KaiSmith/genda - Some Basic Analytic Tools (Hardy-Weinberg, Hierarchical Clustering) Secret

## genda - Some Basic Analytic Tools (Hardy-Weinberg, Hierarchical Clustering)
{
 "metadata": {
  "name": "genda - Basic Analytic Tools"
 },
 "nbformat": 2,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from genda.formats import panVCF"
     ],
     "language": "python",
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "v = panVCF.VCF('tests/data/chr22.test.vcf')",
      "v.geno.ix[0:10,0:5]"
     ],
     "language": "python",
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">",
        "<table border=\"1\" class=\"dataframe\">",
        "  <thead>",
        "    <tr style=\"text-align: right;\">",
        "      <th></th>",
        "      <th>HG00096</th>",
        "      <th>HG00097</th>",
        "      <th>HG00099</th>",
        "      <th>HG00100</th>",
        "      <th>HG00101</th>",
        "    </tr>",
        "  </thead>",
        "  <tbody>",
        "    <tr>",
        "      <th>rs149201999</th>",
        "      <td> 0</td>",
        "      <td> 1</td>",
        "      <td> 1</td>",
        "      <td> 0</td>",
        "      <td> 1</td>",
        "    </tr>",
        "    <tr>",
        "      <th>rs146752890</th>",
        "      <td> 1</td>",
        "      <td> 1</td>",
        "      <td> 1</td>",
        "      <td> 0</td>",
        "      <td> 1</td>",
        "    </tr>",
        "    <tr>",
        "      <th>rs139377059</th>",
        "      <td> 0</td>",
        "      <td> 1</td>",
        "      <td> 1</td>",
        "      <td> 0</td>",
        "      <td> 1</td>",
        "    </tr>",
        "    <tr>",
        "      <th>rs188945759</th>",
        "      <td> 0</td>",
        "      <td> 0</td>",
        "      <td> 0</td>",
        "      <td> 0</td>",
        "      <td> 0</td>",
        "    </tr>",
        "    <tr>",
        "      <th>rs6518357</th>",
        "      <td> 0</td>",
        "      <td> 1</td>",
        "      <td> 1</td>",
        "      <td> 0</td>",
        "      <td> 1</td>",
        "    </tr>",
        "    <tr>",
        "      <th>rs62224609</th>",
        "      <td> 0</td>",
        "      <td> 1</td>",
        "      <td> 1</td>",
        "      <td> 0</td>",
        "      <td> 0</td>",
        "    </tr>",
        "    <tr>",
        "      <th>rs62224610</th>",
        "      <td> 0</td>",
        "      <td> 1</td>",
        "      <td> 1</td>",
        "      <td> 1</td>",
        "      <td> 1</td>",
        "    </tr>",
        "    <tr>",
        "      <th>rs143503259</th>",
        "      <td> 0</td>",
        "      <td> 1</td>",
        "      <td> 1</td>",
        "      <td> 0</td>",
        "      <td> 0</td>",
        "    </tr>",
        "    <tr>",
        "      <th>rs192339082</th>",
        "      <td> 0</td>",
        "      <td> 0</td>",
        "      <td> 0</td>",
        "      <td> 0</td>",
        "      <td> 0</td>",
        "    </tr>",
        "    <tr>",
        "      <th>rs79725552</th>",
        "      <td> 0</td>",
        "      <td> 1</td>",
        "      <td> 1</td>",
        "      <td> 0</td>",
        "      <td> 1</td>",
        "    </tr>",
        "  </tbody>",
        "</table>",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "             HG00096  HG00097  HG00099  HG00100  HG00101",
        "rs149201999        0        1        1        0        1",
        "rs146752890        1        1        1        0        1",
        "rs139377059        0        1        1        0        1",
        "rs188945759        0        0        0        0        0",
        "rs6518357          0        1        1        0        1",
        "rs62224609         0        1        1        0        0",
        "rs62224610         0        1        1        1        1",
        "rs143503259        0        1        1        0        0",
        "rs192339082        0        0        0        0        0",
        "rs79725552         0        1        1        0        1"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Can pick any SNP from the dat and easily see if it is in Hardy-Weinberg Equilibrium",
      "v.hardyweinberg('rs146752890')"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "False"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "v.hardyweinberg('rs149201999')"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Construct a dendrogram out of the data for heirarchical clustering",
      "v.dendrogram()"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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cOy5O3aTrTkwmVfWj13fO8e3tVY/aaFTD3hMTYeLEs+ms2bPVDd2nn1Y98W/f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\n9Ck2m40vX77Q29uLUqkU+j1x4gQlJSUMDw9js9l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      }
     ],
     "prompt_number": 5
    }
   ]
  }
 ]
}