examples/f2.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:1e0570f026e0f6683d8d7ada5bec4ada8ae94a9263b5668ea1812c72af519073"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "[``anhima.f2``](http://anhima.readthedocs.org/en/latest/f2.html) - Doubleton sharing"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from __future__ import division, print_function\n",
      "import numpy as np\n",
      "np.random.seed(1)\n",
      "import scipy.stats\n",
      "import random\n",
      "random.seed(1)\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline\n",
      "import sys\n",
      "import anhima\n",
      "# # dev imports\n",
      "# sys.path.insert(0, '../src')\n",
      "# %reload_ext autoreload\n",
      "# %autoreload 1\n",
      "# %aimport anhima.sim\n",
      "# %aimport anhima.gt\n",
      "# %aimport anhima.af\n",
      "# %aimport anhima.f2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# simulate genotypes \n",
      "n_variants = 10000\n",
      "n_samples = 100\n",
      "ploidy = 2\n",
      "af_dist = scipy.stats.beta(a=.1, b=6)\n",
      "p_missing = 0\n",
      "genotypes = anhima.sim.simulate_biallelic_genotypes(n_variants, n_samples, af_dist, p_missing, ploidy)\n",
      "\n",
      "# simulate three sub-populations with relatedness\n",
      "pop1_genotypes = anhima.sim.simulate_relatedness(genotypes[:, :n_samples//3], relatedness=.5, n_iter=100)\n",
      "pop23_genotypes = anhima.sim.simulate_relatedness(genotypes[:, n_samples//3:], relatedness=.5, n_iter=100)\n",
      "pop2_genotypes = anhima.sim.simulate_relatedness(pop23_genotypes[:, n_samples//3:], relatedness=.5, n_iter=20)\n",
      "pop3_genotypes = anhima.sim.simulate_relatedness(pop23_genotypes[:, :n_samples//3], relatedness=.5, n_iter=20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pop1_ac = anhima.af.allele_count(pop1_genotypes, allele=1)\n",
      "pop2_ac = anhima.af.allele_count(pop2_genotypes, allele=1)\n",
      "pop3_ac = anhima.af.allele_count(pop3_genotypes, allele=1)\n",
      "subpops_ac = np.column_stack([pop1_ac, pop2_ac, pop3_ac])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# raw counts of shared doubletons\n",
      "counts = anhima.f2.count_shared_doubletons(subpops_ac)\n",
      "counts"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "array([[63, 10, 20],\n",
        "       [10, 44, 76],\n",
        "       [20, 76, 89]])"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "subpop_labels = ['pop1', 'pop2', 'pop3']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "anhima.f2.plot_shared_doubletons(counts, \n",
      "                                 subpop_labels=subpop_labels);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAANEAAADQCAYAAACZZoRKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xFFFFFFFFF>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "anhima.f2.plot_shared_doubletons(counts, \n",
      "                                 subpop_labels=subpop_labels,\n",
      "                                 flip=True);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAANEAAADQCAYAAACZZoRKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ntXHKkSRJkiRJktQi/wePEhsBc3VGLwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xFFFFFFFFF>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "anhima.f2.plot_shared_doubletons(counts, \n",
      "                                 subpop_labels=subpop_labels,\n",
      "                                 relative=True);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAANEAAADQCAYAAACZZoRKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xFFFFFFFFF>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n_samples = [pop1_genotypes.shape[1], pop2_genotypes.shape[1], pop3_genotypes.shape[1]]\n",
      "n_samples"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "[33, 34, 33]"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# normalise doubleton counts by the number of distinct pairs of haplotypes in each comparison\n",
      "counts_normed = anhima.f2.normalise_doubleton_counts(counts, n_samples=n_samples, ploidy=2)\n",
      "counts_normed"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "array([[ 0.02937063,  0.00222816,  0.00459137],\n",
        "       [ 0.00222816,  0.01931519,  0.01693405],\n",
        "       [ 0.00459137,  0.01693405,  0.04149184]])"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "anhima.f2.plot_shared_doubletons(counts_normed, \n",
      "                                 subpop_labels=subpop_labels);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAANEAAADQCAYAAACZZoRKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nHVOQziy4uLRxKZAkSZIkSZLUIv8DI0cZxtDeHK0AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xFFFFFFFFF>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "anhima.f2.plot_total_doubletons(counts, subpop_labels=subpop_labels);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAAEICAYAAAB7+s71AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nE5gBAAAAAAAAgNXuv7+2VpaFcQGuAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xFFFFFFFFF>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "anhima.f2.plot_total_doubletons(counts, subpop_labels=subpop_labels, orientation='horizontal');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAD/CAYAAAD7X81yAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xFFFFFFFFF>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "anhima.f2.plot_f2_fig(counts, subpop_labels=subpop_labels, figsize_factor=1.5);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAEkCAYAAADKE0EUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAADv1JREFUeJzt3X+MZeVdx/H3wS0BKVN2q8ZfXUeWYkri4kJqwMqmAYox\nVWNDA+nKShFjuyVNWkgj1sQ5E5NaFoskhUptgKyuBVxsrMamqYZ2Q91lAXc7iyBUwKGrNRF0dvc2\nKXanHP94zmTODHPvnLlz5p57v/N+JZN79jnn3vu9z8x+5pnnnvsckCRJkiRJkiRJkiRJkiRJkiRJ\ngqfaLmCEbAYeBL4OfBx4Q2Xf37RS0ei6EPhHUn/+NPBV4ATwKHBei3VpSG1ou4Ahd/USbQWQAT82\n4FpG2X3Aw8Ah4EZgP/BrwCvAT7VY1yi6B/gE8EbgAHAz8BDwbuAzwFXtlaZhlLVdwJA7BXweeG1R\newa8l/QfTcubIo1A51xHGtH/Kin8t7VR1Ig6wnx/Pc/C0Xt1n6QaDgM/22XfsUEWMuKeBs5Y1HYl\nKaT+a/DljLSjle0PLdr3L4MsRKPhB9ouYMj9K/DfpDnPxb4OfHuw5YysM0g/a9OVthdJ88gXAn/R\nQk2jqgCeA74HPFFpfyvwQ8CX2yhKkiRpaG0B/o70RuHLwBeBc1utaDTZj82xL6UGHQJ2kk79ewPp\njcNDrVY0muzH5tiXUoOOLtE2NfAqRp/92Bz7UrX4xms948Dbgf8AzgE+UG4fBs4EvttaZaNlHPux\nKePYl6rB8+TrmSad1bCUAudC65rGfmzKNPalJEnrmyP5ek4HdgHbSaOk/aSPl59qs6gRZD82x75U\nLYZ8PfeS1vnZQ+qzncAs8NttFjWC7Mfm2Je95UAH+NQK7/d+4GLgw0vs+w69lzJ5E7AD+NMVPqeG\nwFJnMizVpt7sx+bYl71NALf0cb/rgU932ddZ5r7jDOHqtKe1XcCImGXhQlBbyjatjP3YHPvy9X6f\ntOTDo8DPlG0/BzxGOr30C6QzkQC+RhqxQ1oO4t/L7Qx4C2kJ528Cf9DluT4GPF4+bl62fZL0fTgC\n3Fa23U4K/qPANWXbO8vn30daOmVv5XE/SVrraaq8rwbkCuBbpG/MfuAl4PI2CxpR9mNz7MuFLiYF\n6RnA2cC/kUbyU8Bl5TGTwJ+U218FLiq3qyH/ftKaVBvLx3qqctzcSP4q4LPl9mmkTx5fRlo2uzqS\nvxr4CukXx4+Qvkc/Sgr548CPl/sOAO8A3gw8W7n/WP2X350j+XoOAH9GWnL4f0jf4AOtVjSa7Mfm\n2JcLXUYaqb9KCuO/Bc4ijdwfLY/ZQ3qjejlfAWbKx/oC878k5lxVfh0B/pn0V8N5vP49zneQliov\nSAsd7id9tqEg/RXw7XL7G6RfEMfL57wXeA8NfdbBkK/nz0lX4flD4C7SOciunLhy9mNz7MuF5i7m\n00t1/yzz+bd4GezF91l8PQmAPyKt3b8NOB+4v8ZzztUJ8H+Vtu+Tlqb4PvDzpGss/AquKDpQz9Rs\nU2/2Y3Psy4W2kaZm5qZrvkmarvkG8IvlMTnzZ9t8Dvhguf0RFk7X/CdpuubM8jEXT9e8izTPf1b5\n758Afpg03TJdqek9pKA+rdw/TZq2eSdpimfOp0lv+J5V7od0ps4rdV74crz8Xz2HgUuBg+W/LyH9\nmaaVsR+bY18udIR0GcQp0tTI46RR8/Wkzw/8IPACcEN5/B8DfwX8DvD3zI+w56ZS/hr4SdJfR4cr\n+wD+AXgb833/HeA3SL8o/ok0L/8l4HdJ36Op8r4fK2t7G6//tHJB+uX0RdIvqgz4aF89sYjnydfz\nLOlPsmOkb8Zm0rv4s+W/t7ZX2kixH5tjX6oWQ76e8WX2Tw+ghgjGl9k/PYAaohhfZv/0AGqQJEmS\nJEmStDJDNSdfFEW39bGlVmwaG2Oms9ySJaqpr7yZmJgo8jxvuJRYsizr2reGvNRDlmVdr8yhlcn6\nzxujYRm9Qt5PvEpSYIa8JAVmyEtSYIa8JAVmyEtSYIa8JAXmKpSSht7k5GTbJQyNiYmJFR3vSF6S\nAnMkH9TYOWN0TvhJTWm9M+SD6pzozF9DXv3L2y5AWh2nayQpMENekgIz5CUpMENekgIz5CUpMENe\nkgIz5CUpMENekgIz5CUpMENekgIz5CUpMENekgIz5CUpMENekgIz5CUpMENekgIz5CUpMENekgIz\n5CUpMENekgIbSMgX8MECjhZwpICDBVw4iOeVpPVuUCP5v8xgawbbgE8AnxrQ80rSutYz5AsYL+DZ\nAvYW8EwB+wo4s4ArCjhcjs7vLeD08vjpAm4r2w8VsAUgg07lYd8IvLJ2L0mSNKfOSP584O4MLgBO\nArcA9wPXZLAV2ADsKo8tgONl+13Anczv+FABzwN3AL/X3EuQJHVTJ+SPZXCw3N4LXA68mKXABtgD\nbK8c/0B5+yBw6VxjBp/J4DzgZuC+VVUtSaqlTsgXle0MOF7eVtsKlrZU+0PARbWqkyStSp2Q31zA\nJeX2DuBJ0lz9lrJtJ7C/cvy1ldsDAAW8tbL/3cDRviuWJNW2ocYxzwE3FWmK5WnSnPpjpDdhNwCP\nA/dUjt9YwBTwKvC+su2mAq4ETgEvAzc0VL8kqYc6IT+bpdF61SN0n3LZncGt1YYMPtJPcZKk1Vnp\nnHyTx0qS1ljPkXwG06TTIWvJ4NzVFiRJao5r10hSYIa8JAVmyEtSYIa8JAVmyEtSYIa8JAVmyEtS\nYIa8JAVmyEtSYIa8JAVmyEtSYIa8JAVmyEtSYIa8JAVW56IhAzM2tolOZ6btMiQpjKEK+RTwXnek\nGdnyh0gKz+kaSQrMkJekwAx5SQrMkJekwAx5SQrMkJekwAx5SQrMkJekwAx5SQrMkJekwAx5SQrM\nkJekwAx5SQrMkJekwAx5SQpsqNaTByYhm2i7iDDytguIwZX5G5PjT+XA+fMradgVReHFhHrJsqxr\nljtdI0mBGfKSFJghL0mBGfKSFJghL0mBGfKSFJghL0mBGfKSFJghL0mBGfKSFJghL0mBGfKSFNhQ\nrULpKkRSXL0W0dLacSQvSYEZ8pIUmCEvSYEN1Zy8JC1lcnKy7RL6NjHR7sXuHMlLUmCGvCQFZshL\nUmCGvCQFZshLUmCGvCQF5imUUg+bxsaY6XTaLkPqmyEv9TDT6eCCSs1w4Zp2OF0jSYEZ8pIUmCEv\nSYEZ8pIUmCEvSYEZ8pIUmCEvSYEZ8pIUmCEvSYEZ8pIUmCEvSYEZ8pIUmCEvSYEZ8pIUmCEvSYEZ\n8pIUmCEvSYEZ8pIUmCEvSYEZ8pIU2GAu5J1zM3AjMAu8DPwWOd8ayHNL0jo2qJH8YeBici4EHgZ2\nD+h5JWld6z2SzxkHvgw8CVwEPA38JvALwO3l/Z8AdpHzPXKmgYeAXwa+C+wg5wVyvlZ51EPAdU2+\nCEnS0uqM5M8H7ibnAuAkcAtwP3ANOVtJQb+rPLYAjpftdwF3LvF4NwJfWm3hkqTl1Qn5Y+QcLLf3\nApcDL5LzfNm2B9heOf6B8vZB4NIFj5RzHekvgtv7LViSVF+dN16LynYGHAfevKitYGnz7TlXAh8H\ntpNzamVlaqXGzhmjc6LTdhmSWlYn5DeTcwk5jwE7SPPzHyBnCzkvADuB/ZXjrwVuK28PAJCzDbgH\n+CVyXmmwfnXROdGBvO0qAsjbLkBanToh/xxwEzn3kd54vQN4DNhHzgbgcVKAz9lIzhTwKvC+sm03\ncBbwcPmf5iVyfr2JFyBJ6q5OyM+Ss3NR2yOkufWl7Cbn1gUtOe/qozZJ0irVeeO123z7ao+VJK2x\n5c6Tnwa21n60nHNXV44kqUmuXSNJgRnykhSYIS9JgRnykhSYIS9JgRnykhSYIS9JgRnykhSYIS9J\ngRnykhSYIS9JgRnykhSYIS9JgRnykhSYIS9JgRnykhSYIS9JgRnykhSYIS9JgRnykhSYIS9JgRny\nkhTYhrYLqBob20SnM9N2GZIUxlCFfAr4ou0ygsjaLkDSEHC6RpICM+QlKTBDXpICG7aJ28I5+aZk\nkLddQwC5P5FNyfrPm6Io/C70kmVZ1751JC9JgRnykhTYsE3X5MBE20VIWhOT9DeJ6HTNMnpN1wxb\nyEvSYob8MpyTl6R1ypCXpMAMeUkKzJCXpMAMeUkKzJCXpMAMeUkKzJCXpMAMeUkKzJCXpMAMeUkK\nbKiu8SpJS5mcnGy7BCYmRnPtREfykhTYUI3kXWpOiqvXSolaO47kJSkwQ16SAjPkJSkwQ16SAjPk\nJSkwQ16SAjPkJSkwQ16SAjPkJSkwQ16SAjPkJSkwQ16SAjPkJSkwQ16SAjPkJSkwQ16SAjPkJSkw\nQ16SAjPkJSkwQ16SAjPkJSmwDW0XUDU2tolOZ6btMkI4++yNnDz5v22XIallQxXyKeCLtssIodPJ\n2i5B0hBwukaSAjPkJSkwQ16SAjPkJSkwQ16SAjPkJSkwQ16SAjPkJSmwAYV8sR2Kw1CcguLqwTyn\nJGlQI/mXgOuBzw/o+SRJLBvyxTgUz0KxF4pnoNgHxZlQXFGOzI9CcS8Up5fHT0NxW9l+CIotqT17\nCbKngNfW8LVIkhapM5I/H7gbsguAk8AtwP3ANZBtJa1/s6s8tgCOl+13AXc2X7Ikqa46IX8MsoPl\n9l7gcuBFyJ4v2/YA2yvHP1DePghc2kiVkqS+1An56rKQGXC8vK22dVs6cql2l5mUpAGpE/Kbobik\n3N4BPAmMz8+3sxPYXzn+2srtgUWPlbHwF4QkaQ3VCfnngJvSG6+8CbgDuAHYl95gZRa4p3L8Riim\ngA8DH01NxduhOAa8F/gsFE819gokSV3VuWjILGQ7F7U9AlzU5fjdkN26sCl7AnjLiquTJK3KSufk\nmzxWkrTGlhnJZ9PA1voPl527mmIkSc1y7RpJCsyQl6TADHlJCsyQl6TADHlJCsyQl6TADHlJCsyQ\nl6TADHlJCsyQl6TADHlJCsyQl6TADHlJCsyQl6TA6lw0RFq3No2NMdPptF2G1DdDXuphptPxSjgN\n8eLO7XC6RpICM+QlKTBDXpICM+QlKTBDXpICM+QlKbBhO6upwBPWGpJRFPblamVZ5k9kQ7L+86bw\nZ7m3LMu69q3nyUd1WgooSeubIR/Va0DedhEB5G0XIK2Oc/KSFJghL0mBGfKSFJghL0mBGfKSFJgh\nL0kamJz0aSi//PIr3ldOf/q9nyRJkiRJkiRJkiRJkiT18P8ACLvukcSXEAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xFFFFFFFFF>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "anhima.f2.plot_f2_fig(counts, subpop_labels=subpop_labels, figsize_factor=1.5, relative=True);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAEkCAYAAADKE0EUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xFFFFFFFFF>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "anhima.f2.plot_f2_fig(counts, \n",
      "                      subpop_labels=subpop_labels, \n",
      "                      figsize_factor=1.5, \n",
      "                      relative=True, \n",
      "                      normed=True, \n",
      "                      n_samples=n_samples, \n",
      "                      ploidy=2);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAEkCAYAAADKE0EUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xFFFFFFFFF>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    }
   ],
   "metadata": {}
  }
 ]
}