Genes_to_Parlces_Communities.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Map Genes to Parcels & Communities in Schaefer\n",
    "#### Cedric Huchuan Xia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import necessary modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import subprocess\n",
    "import numpy as np\n",
    "import nibabel as nib\n",
    "from nilearn import plotting\n",
    "import pandas\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set up files paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up the working directory\n",
    "# All paths after this are relative path to the root of chead\n",
    "\n",
    "wk_dir = '/Users/hxia/Desktop/BBL/' # this is where I have mounted chead to on my laptop. \n",
    "# Change the above to an empty string if working directly on chead \n",
    "\n",
    "# Set up the project directory\n",
    "project_path = os.path.join(wk_dir,'data/joy/BBL/projects/prsConnectivity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parcellation\n",
    "schaefer_path = os.path.join(wk_dir,'data/joy/BBL/studies/pnc/template/Schaefer/MNI')\n",
    "parcel_filename = os.path.join(schaefer_path, 'Schaefer2018_100Parcels_7Networks_order_FSLMNI152_1mm.nii.gz')\n",
    "community_filename = os.path.join(schaefer_path,'Schaefer2018_100Parcels_7Networks_order.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Genes\n",
    "ABI_path = os.path.join(wk_dir,project_path,'ABI')\n",
    "donor='10021'\n",
    "gene_filename = os.path.join(ABI_path,'normalized_microarray_donor'+donor,'SampleAnnot.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c10442d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Parcellation\n",
    "img = nib.load(parcel_filename)\n",
    "img_data = img.get_data()\n",
    "plotting.plot_img(img,draw_cross=False,title='Schaefer 100 Parcels',colorbar=True)\n",
    "\n",
    "# Community Assignment\n",
    "community = pandas.read_csv(community_filename,delim_whitespace=True,header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Genes\n",
    "gene = pandas.io.parsers.read_csv(gene_filename,delimiter=',')\n",
    "gene_mni_coord = gene.iloc[:,-3:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Parse the Community File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "community_assignment ={parcel[0]: parcel[1].split('_')[2] \n",
    "                       for idx, parcel in community.iterrows()}\n",
    "community_assignment[0] = 'NotAssigned'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Map MNI to Image Coordiantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initiate an empty dataframe of nrow = num gene, ncol = 3 for x y z coordinates\n",
    "gene_vox_coord = pandas.DataFrame(np.zeros(gene_mni_coord.shape))\n",
    "gene_vox_coord.columns = ['img_x','img_y','img_z']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loop through each gene coordinate in MNI space\n",
    "# and then apply FSL std2imgcoord to transform the points\n",
    "# to the image voxel coordiates\n",
    "\n",
    "for ridx, row in gene_mni_coord.iterrows():\n",
    "    coord_cmd = \"echo %f %f %f|std2imgcoord -img %s -std %s -vox \" \\\n",
    "    % (row[0],row[1],row[2],parcel_filename,parcel_filename) # a shell command to apply FSL std2imgcoord\n",
    "    vox_coord_str = subprocess.check_output(coord_cmd, shell = True) # get the output from the shell\n",
    "    vox_coord_str_list = vox_coord_str.split()\n",
    "    for cidx, coord in enumerate(vox_coord_str_list): \n",
    "        vox_coord_float = float(coord)\n",
    "        gene_vox_coord.iloc[ridx][cidx] = vox_coord_float\n",
    "gene_vox_coord_rd = np.round(gene_vox_coord).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loop up parcel and community assignment for each gene\n",
    "gene_parcel_assignment ={idx: img_data[tuple(gene)].astype(int)\n",
    "                       for idx, gene in gene_vox_coord_rd.iterrows()}\n",
    "gene_community_assignment = {idx : community_assignment[gene_parcel]\n",
    "                            for idx, gene_parcel in gene_parcel_assignment.iteritems()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concat a master loopup table of gene assignment\n",
    "gene_assignment = pandas.DataFrame([gene_parcel_assignment, gene_community_assignment]).T\n",
    "gene_assignment.columns = ['d{}'.format(i) for i, col in enumerate(gene_assignment, 1)]\n",
    "gene_assignment.columns = ['parcel','community']\n",
    "gene_assignment = pandas.concat([gene_mni_coord,gene_vox_coord,gene_assignment],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save the Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "variables_filename = os.path.join(project_path,'ABI/gene_mapping','%sdonor_%sParcels_%sNetwork_%smm.pkl' \\\n",
    "                                      % (donor,100,7,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(variables_filename, 'w') as f:\n",
    "    pickle.dump([gene_mni_coord, gene_vox_coord, gene_vox_coord_rd,\n",
    "                 gene_parcel_assignment,gene_community_assignment,gene_assignment],f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "#write gene_assignment to a csv\n",
    "csv_filename = os.path.join(project_path,'ABI/gene_mapping','%s_100Parcels_7Network.csv' % donor)\n",
    "gene_assignment.to_csv(path_or_buf=csv_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A preview of the outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<nilearn.plotting.displays.OrthoSlicer at 0x1c162ab910>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1609aa50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ex_gene = 40\n",
    "plotting.plot_img(img,draw_cross=True,cut_coords = tuple(gene_mni_coord.ix[ex_gene]),\\\n",
    "                  title='Parcel: ' + gene_parcel_assignment.values()[ex_gene].astype(str) + \\\n",
    "                  ' as '+ gene_community_assignment.values()[ex_gene], colorbar=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<nilearn.plotting.displays.OrthoSlicer at 0x1c26d0e690>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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erPnrGk1J5Uf7aMLwYyI80E89fC+J5GkZfAJVp+zFgJ8rBSG9q0FmwcqPH7wdDMcD/UpcQuA//FrQ8jA/rRntvSSS5OXkyx5sqjW9Q88z1fYCAF7P7aDJ9Xsf1WaekNy/mfP9ZiGKnPPjF2/fOGY6FC6c8DA/IvXp8QeDlqFHsHmvAkiBx9NtH1xqrnp/tBgOM+ofeXoomfOj/S48htvnJxg8zC/88DA/ovCWNeVOvbugG28Tpikw0pwfuaaWiAo/FJ54mB9ReJKCT1Sx+nM0El/a47PqQ5HJJgqyTS0MP2QYZj7MjyjcaFnxkTY0DHZF16Zbqmk+SZnVH3WFqvITMXN+KPxJh/ndc889eneFiLzwFHycqz/2MqLia3nbuVna2DBQq07txoD6d7oGoHZBXdIjrgZTj+85P4Hv6+SM4Yd0x8P8iMKHGhUfX0dVaOnCfu1+7Wm1GsxMfO/wzPBDEWLDhg2499578fXXX2PQoEG4cOECKleurHe3iMiNkuAjV/XxN/AEW/XxRS6kcBhLX6II2FQ8wFQO5/yQYTgf5kdExhJMxUfPSo+/gp0nxPAUHKnyI9fUEnGVH0+H+YXDgX5UwqyH+REZmb/Bx0hhR5r34y9PAUhpsOHwV3C4z08AUlNTUb58eUebOnWq3l0iPzgf5kdE4UeNZe9aD3kFyp9AwwpQYFj5CYDcYX5kXDzMjyiyNJim7YGjFNlEAHbO+SEionBhhuDD4SxtharyEzHhx9vBb2Y6FC6c+Pp34b8bkf6UzPdpMG23KYKPRGkA4tBXYGwQZJtaImbYiwfDhR/+mxEZm6/gIxd4PB10qnQStD/zfVad8h24ApnsrIQUgBhw1CWKAort2keTiKn8EBGRegJd2i53wrvaJ78rCT56eyjuZ7yb9a3e3QgrnPBMREGL+bq2y+fn/4zz+ZxKqb9o1R0KE4EGn6wpdyKq2L/jLYzuLbfwMqpBJ516Yg4lE561P9CW4YcoArmHHn8UfNYEgOcQJN0nYVCKPMEEH4m03D2SQpCcTbdUKzX09VDczy6fv5v1LYYxNCni+3gLdTD8EEWQYEKPu4LPmjjCjXvocX4MwBBkNu5zfbQ84V0v7hUf59vdqz/OAcg9+EgYgJSzc5NDIlJKzeAjKfisiWzwCeRxZC7xJ0tauJELPkrvp8CJIlBsj5JtamHlhygMaRF01FBlV1Wfj6lfvmQLgx/aRP6QCJWQAlDeox1R9R3vq74aTtmt6w7PgQabTbdU4+RmFYgQuMkhEbmK+bq2YYMPAGQW+A4/ktsOCbjtkPYTG0ldkbyfDys6gTl+/DiSk5MdLT4+HvPmzXPcP3fuXAiCgAsXLvi8lgigWIySbWph+CEKE8GGHm8rvWwqlpP9CUAAQ5CRKJm7E+z8nrxHfS95bzjFd8DquHxiUP3QQ6RWhpo3b4709HSkp6fj4MGDiI2NRZ8+fQAAp06dwtatW9GgQQOFVyup/Mg1tTD8EIUBI1d7nCVWygv4uQxB+ovkqo4vrPqo48svv0Tjxo3RsGFDAMDTTz+N1157DYKg7GdbFFn5ITI9NYa5zv8Z56j62OxRHpslyq7oWkpWdQUTgAAwAIUB5+qPXCUoLkv/OV1a7e7MvX7krVq1CgMHDgQAbNq0CfXq1UPr1q39ugYrP0QmZtleV5Xr1KxwGYD80Ja/wScUy9qlKtAnZw5q/loUmEhb3u4r0Ixq0MnRyLPr169j06ZN6NevH4qKijBz5kxMnz7dr2uIEGAVo2SbWrjai8iApOBjvRFYohUGFH9Uvf+EoZenz6p1AAAcAeiaWIy+CXfo2SVyIwWgSBku0zrYRPo+P5s3b0abNm1Qq1Yt/Pjjj/jtt98cVZ/Tp0+jTZs22LdvH2rXlq9mh2qHZ1Z+iAxGrYqPEkqrOKHexFAKPu7Wnf4upP0g/fia9Lxn+NwQ9cS3t7K+5ZwhAGlpaY4hr1atWuH8+fPIzMxEZmYmEhIScOjQIa/BBwAgCrDaLbJNLaz8EBmIXPCx2qM0qf4EqlLqL6WqRkrm+kh7/ARj3envWAEin7Sa7yORwk4ZAMWavlJ4KCoqwtatW7F06dKgriMCsINnexGZgpJqT6BDYNJk56r3n/B4v6cg436/t+cpneCc/9c8OEcfTxsirkzcquha7hUghiF9CLaS/5566WbQqP9KZAyBufNW2SkTwn4YVWxsLHJzc2Xvz8zMVHQdETf/X6clhh8infk7zOVPFej8n3GGOHcr/6+lA1L+X/MU7QgtiRFKfsVcE0v/nc1qUGhJoccT5yAkCcdApHQYqxjKwk+kz/dRE+f8EEW4QOf3KP3LKNjgo0Zw8hR8lNznL84H0o5gc21GF+yQF+fv6CdUq70Yfoh0ovXE5mudcxQ/NtCQY4SqkjMGoOBpsXLLUzVICW+TnsNxh2dSQCyp/Mg1tXDYi0gHwQQfW9fskv+q1ZkbfM39kSNVb9yHsJRWdaThL6XzfXxxDkAcCgtMg2m7I24fH6W0qPpwyEs5zvkhilCBBh8p9IRCIBWdYIaw8v+aB5wJ+OmypCDEEOQ/tQPQqZfuDMu5P8Fi8PFPyanuXO1FFFGMHnz0HMa6v15br/dzt+fQk4bAAh22UkPDKbvx+zTlr/96bgcNe3PT7HP3YnKtbR7vY+AJjk3FuT1yGH6IKCxI4SiQeT2sAEUmLYLOqAadFA99eQpADD7BEcXQrPZi+CEKkVDu3BzJpADDEBQ67sNVgVSCIm3ISwo9s8/dCwA4mXJVz+5EEEH2HEI1MfwQhUCwwceyvW5I5/yEg74Jd+CTMwc97vtDkaViZukT4i8lalcd8Kf648ucTNeQ/mwig7cvIvf5ISKSd3+9to7ND/3BJfGenX3mThT16eDS1KBF1cfTMvj9yRbsT1bn/CdvJ7jPPnevo+IjVYAa7y/n8pg5md+VCj7S7SRPFAGbXZBtamHlhyhMBFv9STpY8uP+c1urWl0yhPvrtXWZDK20EsQhsJvOPnNzGKugkQWVfr25kUJRnw6I/WivHt3yaX+yBSnp+u+6OLnWNsw+dy/mZH6HZxPvcAk4D+0eW+rx6zOXsALkhbezvdSKPww/RBr6ZeXtJR+cBv5353seH/NaVk/N+yEFH+ePIykEua8U86eyY/YQ5Bx8nEOPWtSo+lR9Zw/yHu3o8rlErUqPHPchMKni4/6xv6SgRK5EH3N+1AotHPYi0ogj+PjwXIMtGvfEM+dAFGkCCTIcClNGmuxc7SffQSlSJjnLDX+5W/JHF5z8IBkP7R7raJ7I3U4lRFG+qYXhh0gD7sFHruojsXXN9jmkFcyQl1yVhwHIldkDUEEjz1UU57k/1X6yOYKP9LFzA0pCj5bBx7kK5EzLIbBRDTr5rPSMrbED6+9covianP9TmigCdnuUbFNL5P6fj0gnSis+nngKOFqv9Eo6GB1RQ2DO+ibcYfpA44005KXmcJeSilCwnIe9nGk9BBYsT8GIw1+l8VR3oggxYvdQjNg9VPb+50/+IHufWsHHW8BJOhgdsVWgvgl3IEYoo3hV2LrT3zEwkUfB7uXjbSiMbrLbBdmmlsj8vx2RToKp+jx/8gf8u/FtKvbGf5E4GZp8cx7u8lYFUrL0XauVYXLVnnDhz3CYmYkQuM8PUaTxVv0BvFeA1KA01ERaFUhaDRbInkCRynmVlxJ6Bp9wwspOkG4cbyHX1MLwQ6QSpVUfXwFIa6zqKMOhLyKdiF6aShh+iAxG6+oPKRepAcjfqo8SrPrIV318DXlx1ZcrzvkhChPBzPXxRApAWs0Bcq7+eBriirTqUDBngJl9E0SiUBLBs72IwkIgwUfp0FcoqkA/t7WWapGGh58qJ7fXD3n3RUYLj7dzorOfREC0C7JNLQw/BhKd2EDvLlAIGSkARTo1qjaRMgQW3ShR7y5EpO5NjwX1fA59SeSDD8NPBIpOqAdYbQxAJqP35Gcz4UqvEqf61PX5GFZ/SFchmPDMOT86ik6oV/pGpwBkzcwKcY/IqLSeA2QG0nL3YCo4Zpr/4366O1FI3Bj20horPzqITqjnOfi4Py6IKtAvK293NNJOsO+vvdiCR78ervjxHAILnhmCC+mDQ19qEbw0dTD8hJiS0OPy+AACkPsvZAYg/TQZchhNhhz2eJ+9OLChBQYg/YXr/B8tlrhLlGyCaDaBTHZmAAJg99JUwvATBvwJQHJBhwFIX3IBiPTRN+EO01WAAgk+nPsTOH+DT7EY5WimJgIQBfmmEpO/y6GjdKgLAGC13WzS830EICVDXFdaVMSVFhU5JKYC6b30xj3wNBlyGPZii6M5e/Tr4Rz+0kEwIShcqz9kHFLQcQ88s37bp0d3DEO0yze1MPyEgL9DXTefqP1fXQxA2gnkvVUagLqUV/H/AmTKSpBSrP5op4zAn2OPWPkJb35Vezyxuq604DJ4YwgmMDYbfiDo12fw0Y6/IShcqj9azvWR8HiLEuvvXKLKxoamrf6IgGCXb2rhUvcIEGz1JuO9NgCApkMPqdEdUplc2Pk8Ox096iaHuDfm0DfhDgw+dtrx+UNxp7082thCEXxIXjmh9HYBV0UL5/bIUrfCI4fvvkaCqvjoJOO9No4gRJ6pMUzoq/rjz/yfz7PT8Xl2etB9Inm+gk+4VH+0ZKaqz8mUq17vLyfYXJrcYypG+T5yxbTVH672Ck+qBh+3ic+kn0CCT6jmVDEAqe//WiQorvhEagASo0raxSac96PE+Jpf+vX4ilHFjhbuEhMT0apVKyQnJ6Ndu3YAgLy8PHTr1g1NmzZFt27dkJ+fr+xiIdjhmeFHZeFY8XHH6o939qLgR4uVzP3xdwUYkVbqztmN2I/2OpozM1V9JCdTrnpswXAOQuEahrZv34709HQcOFDy/7fZs2fjb3/7GzIyMvC3v/0Ns2fP9n0RERDsgmxTC8OPijQJPtEWTVd9uW8jLl7lX3ienFiaAntR9M3go/CH0Nv+PmpMfiZthfsKsEDn+0gVHzlmDDzkv40bN2Lo0JLzC4cOHYoNGzYoeyIrP+EjVBUf9xVfwQyriHYB4lWLS+ARypUMsbH6Iy8q1gpEyf8USrs6K9nYUEkA+suWkfjLlpGyn5O2lAagSBn6UjoP11MViNQTbhUgQRDQvXt3tG3bFsuWLQMAnDt3DnXq1AEA1KlTB+fPn1d2LVG+qYWrvczGLigetsl4rw1XgKGk6qOlZsMP4MTydj4fx8Cjn74Jd0RMuPGGC5AoULt27ULdunVx/vx5dOvWDS1atAjsQiIUV9aDwW91FchVfcTCSxALL6n/eokNAt+hWaZiIVWA3BuV5i08BnqMRbPhBzgMFgHCMSBJQ1wMPsZzyV4mbFZ81a1bFwBQs2ZN9OnTB/v27UOtWrVw9uxZAMDZs2dRs2ZNRdcKxT4//HZXkRR2nEOPEO/9CASvQrDDsy9aVz0iiRrndzEEGZev4a/3CpvivcKmIeqNf5wDjnsjCtaff/6JS5cuOT7+4osvcOutt6J379547733AADvvfceHnjgAWUXDMGcHw576UCVYES6iYq16t0FIiLDOHfuHPr06QMAsFqtGDRoEHr27ImUlBT84x//wP/+9z80aNAAa9as8XktQVS3wiOH4SdIlvh4r0NbSu6TDUFWm2z1p8X/y4U1M8v/oa8AxlJPLE1BszH7/X5eJFBa+Qqk6nPibS/zfKQffv5lbiie5v64V3t6H83FpluqhbJbXrG6EzrjGv4V//19l97dCLlGjRrh+++/L3V7tWrV8OWX/u19BCAkOzwz/AQhOqGeJnN6lLBmZgEA7NdKwlFUDDdC1IvqwYdIBdnP8liLcBdOq73UFIrKD/8mCDNiwSWIBfoELrPRfb6Tgp9O52Xv3OU5NJQsfe99NDcEPfGubIGKEySIQon7/EQ+W2EhrKfPyN7vHHQYeoxHjUnOauAy+NCSJjcbdYIzybvSJF7T649r+Negnn/JXkalnoQpnupubGoPeUkBSFo2L/5Z5LhPSehxWX5tuRGPbTfHTZuN2Y+LIsOTauwCyp8oBAIYmlY05KWw6iP5refbAG5Wf3jau3aMUNUJpbpzdmt38SgLYA/dkH3Ggg7AfO33LlMSgJznBrkHHgYg7V+C4UdHtsL6xs7dAAAWC0lEQVTCUrc5h57gLi7/W7nZmP36D+mEMXtRdEnw0UoA9dhSlZ/lPD7D7Kov2+My70caBrteqfT/GzQNON44B58oS+nbNHKlSTxwUvOX8co5IIXLXj6houZOznI47BWgYKs+noKParwEH4m/q7cYltTTbCRDCYWW8/wfw84FsttKWpQ2+5tlLOjg9XMyEO7zQ3IcYUThH0knlqYAo3do1R3yk3sAKjUU5j62zT9TDGPTLdXCaujLU9hxrgLpVvVxplHgcSbG2SBcdn2djAUd0PRJnk9mKNznx7gs8dpMmLNUqaL4sVLlJpQVGTPv9+Os2Zj9ULt2I1cNCmZJvHReGIe/SI4hqkCego/Kc4GknwUxzlYyJ9KpOi5VgPQKQRzy8oDDXubhT/BRA4exvDPK+9Ns5IGgh8lOLG+n6OBUUibcqj6e5vg4uzC6Y4h6ow+P3/sWsSQIOdFjGExJ8HnhL+1D0BPjEMDVXoZlKyzUrPpD5InSk9+9YSUoeOEUfICSYS0l1R0pAFVftif4F1VatQnBUJc37kNggLIApEaFiNUeL0ROeDYsvYOPLT9f19c3O6NUhQLFKlBg/Ak+RjreImSiLDcDjfSxc3N/LJEcu5emElZ+dOC+0svfIS9LlSoBBaArzeIdS7S53N07X+/PlWY3AnBG4K+RsVB5mV0sz+NL9BRuFR+JIeb0SHwEnqhyMQAAoUIFAIDtjz807Y6nCdChwKqPb6z8RCBNl7hTyAUaIP0JPlpg9Yc8ifT5P3pj8FFABCs/ZhDqic5XmsU7qhWs/qhD7j2UWxmnd/AhMpqocjGOio8uDDoKZ7bJzhJWfshQzLbMPdivN2NhB0MHHVZ/tGHK+T4BiioX4xjuEv/806VFGlZ9lDPdaq8D4g69u6BMASBY/H/rRJvV8bHj+YX+HQ4lWotdPr8yV/k5NddPZQMAvnE+i9GP5xeK5huyk3t/pffy3Nyl8k++8ddLThOgfEbJe3elaTzwnwDOForS5k+hc40FlP/FfP+u/sr8p9X3gwBc2O/7/wsVURnNBZ69JoUeXRm04mN6PNsrsgQSmCgyXGmq0gpB+42wLIUhu1t4DiAkOZ9yzSBEAfNyNIUhgo7OHv78CQDA2h5vKnq8mYe8QjHsZajfxu2ELnp3QTm7SkveLf796eG+yuvEROVzdqQqRa2JY/x6TYnZhr0AABme5/QoeS8F681RZTHa7vK5GsTyNghXLKVuU8rTShdpDxNu+++q9wplq72MNuRVfdke3SYxS4HHfvWaLq8fSieWlA4qzcbu83j/w58/oTgAmRWPtyCfOGnZuMRobX+ChSsWl7AjXLE4wpBzMApkmTyDz01Kl7kbLfiorn2rmx/v+9HzYzxUfyK96uMp+Hi7XQmzVn0cOOHZBGy2kmZwpqz6qEjtqk9AfbhscWme8KTr0s5er4yz1yvr3Q19OQcfT58TqUU04YTncKP6MRdSCPJzKIzVH+NSGnqaPq5upSWYlVzOAShSK0Ap6Z7/4KhT9iIAeAw73gKQ9DwjUjL0VT39sv8Xbt/KcwXIbkNUbKz/1wslu1BqyFgJtX5Opfk/Eg6DuTHbnB9Ts9n8Dj3OnCszagchs1d9Ag2XegUfoOT8LiMvs5fjKZTsT1Z3SY5c8AmG0StDzgHIr6ATARUeNc7Fo9DinB+zCTIASaSwEmwIMnvocRZIAHKe86PHsJenUBXJw1rOocY9MCkNPIGGGLUDmq4iIPBQeDPdaq9wZOQT3oMJQQw+pak9vKhFxcfnazqt5lIiVKu+vIWTlHSbbLiQe54W1R054RR8AhreIgol6XgLjTH8qEA6r0vrEBToae5SkDnXtPRt5B/pfSsUC28ebqrg8UbjHmiMFobchTLM+MP0wUdu3o/RRYket4rQi/McIOcl8mYkgJUfc/Iw9BVo6HHnfKI7Ba/8icKIeS+9VYTcb1M7ABk12PgSTsEHQElI4ZCWg1GCjzOzBx+JYNc+/TD8qEiLCpBawYdICaXDYmZYERZ24YaUc98ZnYxDBPf5CVe2wkJHEPL1GOfmcn9+PoMP6abpk3tDFmqMFjL2J1sM1yfVhOMQlYFouYKSVZ+buM9PmPMVgIJ9PJHWmj65V/UVYnd+f93x8e7WZQHcDEChGgKL2HBDymh0UDCpw9ucH7X+5Rh+iMgrX0NhzmHGnRRu5Nz5/XWXx+xPtmgSgBh2zCsc9vhh1ceJyH1+iMhAtKgCqYXhxg8hmPgsuh3ZIwSwf1lUbCzsRUVB9SNjYQcIV9zOtwtyvk/Gwg66bFNhKlztRURG4ikAvff1XQCAoZ2/UeU1GGQij2iz6RaAAHVXdqkdfFj1cSWAq72IyIDch8G8hR5vQ2KA72Ex0giXvQeEwSc0QrHPD1d7EVFAmj65N6hqD4OPzkK88st9KEypYA5JFWNsjmY0kRR8Tp06ha5duyIpKQktW7bE/PnzAQBTp05FvXr1kJycjOTkZHz22We+LyYCgk2+qYWVHyIK2O7WZX1Wd8jAgq0A+RmgnAOQP8Ng/g5/nXi79CRnMcYG4Vrgw19qVn0iKfgAQHR0NF5//XW0adMGly5dQtu2bdGtWzcAwNNPP42JEyf6d0HO+SEio5MqOP6EIFZ9SCuegk8woYd8q1OnDurUqQMAqFixIpKSknDmzJnALiaGZs4Ph72ISBW7W5d1NF+PIwMJZvgriKqRv8NgSoa/PAUfCq3MzEwcPnwYHTqUzAl88803cdttt2H48OHIV7hxryDKN7Uw/BCR6pyDkHsjA9IpAIW7E0va+/2cSBvycnb58mX07dsX8+bNQ3x8PB577DGcPHkS6enpqFOnDp555hmf1yhZ7aX9Ds8MP0REpEsAEm02l+aLv5Of1Rru8jTfh8HHVXFxMfr27YvBgwfjoYceAgDUqlULFosFUVFRGDVqFPbtU/D1i6L3phKGHyIiKqFzBUhJCFIagKTgo0YA0vJMr0ggiiJGjBiBpKQkTJgwwXH72bNnHR9/9NFHuPXWWxVdj2d7ERGR4Qnfnyj5ICYm+GsFsBkiENr5Pqz6uNq1axdWrlyJVq1aITk5GQAwa9YspKWlIT09HYIgIDExEUuXLlV0vVDs88PwQ0REYcVSpQpsCifPqoXHWsjr1KkTRA9DUqmpqf5fTARg4w7PREQUSn7u/eOo+qhEGvaSqwAJN6pLUgCSq/gEu6+PJ9Lwl/Nme6LF9y/qSK76aIGVHyIiCjnLsd9dPre1aOjxcWoHH3/8PKex1/u5t0/44j4/REQUcrbCQr27EBQtg0/Tx/f6NfzFqo+fRB9NJaz8EBGRV0orQUbX9PG9yGmKgH+Jcs6P9gQAAuf8EBGRHqTqjyU+vtR9lmO/OwKQeO2a43bBabWX8+2e7g+E8/OTnj3pOvRlEwCLWPJfN2K0Hc3G7MeJpSnAnEN+vaanwJOxsAPEaKd112Lp1wRY9QmUoOJ+PnIYfoiIyG+WY7/DfdsVT4HH/X4lAUi8dg2Cj/18PM75uRF8xGg7BGuU42MAJcHHT4qCj0SapXsjCDH4BEgUAc75ISIiPXmb/xMVE+OxMhQMKUDZi4ogxMS4NGdJz570fp1ou0xIUdYPuSEur8HnxscMPsEJxdlerPwQEVFQhJgYn1UfidLqDwDY8vNhqVJFWSecl5x7GPpyIciEmBuajdnv8fZAqkfkJ5FzfoiIKAxExcQ4dndWslLMWwByD1J+BSCN+BN65IIT+YFzfoiISG/eJj+7kx7jKwS5V4q8VYPkApDHSc8qCaTKw+CjDu7zQ0REhuHP/j+W+Hi/5gOJ1645ApF7EPJa+bEJN5tKGHx0FoJT3Vn5ISIixWyFhX6FGkt8fECbJioZ6vp5dhO/r6sFBh/1CKLIOT9ERGQOes/rCRSDjwbs8pPRla7W88VQ4Wd19jJUrW2sH4C8nHz0rzta726Qn/T+XuL3DUUyf+YAuT/OvQoU6FJ5W36+I3josQqLoUcjIlBqAylnKp1cYqjwo9UvK1EUMWvWLCxduhQXL15Eamoqli1bhvgbP3TDhg3DBx98gLJlyzqeU1BQAIvFYrgwRsqo8e/24YcfYt68eUhPT0f79u2xY8cOl/u/+uorTJw4Eb/88guqV6+OyZMnY/To0aq9PpnXAXGH3l1QpgAQLH7+GnGfaXr5xn8F//6kF8Xim5+M3oErzZSFqOunsgEA5+Yu9ev1nJU/UYgDAT+bfBG8VX5UCj+mmPC8YsUKrFy5Ert27UJ2djauXLmCcePGuTzmueeew+XLlx3NYuGJwGZXtWpVjB8/HpMnTy51X3FxMfr06YMxY8agoKAAq1evxoQJE/D999/r0FMi/Yg2q95dAFASSCLpdczLy2TnSJ7wPGfOHHz33XdYt26d47Zx48bBYrFg3rx5AV3z448/xogRI1C/fn0AwKRJk3DPPfdg8eLFiPWxhTqFp5MnTyIlJQXbtm1DmzZtkJ2djdtuuw1r165Fly5dFF3j3nvvBQC8/fbbpe7Ly8tDYWEhhgwZAkEQkJKSgqSkJBw9ehStW7dW80shE2ondNG7C/6xBz585cKPPzpt+fmlbjsx0ffwl1TxqTVxjPJ+3dBszH7V5pyQDBFACCY8G67y88gjj2DLli24ePEiAMBqtWL16tUYMmQI/vWvf6Fy5coe22233SZ7TVEUITolRlEUce3aNWRkZDhuW7RoEapWrYq2bdu6BC8KT40bN8arr76KwYMHo6ioCI8++iiGDRuGLl26BPx95KxWrVoYOHAg3nnnHdhsNuzZswe///47OnXqpPFXRmRMtsJCRwv8Irag+tBszH5HU4sW1yTvBFGUbWoxXPipU6cO7r77bqxZswYAsGXLFlSvXh1t27bFokWLcPHiRY/thx9+kL3mfffdh7fffhuZmZkoKCjAq6++CgAoKioCADz55JPIyMjA+fPnMWPGDAwbNgy7du3S/osNsYqojIqorHc3QmbUqFFo2rQpOnTogLNnz2LmzJkAEPD3kbuBAwdi+vTpiImJwV133YWZM2c6qotEZhZUAAKCDkFqYeDRgQjAZpdvKjFc+AGAoUOH4v333wcAvP/++xgyZIji537zzTeIi4tDXFwcWrZsCQAYPnw4Bg4ciC5duqBly5bo2rUrACAhIQEA0KZNG1SrVg3R0dFITU3F4MGDsX79epW/Kv01F5LRXEjWuxshNWrUKBw5cgTjxo1DjMLzhJQ4duwY+vfvjxUrVuD69ev46aef8Nprr+HTTz9V7TWIwlnAVSAp+KhQBdLz+RSo0Mz5MWT4efDBB/HDDz/gyJEj+OSTTzB48GAAwNixYx3Bxr1JQeeuu+5yTFr+6aefAABRUVGYNm0aMjMzcfr0abRs2RL16tVDvXr1PL6+IAguw2QUni5fvozx48djxIgRmDp1KvLy8gAo+z7y5ciRI2jevDl69OiBqKgoNG/eHL169cLmzZu1/JKIwk7QVSBP1/Qw30cNHOIyCLtdvqnEcBOeAaBcuXJ4+OGHMWjQILRv3x4NGjQAACxZsgRLlizx+3p5eXnIz89Ho0aN8PPPP2PChAl4+eWXERVVkv3Wrl2Lnj17IjY2Ftu2bcP777+Pjz/+WNWviULvqaeeQtu2bfH2229j9OjRGDt2LD788EPF30c2mw3FxcWwWq2w2+24evUqLBYLypQpg9tvvx0ZGRn46quv0LVrV/z666/45JNPMGnSpBB8ZUThJeC9fZyqP4GEKE8h5lxT+fvIAEQAZj7ba+jQofjxxx/9GvKSc+HCBaSmpqJChQq47777MHz4cMd+LAAwf/581KtXD5UrV8azzz6Lt956S/GKIDKmjRs3YsuWLY6Q88Ybb+DQoUP4v//7P8XXWLlyJcqXL4/HHnsM33zzDcqXL49Ro0YBKJlQvXz5cjz55JOIj49H586d0bdvX4wYMUKTr4cokvg7JKZm9aj8iUIuVzc0EbDb5JtKBNGg4ztZWVlo0aIFcnJyHJsR6qVbVD9dX5/8t9W+Ru8u8PuGyA/u1SAthsuAmxtIht12AgEywv8L/VGpbC3cWXug7P1/1PwWBw4Ev8WkIYe97HY73njjDQwYMED34ENERNrTKuxQGFJxbo8cw4WfP//8E7Vq1ULDhg2xZcsWvbtDREREIaPuqi45hgs/FSpUwOXLl30/MITCrWxIxsDvGwoUh0zJtESEZJ8nw4UfIiKzW529LKSH4+bl5KN/3dG+H0gUCmas/BARmV0og48er0ckSxQhhqDyY9il7kREpJ4PP/wQSUlJqFixIm655RZs2LBB7y4ReWYX5ZtKWPkhIopwZ86cwSOPPIKNGzeiZ8+e+Oyzz9CvXz9kZmaiZs2aeneP6CZRDMmcH1Z+iIgMZvXq1S7HrsTExAS18erp06dRuXJl3HfffRAEAb169UKFChVw8uRJ9TodJsx2wHNYMuvZXkREZta/f3/HGYXZ2dlo1KgRBg4ciNmzZ6Ny5cqyTU67du2QlJSETZs2wWazYcOGDYiJicFtt90Wwq/KGMx4wHN4KZnzI9fUwmEvIiKDstvtGDRoELp06YIxY8YAACZPnuz3dSwWC/75z39i0KBBuHr1KsqWLYs1a9agQoUKaneZyMWWLVvw1FNPwWazYeTIkb6/f81+thcRkdm9+OKLuHTpEhYsWKD4OVlZWS5DZgCwbds2PPfcc9ixYweuX7+Or7/+GiNHjkR6erpWXSeCzWbD448/js2bN+Po0aNIS0vD0aNHvT5HBEJS+WH4ISIyoFWrViEtLQ1r165FmTJlAACzZs1yCTbuDQAaNGjgGDKTNoxNT0/H3XffjXbt2iEqKgopKSno0KEDtm3bptvXR5Fv3759aNKkCRo1aoSyZctiwIAB2Lhxo/cniSIg2uWbShh+iIgM5vDhwxg3bhw2bNiAGjVqOG5/4YUXXIKNe5OTkpKCb775xlHpOXz4ML755htTzvmh0Dlz5gzq16/v+DwhIQFnzpzx+pw7e7RHXutfZFv16tVV6Rvn/BARGczGjRuRn5+PTp06OW676667sHnz5oCu17lzZ0ydOhUPP/wwzp07hxo1auCFF15A9+7d1eoyUSmih9VZgiB4fU6ozvRk+CEiMpipU6di6tSpql7ziSeewBNPPKHqNYm8SUhIwKlTpxyfnz59GnXr1tWxRzdx2IuIiIhUl5KSgoyMDPz222+4fv06Vq1ahd69e+vdLQCs/BAREYCt9jV6d4EiTHR0NN5880306NEDNpsNw4cPR8uWLfXuFgBAED0NyhERERFFKA57ERERkakw/BAREZGpMPwQERGRqTD8EBERkakw/BAREZGpMPwQERGRqTD8EBERkakw/BAREZGpMPwQERGRqTD8EBERkakw/BAREZGpMPwQERGRqTD8EBERkakw/BAREZGpMPwQERGRqTD8EBERkakw/BAREZGpMPwQERGRqTD8EBERkakw/BAREZGpMPwQERGRqTD8EBERkakw/BAREZGpMPwQERGRqTD8EBERkakw/BAREZGpMPwQERGRqTD8EBERkan8f+VdiAQC7GLmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c26aad8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ex_gene = 354\n",
    "plotting.plot_img(img,draw_cross=True,cut_coords = tuple(gene_mni_coord.ix[ex_gene]),\\\n",
    "                  title='Parcel: ' + gene_parcel_assignment.values()[ex_gene].astype(str) + \\\n",
    "                  ' as '+ gene_community_assignment.values()[ex_gene], colorbar=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mni_x</th>\n",
       "      <th>mni_y</th>\n",
       "      <th>mni_z</th>\n",
       "      <th>img_x</th>\n",
       "      <th>img_y</th>\n",
       "      <th>img_z</th>\n",
       "      <th>parcel</th>\n",
       "      <th>community</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-29.2</td>\n",
       "      <td>5.8</td>\n",
       "      <td>-2.6</td>\n",
       "      <td>119.2</td>\n",
       "      <td>131.8</td>\n",
       "      <td>69.4</td>\n",
       "      <td>0</td>\n",
       "      <td>NotAssigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-10.1</td>\n",
       "      <td>5.9</td>\n",
       "      <td>-8.4</td>\n",
       "      <td>100.1</td>\n",
       "      <td>131.9</td>\n",
       "      <td>63.6</td>\n",
       "      <td>0</td>\n",
       "      <td>NotAssigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.9</td>\n",
       "      <td>8.5</td>\n",
       "      <td>-7.4</td>\n",
       "      <td>81.1</td>\n",
       "      <td>134.5</td>\n",
       "      <td>64.6</td>\n",
       "      <td>0</td>\n",
       "      <td>NotAssigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.9</td>\n",
       "      <td>0.8</td>\n",
       "      <td>-6.7</td>\n",
       "      <td>82.1</td>\n",
       "      <td>126.8</td>\n",
       "      <td>65.3</td>\n",
       "      <td>0</td>\n",
       "      <td>NotAssigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-9.1</td>\n",
       "      <td>73.1</td>\n",
       "      <td>126.9</td>\n",
       "      <td>62.9</td>\n",
       "      <td>0</td>\n",
       "      <td>NotAssigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>37.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>4.1</td>\n",
       "      <td>53.0</td>\n",
       "      <td>125.5</td>\n",
       "      <td>76.1</td>\n",
       "      <td>76</td>\n",
       "      <td>SalVentAttn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13.4</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>76.6</td>\n",
       "      <td>128.2</td>\n",
       "      <td>72.2</td>\n",
       "      <td>0</td>\n",
       "      <td>NotAssigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>24.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>-22.1</td>\n",
       "      <td>65.2</td>\n",
       "      <td>127.7</td>\n",
       "      <td>49.9</td>\n",
       "      <td>80</td>\n",
       "      <td>Limbic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19.2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>-14.9</td>\n",
       "      <td>70.8</td>\n",
       "      <td>126.7</td>\n",
       "      <td>57.1</td>\n",
       "      <td>0</td>\n",
       "      <td>NotAssigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>18.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>-22.1</td>\n",
       "      <td>71.9</td>\n",
       "      <td>127.6</td>\n",
       "      <td>49.9</td>\n",
       "      <td>0</td>\n",
       "      <td>NotAssigned</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mni_x  mni_y  mni_z  img_x  img_y  img_z parcel    community\n",
       "0  -29.2    5.8   -2.6  119.2  131.8   69.4      0  NotAssigned\n",
       "1  -10.1    5.9   -8.4  100.1  131.9   63.6      0  NotAssigned\n",
       "2    8.9    8.5   -7.4   81.1  134.5   64.6      0  NotAssigned\n",
       "3    7.9    0.8   -6.7   82.1  126.8   65.3      0  NotAssigned\n",
       "4   16.9    0.9   -9.1   73.1  126.9   62.9      0  NotAssigned\n",
       "5   37.0   -0.5    4.1   53.0  125.5   76.1     76  SalVentAttn\n",
       "6   13.4    2.2    0.2   76.6  128.2   72.2      0  NotAssigned\n",
       "7   24.8    1.7  -22.1   65.2  127.7   49.9     80       Limbic\n",
       "8   19.2    0.7  -14.9   70.8  126.7   57.1      0  NotAssigned\n",
       "9   18.1    1.6  -22.1   71.9  127.6   49.9      0  NotAssigned"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene_assignment.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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