ipynb-lamanno2016-speciescomparison.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": "true"
   },
   "source": [
    "# Table of Contents\n",
    " <p><div class=\"lev1\"><a href=\"#Import-libraries\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Import libraries</a></div><div class=\"lev1\"><a href=\"#Load-data-and-annotations\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Load data and annotations</a></div><div class=\"lev2\"><a href=\"#Load-Homologene-mappings\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Load Homologene mappings</a></div><div class=\"lev2\"><a href=\"#Load-cell-type-pattern-and-median-tables\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Load cell type pattern and median tables</a></div><div class=\"lev2\"><a href=\"#Restrict-the-genes-to-the-one-that-have-a-clear-homologue\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>Restrict the genes to the one that have a clear homologue</a></div><div class=\"lev1\"><a href=\"#Filters\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Filters</a></div><div class=\"lev2\"><a href=\"#Filter-the-binary-patterns-so-that-the-distance-is-useful\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>Filter the binary patterns so that the distance is useful</a></div><div class=\"lev1\"><a href=\"#Calculate-correlation\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Calculate correlation</a></div><div class=\"lev1\"><a href=\"#Visualize-by-heat-map\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Visualize by heat map</a></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "code_folding": [],
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "#imports\n",
    "import pandas as pd\n",
    "import cPickle as pickle\n",
    "from scipy.spatial.distance import cdist, pdist, squareform"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data and annotations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Homologene mappings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "mouse2human_dict = pickle.load(open('data/Homologene_mouse2human_dict.pickle'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load cell type pattern and median tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "pattern_df = pd.read_csv('data/Human_binary.tsv',sep='\\t', index_col=0)\n",
    "pattern_df_m = pd.read_csv('data/Mouse_binary.tsv',sep='\\t', index_col=0)\n",
    "median_df = pd.read_csv('data/Human_medians.tsv',sep='\\t', index_col=0)\n",
    "median_df_m = pd.read_csv('data/Mouse_medians.tsv',sep='\\t', index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Restrict the genes to the one that have a clear homologue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "# For easier handling convert the mouse name in the human name\n",
    "mouse_translable = [i for i in pattern_df_m.index if i in mouse2human_dict]\n",
    "pattern_df_m = pattern_df_m.ix[mouse_translable,:]\n",
    "median_df_m = median_df_m.ix[mouse_translable,:]\n",
    "pattern_df_m.index = [mouse2human_dict[i] for i in pattern_df_m.index]\n",
    "median_df_m.index = [mouse2human_dict[i] for i in median_df_m.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "# Take genes that have an homologue\n",
    "common_genes = intersect1d(pattern_df_m.index, pattern_df.index)\n",
    "pattern_df_m = pattern_df_m.ix[common_genes, :]\n",
    "pattern_df = pattern_df.ix[common_genes, :]\n",
    "median_df_m = median_df_m.ix[common_genes, :]\n",
    "median_df = median_df.ix[common_genes, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filter the binary patterns so that the distance is useful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13100 are not significant as basal in human\n",
      "12969 are not significant as basal in mouse\n",
      "2805 are significant in at least one cell type in both mouse and human\n",
      "13044 are significant in less than 6 cell types type in either mouse or human\n",
      "5998 max median is more than 1.5 in either mouse or human\n",
      "10261 min median is less than 0.25 in either mouse or human\n",
      "1405 satisfy all the conditions above\n"
     ]
    }
   ],
   "source": [
    "# Exclude house-keeping and noisy genes genes\n",
    "bool1 =  (pattern_df.ix[:,0] == 0)\n",
    "print '%s are not significant as basal in human' % sum(bool1)\n",
    "bool2 =  (pattern_df_m.ix[:,0] == 0)\n",
    "print '%s are not significant as basal in mouse' % sum(bool2)\n",
    "bool3 = (pattern_df.ix[:,1:].sum(1) > 0) & (pattern_df_m.ix[:,1:].sum(1) > 0) \n",
    "print '%s are significant in at least one cell type in both mouse and human' % sum(bool3)\n",
    "bool4 = (pattern_df.ix[:,1:].sum(1) < 6) | (pattern_df_m.ix[:,1:].sum(1) < 6) \n",
    "print '%s are significant in less than 6 cell types type in either mouse or human' % sum(bool4)\n",
    "\n",
    "# Exclude gene whose signal-to-noise ration is low\n",
    "bool5 = (median_df.ix[:,1:].max(1) > 1.5) | (median_df_m.ix[:,1:].max(1) > 1.5) \n",
    "print '%s max median is more than 1.5 in either mouse or human' % sum(bool5)\n",
    "bool6 = (median_df.ix[:,1:].min(1) < 0.25) | (median_df_m.ix[:,1:].min(1) < 0.25) \n",
    "print '%s min median is less than 0.25 in either mouse or human' % sum(bool6)\n",
    "\n",
    "selected = (bool1 & bool2 & bool3 & bool4 & bool5 & bool6) \n",
    "print '%s satisfy all the conditions above' % sum(selected)\n",
    "\n",
    "#Apply the filter\n",
    "pattern_df_m = pattern_df_m.ix[selected,:]\n",
    "pattern_df = pattern_df.ix[selected,:]\n",
    "median_df_m = median_df_m.ix[selected,:]\n",
    "median_df = median_df.ix[selected,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "# These values were calculated in ipynb-lamanno2016-bayesGLM.ipynb here, for simplicity is just loaded\n",
    "total_relative = array([1.   ,  1.257,  1.076,  0.973,  1.001, 1.085,  0.868,  1.237,  1.255,\n",
    "                        1.056, 1.003,  0.887,  1.269,  0.868,  0.712, 0.847,  0.871,  0.788,\n",
    "                        0.738,  0.641, 1.078,  0.826,  0.816,  0.703,  1.423,1.228])\n",
    "total_relative__m = array([ 1.,  1.164,  0.852,  1.203,  0.935, 0.941,  1.019,  0.972, 0.927,  0.6496,\n",
    "                           0.629,  0.908,  0.645,  0.692,  0.9322, 0.838,  0.781, 1.205,  1.025,\n",
    "                             1.3181, 1.402,  0.875,  0.873,1.615,  1.3315, 1.450,  0.664])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "# Normalize\n",
    "median_df_m = (median_df_m/total_relative__m)\n",
    "median_df = (median_df/ total_relative)\n",
    "median_df_logn = log2(median_df+1).subtract( log2(median_df+1).mean(1), 'rows' )\n",
    "median_df_m_logn = log2(median_df_m+1).subtract( log2(median_df_m+1).mean(1), 'rows' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "# Calculate Correlation\n",
    "metric = 'correlation'\n",
    "D = 1-cdist(median_df_m.ix[:,1:].T, median_df.ix[:,1:].T, metric) # this is faster and equivalent at np.corrcoeff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize by heat map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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MasI1E1s2pQ9m1e0djulNzMlJI/BlFRPX1PJFeHtgrvClmBLyDqYUX4C5gT7A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\ngnYveX0H6+lYcC5FAT9XEjjU2cLigqMcD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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113961110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the code below just takes D and plots it\n",
    "rcParams['pdf.fonttype'] = 42\n",
    "figure(figsize=(5,5))\n",
    "\n",
    "hh = 0.02\n",
    "ww = 0.065\n",
    "color_name = 'RdYlBu_r'\n",
    "for i, ct_x in enumerate( median_df_logn.columns[1:] ):\n",
    "    for j, ct_y in enumerate( median_df_m_logn.columns[1:] ):\n",
    "        colorvalue =   clip( (D[j,i] + 0.425) / 0.85 ,0,1)\n",
    "        colormap = plt.cm.get_cmap(color_name)\n",
    "        edc = '0.02'\n",
    "        lww = 0.4\n",
    "        zzor = -1\n",
    "        gca().add_patch(Rectangle((i*ww,j*hh),0.98*ww,0.99*hh,ec=edc,lw=lww,\\\n",
    "                                  fc=colormap(colorvalue),clip_on=0, zorder=zzor))\n",
    "        \n",
    "        # It adds horizontal and vertical lines to the max horizontal of row- and columns-wise respectivelly\n",
    "        if D[j,i]==max(D[j,:]):\n",
    "            gca().add_patch(Rectangle((i*ww,(j+0.485)*hh),0.98*ww,0.03*hh,ec=edc,lw=1,\\\n",
    "                                  fc='none',clip_on=0, zorder=1000))\n",
    "        if D[j,i]==max(D[:,i]):\n",
    "            gca().add_patch(Rectangle(((i+0.485)*ww,j*hh),0.03*ww,0.99*hh,ec=edc,lw=1,\\\n",
    "                                  fc='none',clip_on=0, zorder=1000))   \n",
    "\n",
    "for j, ct_y in enumerate( median_df_m.columns[1:] ):\n",
    "    gca().text(-ww+0.05,hh*(j+0.5), ct_y,\\\n",
    "               fontdict={'fontsize':14}, va='center', ha='right')\n",
    "for i, ct_x in enumerate( median_df_logn.columns[1:]):\n",
    "    gca().text(ww*(i+0.5)-0.03,hh*len(median_df_m.columns[1:])+0.01,ct_x,\\\n",
    "               fontdict={'fontsize':15}, va='bottom', ha='left',rotation=75)\n",
    "axis('off')\n",
    "ylim(0.,0.35)\n",
    "tight_layout()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  },
  "toc": {
   "toc_cell": true,
   "toc_number_sections": true,
   "toc_section_display": "none",
   "toc_threshold": 6,
   "toc_window_display": true
  },
  "toc_position": {
   "height": "306px",
   "left": "1131.015625px",
   "right": "20px",
   "top": "113px",
   "width": "189px"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}