ExampleSeqFigure.json

{
 "metadata": {
  "name": "ExampleSeqFigure"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Example Sequence Figure"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is an example implemntation of a visualization of biological sequence data. The goal is to be able to visualize whether patients grouped by other variables (in the same `Glue` dataset) have common differences in their sequence.\n",
      "\n",
      "I'm assumming that the sequence input will be `pre-aligned`. Implementing call-outs to alignment programs is not difficult but outside the scope of a `Glue` visualization. I'm using DNA sequence here but its trivial to extend to AAs."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In the example data there is a G->C transition at position 10 between the two groups."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "example_seqs = ['AAAATTTTGGGGCCCC',\n",
      "                'AAAATTTTGGCGCCCC',\n",
      "                '---ATTTTGGCGCCCC',\n",
      "                'AGAATTTTGGCGCCCC',\n",
      "                'AGAATTTTGGCGCCCC',\n",
      "                'AGAATTTTGGGGCCCC',\n",
      "                'AGAATTTTGGGGCCCC',\n",
      "                'AAAATTTTGGGGC---']\n",
      "pat_groups = np.asarray([0, 1, 1, 1, 1, 0, 0, 0])\n",
      "viz_order = '-ACGT'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "seq_mat = np.array([list(s) for s in example_seqs])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "num_mat = np.searchsorted(np.asarray(list(viz_order)), \n",
      "                          seq_mat.flatten()).reshape(seq_mat.shape)\n",
      "num_mat"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "array([[1, 1, 1, 1, 4, 4, 4, 4, 3, 3, 3, 3, 2, 2, 2, 2],\n",
        "       [1, 1, 1, 1, 4, 4, 4, 4, 3, 3, 2, 3, 2, 2, 2, 2],\n",
        "       [0, 0, 0, 1, 4, 4, 4, 4, 3, 3, 2, 3, 2, 2, 2, 2],\n",
        "       [1, 3, 1, 1, 4, 4, 4, 4, 3, 3, 2, 3, 2, 2, 2, 2],\n",
        "       [1, 3, 1, 1, 4, 4, 4, 4, 3, 3, 2, 3, 2, 2, 2, 2],\n",
        "       [1, 3, 1, 1, 4, 4, 4, 4, 3, 3, 3, 3, 2, 2, 2, 2],\n",
        "       [1, 3, 1, 1, 4, 4, 4, 4, 3, 3, 3, 3, 2, 2, 2, 2],\n",
        "       [1, 1, 1, 1, 4, 4, 4, 4, 3, 3, 3, 3, 2, 0, 0, 0]])"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def bin_cols(indata):\n",
      "    tdata = []\n",
      "    for num in range(5):\n",
      "        tdata.append((indata == num).sum(axis=0))\n",
      "    return np.concatenate(tdata)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "XX, YY = np.meshgrid(np.arange(num_mat.shape[1]), np.arange(5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(1,1, figsize=(10,5))\n",
      "ax.set_yticks([0, 1, 2, 3, 4])\n",
      "ax.set_yticklabels(viz_order)\n",
      "ax.set_xlim([-0.5, 15.5])\n",
      "ax.set_title('All Patient groups')\n",
      "sizes = bin_cols(num_mat)\n",
      "ax.scatter(XX.flatten(), YY.flatten(), s=100*sizes, alpha=0.5)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<matplotlib.collections.PathCollection at 0x3189610>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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aitLS0oCujVg4IgpEUlIc6uvbonY/QWhDXFz7B9Gq1XFwu9ugVMZHpRZJakNc3I3TVUql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\n1uY5AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x307e8d0>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(1,1, figsize=(10,5))\n",
      "ax.set_yticks([0, 1, 2, 3, 4])\n",
      "ax.set_yticklabels(viz_order)\n",
      "ax.set_xlim([-0.5, 15.5])\n",
      "ax.set_title('All Patient groups')\n",
      "\n",
      "for group, color in [(0, 'y'), (1, 'r')]:\n",
      "    sizes = bin_cols(num_mat[pat_groups==group])\n",
      "    ax.scatter(XX.flatten(), YY.flatten(), s=100*sizes.flatten(), alpha=0.8, c=color)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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2sA0P09PT2e61qPCFppY1po3Tzj4noLYtWrRg2zZvyPad277dScuWLQNqe845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\nf/XVV2/8OzExEYmJiTb38/b2RqMgwCqKUHZSShq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       "text": [
        "<matplotlib.figure.Figure at 0x3190f50>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here its easy to pick out the C->G transition between the two groups."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    }
   ],
   "metadata": {}
  }
 ]
}