thr |  0.000 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   condor-0-input-2-stdout         0.035 MB | 0.111 sec | 0 thr |  0.312 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   condor-0-input-3-stderr         0.000 MB | 0.086 sec | 0 thr |  0.000 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   condor-0-input-3-stdout         0.035 MB | 0.175 sec | 0 thr |  0.197 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   condor-0-input-4-stderr         0.000 MB | 0.087 sec | 0 thr |  0.000 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   condor-0-input-4-stdout         0.036 MB | 0.089 sec | 0 thr |  0.410 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   condor-stderr-0                 0.003 MB | 0.089 sec | 0 thr |  0.032 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   condor-stdout-0                 0.001 MB | 0.082 sec | 0 thr |  0.012 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   iplant.cmd                      0.001 MB | 0.090 sec | 0 thr |  0.007 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   iplant.sh                       0.004 MB | 0.102 sec | 0 thr |  0.041 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   output-last-stderr              0.000 MB | 0.084 sec | 0 thr |  0.000 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   output-last-stdout              1.599 MB | 1.225 sec | 0 thr |  1.305 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   script-condor-log               0.002 MB | 0.112 sec | 0 thr |  0.021 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   script-error.log                0.000 MB | 0.080 sec | 0 thr |  0.000 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   script-output.log               0.000 MB | 0.114 sec | 0 thr |  0.001 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "D- /Volumes/web/whale/fish546/iplant/Cuffdiff2_analysis1-2014-02-06-10-06-21.400/sorted_data :\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   genes.sorted_by_expressio       0.165 MB | 0.257 sec | 0 thr |  0.643 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   genes.sorted_by_expressio       1.015 MB | 0.833 sec | 0 thr |  1.218 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   genes.sorted_by_fold.sig.       0.165 MB | 0.213 sec | 0 thr |  0.774 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   genes.sorted_by_fold.txt        1.015 MB | 0.938 sec | 0 thr |  1.082 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   transcripts.sorted_by_exp       0.258 MB | 0.318 sec | 0 thr |  0.812 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   transcripts.sorted_by_exp       2.252 MB | 1.778 sec | 0 thr |  1.266 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   transcripts.sorted_by_fol       0.258 MB | 0.354 sec | 0 thr |  0.729 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   transcripts.sorted_by_fol       2.252 MB | 1.613 sec | 0 thr |  1.396 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   cuffdiff.stderr                 2.543 MB | 1.863 sec | 0 thr |  1.365 MB/s\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "From server: NumThreads=6, addr:128.196.172.248, port:20279, cookie=1191899484\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   rebuilt.gtf                   179.009 MB | 33.993 sec | 6 thr |  5.266 MB/s\r\n"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd /Volumes/web/whale/fish546/iplant/ "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/Volumes/web/whale/fish546/iplant\n"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ls"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[30m\u001b[43mCuffdiff2_analysis1-2014-02-06-10-06-21.400\u001b[m\u001b[m/\r\n",
        "\u001b[30m\u001b[43mCufflinks2_analysis1-2014-02-06-07-11-47.565\u001b[m\u001b[m/\r\n",
        "\u001b[30m\u001b[43mCuffmerge2_analysis1-2014-02-06-09-54-32.913\u001b[m\u001b[m/\r\n",
        "\u001b[30m\u001b[43mTopHat2-SE_analysis1-2014-02-05-16-28-28.598\u001b[m\u001b[m/\r\n"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd TopHat2-SE_analysis1-2014-02-05-16-28-28.598/"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/Volumes/web/whale/fish546/iplant/TopHat2-SE_analysis1-2014-02-05-16-28-28.598\n"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ls"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[30m\u001b[43mT1_out\u001b[m\u001b[m/ \u001b[30m\u001b[43mT2_out\u001b[m\u001b[m/ \u001b[30m\u001b[43mU1_out\u001b[m\u001b[m/ \u001b[30m\u001b[43mU2_out\u001b[m\u001b[m/ \u001b[30m\u001b[43mbam\u001b[m\u001b[m/    \u001b[30m\u001b[43mlogs\u001b[m\u001b[m/\r\n"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd Cufflinks2_analysis1-2014-02-06-07-11-47.565/"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/Volumes/web/whale/fish546/iplant/Cufflinks2_analysis1-2014-02-06-07-11-47.565\n"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ls"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[30m\u001b[43mT1_out\u001b[m\u001b[m/ \u001b[30m\u001b[43mT2_out\u001b[m\u001b[m/ \u001b[30m\u001b[43mU1_out\u001b[m\u001b[m/ \u001b[30m\u001b[43mU2_out\u001b[m\u001b[m/ \u001b[30m\u001b[43mgtf\u001b[m\u001b[m/    \u001b[30m\u001b[43mlogs\u001b[m\u001b[m/\r\n"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd Cuffmerge2_analysis1-2014-02-06-09-54-32.913/"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/Volumes/web/whale/fish546/iplant/Cuffmerge2_analysis1-2014-02-06-09-54-32.913\n"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ls"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Crassostrea_gigas.GCA_000297895.1.21.dna_rm.toplevel.fa.fai\r\n",
        "\u001b[30m\u001b[43mcuffmerge_out\u001b[m\u001b[m/\r\n",
        "\u001b[30m\u001b[43mlogs\u001b[m\u001b[m/\r\n"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd Cuffdiff2_analysis1-2014-02-06-10-06-21.400/"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/Volumes/web/whale/fish546/iplant/Cuffdiff2_analysis1-2014-02-06-10-06-21.400\n"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ls"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "cuffdiff.stderr  \u001b[30m\u001b[43mgraphs\u001b[m\u001b[m/          rebuilt.gtf\r\n",
        "\u001b[30m\u001b[43mcuffdiff_out\u001b[m\u001b[m/    \u001b[30m\u001b[43mlogs\u001b[m\u001b[m/            \u001b[30m\u001b[43msorted_data\u001b[m\u001b[m/\r\n"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd graphs/"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/Volumes/web/whale/fish546/iplant/Cuffdiff2_analysis1-2014-02-06-10-06-21.400/graphs\n"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ls"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Troco_Umbo_scatter_plot.png  density_plot.png\r\n",
        "Troco_Umbo_volcano_plot.png\r\n"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![xy](http://eagle.fish.washington.edu/whale/fish546/iplant/Cuffdiff2_analysis1-2014-02-06-10-06-21.400/graphs/Troco_Umbo_scatter_plot.png)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "All data <http://eagle.fish.washington.edu/whale/index.php?dir=fish546%2Fiplant%2F>"
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "!head /Volumes/web/whale/fish546/iplant/Cuffdiff2_analysis1-2014-02-06-10-06-21.400/sorted_data/genes.sorted_by_expression.sig.txt"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!sort -k8 -n /Volumes/web/whale/fish546/iplant/Cuffdiff2_analysis1-2014-02-06-10-06-21.400/sorted_data/genes.sorted_by_expression.sig.txt >/Volumes/web/cnidarian/fish546sort_gene.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!tail -100 /Volumes/web/cnidarian/fish546sort_gene.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "EKC37475\tCGI_10004362\tTroco\tUmbo\t14.84\tUP\t2.26\t0.0453296\t\r\n",
        "EKC39735\tCGI_10018153\tTroco\tUmbo\t2.44\tDOWN\t20.77\t0.0453296\t\r\n",
        "XLOC_008830\t-\tTroco\tUmbo\t6.27\tUP\t12.18\t0.0453296\t\r\n",
        "XLOC_019776\t-\tTroco\tUmbo\t2.10\tDOWN\t73.97\t0.0453296\t\r\n",
        "XLOC_036014\t-\tTroco\tUmbo\t10.79\tDOWN\t3.87\t0.0453296\t\r\n",
        "EKC38006\tCGI_10006955\tTroco\tUmbo\t3.40\tDOWN\t19.78\t0.0455229\t\r\n",
        "EKC39499\tCGI_10027904\tTroco\tUmbo\t4.51\tDOWN\t3.57\t0.0455229\t\r\n",
        "XLOC_028766\t-\tTroco\tUmbo\t2.17\tUP\t89.68\t0.0455229\t\r\n",
        "EKC20773\tCGI_10005392\tTroco\tUmbo\t3.65\tUP\t7.95\t0.0457067\t\r\n",
        "EKC30357\tCGI_10021831\tTroco\tUmbo\t2.72\tDOWN\t53.16\t0.0457067\t\r\n",
        "XLOC_008243\t-\tTroco\tUmbo\t2.33\tDOWN\t47.08\t0.0457067\t\r\n",
        "EKC20007\tCGI_10007068\tTroco\tUmbo\t21.26\tUP\t17.52\t0.0458809\t\r\n",
        "EKC22340\tCGI_10002457\tTroco\tUmbo\t2.17\tUP\t19.26\t0.0458809\t\r\n",
        "XLOC_016380\t-\tTroco\tUmbo\t5.37\tUP\t23.55\t0.0458809\t\r\n",
        "XLOC_029734\t-\tTroco\tUmbo\t3.07\tUP\t169.21\t0.0458809\t\r\n",
        "XLOC_033553\t-\tTroco\tUmbo\t2.93\tUP\t305.69\t0.0458809\t\r\n",
        "EKC19008\tCGI_10009987\tTroco\tUmbo\t4.07\tDOWN\t5.55\t0.0460453\t\r\n",
        "EKC28352\tCGI_10011576\tTroco\tUmbo\t2.09\tDOWN\t102.97\t0.0460453\t\r\n",
        "EKC30991\tCGI_10024990\tTroco\tUmbo\t2.69\tDOWN\t404.78\t0.0460453\t\r\n",
        "EKC36284\tCGI_10010991\tTroco\tUmbo\t6.61\tDOWN\t1.57\t0.0460453\t\r\n",
        "EKC37698\tCGI_10020451\tTroco\tUmbo\t5.62\tDOWN\t18.89\t0.0460453\t\r\n",
        "EKC28941\tCGI_10016445\tTroco\tUmbo\t4.33\tUP\t4.63\t0.0462281\t\r\n",
        "EKC36463\tCGI_10026996\tTroco\tUmbo\t3.58\tUP\t4.17\t0.0462281\t\r\n",
        "EKC39516\tCGI_10027926\tTroco\tUmbo\t6.64\tUP\t14.89\t0.0462281\t\r\n",
        "XLOC_000090\t-\tTroco\tUmbo\t2.01\tDOWN\t3655.26\t0.0462281\t\r\n",
        "EKC24470\tCGI_10019572\tTroco\tUmbo\t2.60\tDOWN\t7.05\t0.0464106\t\r\n",
        "XLOC_003648\t-\tTroco\tUmbo\t2.29\tUP\t58.71\t0.0464106\t\r\n",
        "XLOC_025872\t-\tTroco\tUmbo\t4.58\tDOWN\t7.88\t0.0464106\t\r\n",
        "XLOC_025928\tCGI_10010307\tTroco\tUmbo\t3.12\tDOWN\t57.19\t0.0464106\t\r\n",
        "EKC23378\tCGI_10012127\tTroco\tUmbo\t2.96\tDOWN\t244.69\t0.0465834\t\r\n",
        "EKC35004\tCGI_10023624\tTroco\tUmbo\t13.31\tUP\t3.17\t0.0465834\t\r\n",
        "XLOC_000133\t-\tTroco\tUmbo\t5.34\tDOWN\t841.51\t0.0465834\t\r\n",
        "XLOC_004846\tCGI_10004899\tTroco\tUmbo\t2.64\tUP\t8.34\t0.0465834\t\r\n",
        "XLOC_005195\t-\tTroco\tUmbo\t4.05\tUP\t42.18\t0.0465834\t\r\n",
        "XLOC_023099\t-\tTroco\tUmbo\t2.19\tDOWN\t90.02\t0.0467936\t\r\n",
        "EKC23538\tCGI_10007959\tTroco\tUmbo\t2.09\tDOWN\t51.83\t0.0469848\t\r\n",
        "EKC40818\tCGI_10026501\tTroco\tUmbo\t17.09\tUP\t3.20\t0.0469848\t\r\n",
        "XLOC_014332\t-\tTroco\tUmbo\t2.59\tUP\t14.38\t0.0469848\t\r\n",
        "EKC22776\tCGI_10001495\tTroco\tUmbo\t64.30\tDOWN\t18.74\t0.0471853\t\r\n",
        "EKC24585\tCGI_10024373\tTroco\tUmbo\t21.03\tDOWN\t13.52\t0.0471853\t\r\n",
        "EKC22688\tCGI_10001711\tTroco\tUmbo\t10.52\tUP\t14.58\t0.047376\t\r\n",
        "EKC35331\tCGI_10009319\tTroco\tUmbo\t8.81\tDOWN\t13.78\t0.047376\t\r\n",
        "EKC37827\tCGI_10017650\tTroco\tUmbo\t3.03\tDOWN\t49.91\t0.047376\t\r\n",
        "EKC32649\tCGI_10011464\tTroco\tUmbo\t13.03\tUP\t2.31\t0.0475377\t\r\n",
        "EKC40671\tCGI_10014094\tTroco\tUmbo\t13.22\tUP\t2.50\t0.0475377\t\r\n",
        "EKC42689\tCGI_10023910\tTroco\tUmbo\t13.66\tUP\t4.16\t0.0475377\t\r\n",
        "XLOC_001186\tCGI_10000991\tTroco\tUmbo\t3.80\tDOWN\t95.81\t0.0475377\t\r\n",
        "XLOC_004854\t-\tTroco\tUmbo\t3.19\tDOWN\t494.38\t0.0475377\t\r\n",
        "XLOC_031373\t-\tTroco\tUmbo\t13.67\tUP\t6.56\t0.0475377\t\r\n",
        "EKC19599\tCGI_10008154\tTroco\tUmbo\t2.77\tDOWN\t18.33\t0.0477086\t\r\n",
        "EKC20292\tCGI_10006339\tTroco\tUmbo\t5391.68\tUP\t1.01\t0.0477086\t\r\n",
        "EKC24893\tCGI_10021492\tTroco\tUmbo\t3.96\tDOWN\t7.22\t0.0477086\t\r\n",
        "EKC26706\tCGI_10028240\tTroco\tUmbo\t4.54\tUP\t2.42\t0.0477086\t\r\n",
        "EKC34691\tCGI_10026828\tTroco\tUmbo\t2.48\tUP\t14.28\t0.0477086\t\r\n",
        "EKC39440\tCGI_10020043\tTroco\tUmbo\t2.65\tUP\t2.88\t0.0479082\t\r\n",
        "EKC39718\tCGI_10018135\tTroco\tUmbo\t2.11\tUP\t43.18\t0.0479082\t\r\n",
        "EKC24748\tCGI_10016919\tTroco\tUmbo\t2.56\tDOWN\t41.02\t0.0480688\t\r\n",
        "EKC31094\tCGI_10028719\tTroco\tUmbo\t2.80\tUP\t94.15\t0.0480688\t\r\n",
        "EKC35815\tCGI_10019085\tTroco\tUmbo\t5.28\tDOWN\t15.91\t0.0480688\t\r\n",
        "EKC38040\tCGI_10007775\tTroco\tUmbo\t4.30\tUP\t2.77\t0.0480688\t\r\n",
        "EKC42277\tCGI_10017847\tTroco\tUmbo\t4.09\tUP\t12.63\t0.0480688\t\r\n",
        "XLOC_010164\t-\tTroco\tUmbo\t2.15\tDOWN\t54.85\t0.0480688\t\r\n",
        "EKC17945\tCGI_10000015\tTroco\tUmbo\t2.13\tDOWN\t450.32\t0.0482194\t\r\n",
        "EKC26388\tCGI_10025849\tTroco\tUmbo\t1.98\tDOWN\t1418.81\t0.0482194\t\r\n",
        "EKC30309\tCGI_10024543\tTroco\tUmbo\t5.79\tUP\t2.08\t0.0482194\t\r\n",
        "EKC31639\tCGI_10009151\tTroco\tUmbo\t8.18\tUP\t3.93\t0.0482194\t\r\n",
        "EKC32392\tCGI_10022269\tTroco\tUmbo\t3.80\tUP\t1.96\t0.0482194\t\r\n",
        "EKC36697\tCGI_10010964\tTroco\tUmbo\t2.16\tDOWN\t38.89\t0.0482194\t\r\n",
        "EKC41655\tCGI_10015739\tTroco\tUmbo\t2.83\tDOWN\t19.54\t0.0482194\t\r\n",
        "EKC19211\tCGI_10009331\tTroco\tUmbo\t7.09\tUP\t3.80\t0.0484084\t\r\n",
        "XLOC_030085\tCGI_10023149\tTroco\tUmbo\t2.18\tDOWN\t63.54\t0.0484084\t\r\n",
        "XLOC_034191\t-\tTroco\tUmbo\t15.88\tDOWN\t151.36\t0.0484084\t\r\n",
        "EKC23049\tCGI_10000765\tTroco\tUmbo\t51.81\tUP\t3.02\t0.0485777\t\r\n",
        "EKC24201\tCGI_10011140\tTroco\tUmbo\t16.59\tUP\t11.20\t0.0485777\t\r\n",
        "EKC38577\tCGI_10025930\tTroco\tUmbo\t10.45\tDOWN\t3.83\t0.0485777\t\r\n",
        "XLOC_006086\t-\tTroco\tUmbo\t5.11\tDOWN\t311.86\t0.0485777\t\r\n",
        "XLOC_013300\tCGI_10011136\tTroco\tUmbo\t9.23\tUP\t3.76\t0.0485777\t\r\n",
        "EKC42205\tCGI_10028002\tTroco\tUmbo\t2.91\tDOWN\t20.39\t0.0487857\t\r\n",
        "EKC36733\tCGI_10024965\tTroco\tUmbo\t2.74\tUP\t5.98\t0.0489839\t\r\n",
        "XLOC_006644\t-\tTroco\tUmbo\t10.24\tDOWN\t44.54\t0.0489839\t\r\n",
        "EKC27759\tCGI_10015674\tTroco\tUmbo\t8.21\tUP\t2.12\t0.049172\t\r\n",
        "XLOC_014884\t-\tTroco\tUmbo\t6.49\tUP\t7.11\t0.049172\t\r\n",
        "EKC22964\tCGI_10001026\tTroco\tUmbo\t110.46\tUP\t26.53\t0.0493302\t\r\n",
        "EKC23640\tCGI_10009021\tTroco\tUmbo\t205.22\tUP\t48.28\t0.0493302\t\r\n",
        "EKC26438\tCGI_10016881\tTroco\tUmbo\t140.92\tUP\t54.66\t0.0493302\t\r\n",
        "XLOC_011538\t-\tTroco\tUmbo\t19.76\tDOWN\t17404.94\t0.0493302\t\r\n",
        "XLOC_013596\t-\tTroco\tUmbo\t67.10\tUP\t97.19\t0.0493302\t\r\n",
        "EKC26111\tCGI_10010243\tTroco\tUmbo\t2.10\tDOWN\t39.36\t0.0495079\t\r\n",
        "EKC29657\tCGI_10026039\tTroco\tUmbo\t4.69\tUP\t24.15\t0.0495079\t\r\n",
        "EKC30968\tCGI_10019769\tTroco\tUmbo\t2.62\tDOWN\t15.17\t0.0495079\t\r\n",
        "XLOC_015656\tCGI_10028852\tTroco\tUmbo\t2.89\tDOWN\t34.63\t0.0495079\t\r\n",
        "EKC35109\tCGI_10015126\tTroco\tUmbo\t2.21\tUP\t18.44\t0.0496952\t\r\n",
        "EKC35839\tCGI_10019251\tTroco\tUmbo\t9.81\tDOWN\t2.33\t0.0496952\t\r\n",
        "XLOC_030804\tCGI_10010458\tTroco\tUmbo\t5.40\tUP\t22.03\t0.0496952\t\r\n",
        "EKC18589\tCGI_10011745\tTroco\tUmbo\t12.30\tUP\t2.92\t0.0498525\t\r\n",
        "EKC22366\tCGI_10002407\tTroco\tUmbo\t1.98\tDOWN\t38.02\t0.0498525\t\r\n",
        "EKC26781\tCGI_10013211\tTroco\tUmbo\t12.60\tUP\t22.29\t0.0498525\t\r\n",
        "EKC30487\tCGI_10000421\tTroco\tUmbo\t2.80\tDOWN\t111.80\t0.0498525\t\r\n",
        "EKC31928\tCGI_10012144\tTroco\tUmbo\t2.02\tDOWN\t30.63\t0.0498525\t\r\n",
        "XLOC_000878\t-\tTroco\tUmbo\t12.54\tUP\t7.81\t0.0498525\t\r\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!python /Users/sr320/sqlshare-pythonclient/tools/singleupload.py -d fish546_DEgene_out /Volumes/web/cnidarian/fish546sort_gene.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "processing chunk line 0 to 2988 (0.50559592247 s elapsed)\r\n",
        "pushing /Volumes/web/cnidarian/fish546sort_gene.txt...\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "parsing 25D12DC3...\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "finished fish546_DEgene_out\r\n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd /Volumes/web/cnidarian/"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/Volumes/web/cnidarian\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!python /Users/sr320/sqlshare-pythonclient/tools/fetchdata.py -s \"SELECT * FROM [sr320@washington.edu].[fish546_DEgene_out]deg left join [sr320@washington.edu].[qDOD Cgigas Gene Descriptions (Swiss-prot)]sp on deg.gene_name = sp.CGI_ID\" -f tsv -o fish546_DEGjoinSP.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!head fish546_DEGjoinSP.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "gene_id\tgene_name\tsample1\tsample2\tfold_change\tdirection\ttotal_fpkm\tq-value\tgene_description\tCGI_ID\tSPID\tevalue\tDescription\r",
        "\r\n",
        "XLOC_000003\t-\tTroco\tUmbo\t3.98\tDOWN\t535.89\t0.00168522\t\t\t\t\t\r",
        "\r\n",
        "XLOC_000004\t-\tTroco\tUmbo\t3.11\tUP\t883.48\t0.0125195\t\t\t\t\t\r",
        "\r\n",
        "XLOC_000014\t-\tTroco\tUmbo\t3.27\tUP\t102.63\t0.0303472\t\t\t\t\t\r",
        "\r\n",
        "XLOC_000018\t-\tTroco\tUmbo\t11.28\tUP\t86.26\t0.0153821\t\t\t\t\t\r",
        "\r\n",
        "XLOC_000022\t-\tTroco\tUmbo\t4.2\tDOWN\t105.07\t0.00764577\t\t\t\t\t\r",
        "\r\n",
        "XLOC_000028\t-\tTroco\tUmbo\t2.97\tUP\t310.33\t0.00398356\t\t\t\t\t\r",
        "\r\n",
        "XLOC_000051\t-\tTroco\tUmbo\t2.98\tDOWN\t277.81\t0.0125195\t\t\t\t\t\r",
        "\r\n",
        "XLOC_000060\t-\tTroco\tUmbo\t3.35\tDOWN\t129.55\t0.0399222\t\t\t\t\t\r",
        "\r\n",
        "XLOC_000085\t-\tTroco\tUmbo\t2.52\tDOWN\t128.84\t0.0105174\t\t\t\t\t\r",
        "\r\n"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!awk '{ print $10 }' fish546_DEGjoinSP.txt > fish546_DEGjoinSPonly.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!cat fish546_DEGjoinSPonly.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CGI_ID\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
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        "\r\n",
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        "\r\n",
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        "\r\n",
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        "\r\n",
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    {
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     "input": [],
     "language": "python",
     "metadata": {},
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   "metadata": {}
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 ]
}