CBCX_ON_CBC_contact_comparison.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparision between the size of basal contacts of CBCX and OFF-CBCs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.io import loadmat\n",
    "import rpy2\n",
    "%matplotlib inline\n",
    "matplotlib.rc('font',**{'family':'sans-serif','sans-serif':['Arial']})\n",
    "matplotlib.rcParams.update({'mathtext.default': 'regular'})\n",
    "matplotlib.rcParams.update({'font.size': 14})\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from rpy2.robjects.packages import importr\n",
    "import rpy2.robjects as ro\n",
    "from rpy2.robjects import pandas2ri\n",
    "pandas2ri.activate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#import r package\n",
    "r_stats = importr('stats')\n",
    "r_base = importr('base')\n",
    "r_lme4 = importr('lme4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xbc_contacts=np.loadtxt('data/CBCX_contact_classification.csv',delimiter=',',usecols=range(4))\n",
    "xbc_contacts=pd.DataFrame(xbc_contacts,columns=['cell','cone','type','class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "on_contacts_xbc_comp=np.loadtxt('data/CBCX_ON_CBC_contact_comparison.csv',delimiter=',',usecols=range(4))\n",
    "on_contacts_xbc_comp=pd.DataFrame(on_contacts_xbc_comp,columns=['cell','cone','type','class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "stat_xbc_contacts=pd.DataFrame(np.concatenate((np.tile(np.unique(xbc_contacts['cell']),3).reshape(-1,1),\\\n",
    "                                               np.repeat(np.arange(3),np.unique(xbc_contacts['cell']).shape[0]).reshape(-1,1)),axis=1),columns=['cell','class'])\n",
    "for i in range(stat_xbc_contacts.shape[0]):\n",
    "    stat_xbc_contacts.loc[i,'count']=np.sum((xbc_contacts['cell']==stat_xbc_contacts.ix[i,'cell'])&\\\n",
    "                                                   (xbc_contacts['class']==stat_xbc_contacts.ix[i,'class']))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "stat_on_contacts=pd.DataFrame(np.concatenate((np.tile(np.unique(on_contacts_xbc_comp['cell']),3).reshape(-1,1),\\\n",
    "                                               np.repeat(np.arange(3),np.unique(on_contacts_xbc_comp['cell']).shape[0]).reshape(-1,1)),axis=1),columns=['cell','class'])\n",
    "for i in range(stat_on_contacts.shape[0]):\n",
    "    stat_on_contacts.loc[i,'count']=np.sum((on_contacts_xbc_comp['cell']==stat_on_contacts.ix[i,'cell'])&\\\n",
    "                                                   (on_contacts_xbc_comp['class']==stat_on_contacts.ix[i,'class']))   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tip_invag_joined=pd.concat({'CBCX': stat_xbc_contacts, 'ON-CBC': stat_on_contacts})\n",
    "tip_invag_joined=tip_invag_joined.reset_index().drop('level_1',axis=1).rename(columns={'level_0':'BC_type'})\n",
    "tip_invag_joined=tip_invag_joined[tip_invag_joined['class']>0].reset_index().drop('index',axis=1)\n",
    "tip_invag_joined['contact']='invag.'\n",
    "tip_invag_joined.loc[tip_invag_joined['class']==2,'contact']='tip'\n",
    "tip_invag_joined=tip_invag_joined.drop('class',axis=1)\n",
    "tip_invag_joined['cell']=tip_invag_joined['cell'].astype(int).astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ej4fff/8d5ubm+O+//wBMGp+YmAiZTIbo6GjY2triyJEjuHfv3ozfh9Y15KVSKZKSkiAQ\nCMBisWBjY4O+vj6Mjo5CX18fIyMj2LFjB9zc3JCZmQk+nw8dHR0YGBjgwIEDiIuLg6+vL1xcXNDc\n3Izc3FycPHlyWk0khCAzMxPd3d3gcrlgs9nYvn07iouLkZCQgImJCfj7++PDDz+ccg28ffs2srKy\nqB6Y7du3Y/Xq1Xjrrbfg6uoKFouF0dFRpKamQigUIjk5Gfr6+hgbG4OnpyfWr1+P6OhoWFlZ4f79\n+zh06NCMfZhaZyDDVLTuFMowFcZALYcxUMthDNRyGAO1HMZALed/yj4yAqjbnfEAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f899e79e7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font='Arial',style='white',context='paper',rc={\"xtick.major.size\": 0, \"ytick.major.size\": 4})\n",
    "with matplotlib.rc_context({\"lines.linewidth\": 0.7}):\n",
    "    plt.figure(figsize=(3/2.54,3/2.54))\n",
    "    ax=sns.pointplot(x='BC_type',y='count',data=tip_invag_joined,hue='contact',order=['CBCX','ON-CBC']\\\n",
    "                   ,ci=95,palette=[np.array([0.8,0.8,0.8,1.]),np.array([0.4,0.4,0.4,1.])],linestyles='',markers='s',dodge=-0.2)\n",
    "    ax.set_xticklabels(['CBCX','ON-CBCs'])\n",
    "    ax.set(ylabel='# contact points',xlabel='',ylim=(0,8.5),yticks=[0,2,4,6,8])\n",
    "    legend=plt.legend(bbox_to_anchor=(.8, 1))\n",
    "    ax.spines['left'].set_position(('outward',3))\n",
    "    ax.spines['bottom'].set_position(('outward',3))\n",
    "    sns.despine()\n",
    "#     plt.savefig('figures/xbc_on_comparison5.svg',bbox_inches='tight',dpi=300)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#get r object\n",
    "r_tip_invag=pandas2ri.py2ri_pandasdataframe(tip_invag_joined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#fit model\n",
    "r_model=r_lme4.glmer('count ~ BC_type*contact+(1|cell)',data=r_tip_invag,family=\"poisson\")\n",
    "r_pred=pandas2ri.py2ri_pandasdataframe(tip_invag_joined[['cell','BC_type','contact']])\n",
    "r_count=r_stats.predict(r_model,r_pred,type='response')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pred=tip_invag_joined.copy()\n",
    "pred['count']=pandas2ri.ri2py(r_count)\n",
    "merged = pd.concat(dict(data=tip_invag_joined,mixed=pred),names=['type']).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ofPfdd3To0IH69evTpk0blixZIt3u+eDh5p5Anm3umZcszSwZ02Qww73ezBLb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\nv349DWtHpLQozyL20p6mMnfz5k3MnDkT0WgUbrc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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f899e6d4c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font='Arial',style='white',context='notebook',font_scale=1.25)\n",
    "g = sns.factorplot('contact', 'count', 'BC_type', \n",
    "                   data=merged, \n",
    "                   col='type',\n",
    "                   kind='point',\n",
    "                   size=3.5)\n",
    "g.fig.subplots_adjust(wspace=0.3)\n",
    "sns.despine(offset=10);\n",
    "# plt.savefig('figures/XBC_contact_model.png',bbox_inches='tight',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generalized linear mixed model fit by maximum likelihood (Laplace\n",
      "  Approximation) [glmerMod]\n",
      " Family: poisson  ( log )\n",
      "Formula: count ~ BC_type * contact + (1 | cell)\n",
      "   Data: structure(list(BC_type = structure(c(1L, 1L, 1L, 1L, 1L, 1L,  \n",
      "1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,  \n",
      "2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L \n",
      "), .Label = c(\"CBCX\", \"ON-CBC\"), class = \"factor\"), cell = structure(c(7L,  \n",
      "8L, 9L, 10L, 11L, 12L, 13L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 1L,  \n",
      "2L, 3L, 4L, 5L, 6L, 14L, 15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L,  \n",
      "4L, 5L, 6L, 14L, 15L, 16L, 17L, 18L, 19L), .Label = c(\"543\",  \n",
      "\"550\", \"566\", \"574\", \"590\", \"596\", \"603\", \"604\", \"605\", \"606\",  \n",
      "\"607\", \"608\", \"609\", \"624\", \"640\", \"658\", \"671\", \"687\", \"689\" \n",
      "), class = \"factor\"), count = c(0, 0, 3, 0, 0, 0, 0, 4, 4, 2,  \n",
      "3, 1, 1, 1, 10, 10, 2, 1, 7, 2, 2, 3, 7, 4, 8, 15, 2, 0, 2, 0,  \n",
      "3, 1, 1, 0, 0, 0, 0, 1), contact = structure(c(1L, 1L, 1L, 1L,  \n",
      "1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,  \n",
      "1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,  \n",
      "2L, 2L), .Label = c(\"invag.\", \"tip\"), class = \"factor\")), .Names = c(\"BC_type\",  \n",
      "\"cell\", \"count\", \"contact\"), row.names = c(\"0\", \"1\", \"2\", \"3\",  \n",
      "\"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\", \"11\", \"12\", \"13\", \"14\", \"15\",  \n",
      "\"16\", \"17\", \"18\", \"19\", \"20\", \"21\", \"22\", \"23\", \"24\", \"25\", \"26\",  \n",
      "\"27\", \"28\", \"29\", \"30\", \"31\", \"32\", \"33\", \"34\", \"35\", \"36\", \"37\" \n",
      "), class = \"data.frame\")\n",
      "\n",
      "     AIC      BIC   logLik deviance df.resid \n",
      "   144.9    153.1    -67.5    134.9       33 \n",
      "\n",
      "Scaled residuals: \n",
      "    Min      1Q  Median      3Q     Max \n",
      "-1.1743 -0.6939 -0.5226  0.6181  3.3152 \n",
      "\n",
      "Random effects:\n",
      " Groups Name        Variance Std.Dev.\n",
      " cell   (Intercept) 0.2196   0.4686  \n",
      "Number of obs: 38, groups:  cell, 19\n",
      "\n",
      "Fixed effects:\n",
      "                         Estimate Std. Error z value Pr(>|z|)    \n",
      "(Intercept)               -0.9395     0.6075  -1.546   0.1220    \n",
      "BC_typeON-CBC              2.6000     0.6330   4.107  4.0e-05 ***\n",
      "contacttip                 1.6740     0.6291   2.661   0.0078 ** \n",
      "BC_typeON-CBC:contacttip  -3.6341     0.7141  -5.089  3.6e-07 ***\n",
      "---\n",
      "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
      "\n",
      "Correlation of Fixed Effects:\n",
      "            (Intr) BC_tON-CBC cntctt\n",
      "BC_tyON-CBC -0.954                  \n",
      "contacttip  -0.872  0.837           \n",
      "BC_tON-CBC:  0.768 -0.769     -0.881\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(r_base.summary(r_model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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