scikit-allel-with-bcbio-syn3-dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## scikit-allel with syn3 dataset\n",
    "\n",
    "[`scikit-allel`](http://scikit-allel.readthedocs.org/en/latest/) is a Python library for exploratory analysis of large-scale genome variation data.\n",
    "\n",
    "The syn3 VCF dataset comes from the ICGC-TCGA DREAM challenge processed via the bcbio cancer pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/romanvg/.anaconda/envs/py3/lib/python3.4/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import allel\n",
    "import matplotlib.pyplot as plt\n",
    "import h5py\n",
    "import seaborn as sns\n",
    "import vcfnp\n",
    "sns.set_style('white')\n",
    "sns.set_style('ticks')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[vcfnp] 2015-11-29 15:07:53.998383 :: caching is enabled\n",
      "[vcfnp] 2015-11-29 15:07:53.999642 :: cache file available\n",
      "[vcfnp] 2015-11-29 15:07:54.000092 :: loading from cache file syn3-ensemble.vcf.gz.vcfnp_cache/variants.npy\n"
     ]
    }
   ],
   "source": [
    "syn3_vcf = 'syn3-ensemble.vcf.gz'\n",
    "v = vcfnp.variants(syn3_vcf, cache=True).view(np.recarray)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# pick a chromosome to work with\n",
    "chrom = '6'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SortedIndex((1141,), dtype=int32)\n",
       "[    49147    334919    658019 ..., 241622499 241712027 242524186]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "syn3_pos = allel.SortedIndex(sorted(v['POS'][:]))\n",
    "syn3_pos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the density of these variants over the chromosome."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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QK0ljgAKX7lds8L4YzeUHbtqVpr2Z6vPLnPDFtSgvQFZ/nmQd3vxBg3Y2nyF/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\nMAqKKR+GYRiGYRSU/w9mTpV+k39GpgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107d7b780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bin_width = 100000\n",
    "bins = np.arange(0, syn3_pos.max(), bin_width)\n",
    "\n",
    "# set X coordinate as bin midpoints\n",
    "x = (bins[1:] + bins[:-1])/2\n",
    "\n",
    "# compute variant density\n",
    "h, _ = np.histogram(syn3_pos, bins=bins)\n",
    "y = h / bin_width\n",
    "\n",
    "# plot\n",
    "fig, ax = plt.subplots(figsize=(8, 2))\n",
    "sns.despine(ax=ax, offset=5)\n",
    "ax.plot(x, y)\n",
    "ax.set_xlabel('position (bp)')\n",
    "ax.set_ylabel('density (bp$^{-1}$)')\n",
    "ax.set_title('Variant density');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say I have a gene of interest. I know what position it starts and ends, and I want to find variants within the gene."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "slice(34, 35, None)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start, stop = 2358158, 2431617\n",
    "loc = syn3_pos.locate_range(start, stop)\n",
    "loc"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}