notebooks/Mouse_Gene_Phenotypes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "from ontobio.ontol_factory import OntologyFactory\n",
    "\n",
    "# Create ontology object, for mammalian phenotype ontology\n",
    "# Transparently uses remote SPARQL service.\n",
    "# (May take a few seconds to run first time, Jupyter will show '*'. BE PATIENT, do\n",
    "# not re-execute cell)\n",
    "ofactory = OntologyFactory()\n",
    "ont = ofactory.create('mp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from ontobio.assoc_factory import AssociationSetFactory\n",
    "\n",
    "MOUSE = 'NCBITaxon:10090'\n",
    "\n",
    "# Create association set\n",
    "# Transparently uses remote Monarch service.\n",
    "# (May take a few seconds to run first time, Jupyter will show '*'. BE PATIENT, do\n",
    "# not re-execute cell)\n",
    "afactory = AssociationSetFactory()\n",
    "aset = afactory.create(ontology=ont,\n",
    "                       subject_category='gene',\n",
    "                       object_category='phenotype',\n",
    "                       taxon=MOUSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "[p1] = ont.search(\"abnormal hippocampus morphology\")\n",
    "[p2] = ont.search(\"abnormal glucose homeostasis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "genes = aset.query([p1, p2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MGI:88227',\n",
       " 'MGI:107164',\n",
       " 'MGI:102548',\n",
       " 'MGI:1926884',\n",
       " 'MGI:88145',\n",
       " 'MGI:97362',\n",
       " 'MGI:98331',\n",
       " 'MGI:102709',\n",
       " 'MGI:109583',\n",
       " 'MGI:103555',\n",
       " 'MGI:2145264',\n",
       " 'MGI:1929213']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"MGI:88227 'C3'\",\n",
       " \"MGI:107164 'Ppp3ca'\",\n",
       " \"MGI:102548 'Tsc2'\",\n",
       " \"MGI:1926884 'Huwe1'\",\n",
       " \"MGI:88145 'Bdnf'\",\n",
       " \"MGI:97362 'Nos3'\",\n",
       " \"MGI:98331 'Snap25'\",\n",
       " \"MGI:102709 'Cav1'\",\n",
       " \"MGI:109583 'Pten'\",\n",
       " \"MGI:103555 'Clcn3'\",\n",
       " \"MGI:2145264 'Nhlrc1'\",\n",
       " \"MGI:1929213 'Zbtb20'\"]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[\"{} '{}'\".format(g, aset.label(g)) for g in genes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "z, xlabels, ylabels = aset.similarity_matrix(genes, genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trace = go.Heatmap(z=-np.array(z),\n",
    "                       x=xlabels,\n",
    "                       y=ylabels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~cmungall/2.embed\" height=\"525px\" width=\"100%\"></iframe>"
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      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
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     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "py.iplot([trace], filename='labelled-heatmap')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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