o2_vr_dy_panel.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Oxygen consumption analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import os.path as op\n",
    "import cPickle as pickle\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.close('all')\n",
    "datadir = op.join(os.getcwd(), 'data')\n",
    "#Oxygen consumption data\n",
    "with open(op.join(datadir, 'o2data.pkl'), 'rb') as INPUT:\n",
    "    dfo2 = pickle.load(INPUT)\n",
    "#Mitochondrial Network  Topology data\n",
    "with open(op.join(datadir, 'munged_dataframe.pkl'), 'rb') as INPUT:\n",
    "    df = pickle.load(INPUT)\n",
    "#Mitochondrial tubule heterogeneity data    \n",
    "with open(op.join(datadir, 'lagedges.pkl'), 'rb') as INPT:\n",
    "    dflags = pickle.load(INPT)\n",
    "df['lags_1'] = dflags\n",
    "#Cell size data\n",
    "with open(op.join(datadir, 'cellVolume.pkl'), 'rb') as INPT:\n",
    "    dfsize = pickle.load(INPT)\n",
    "    dfsize.index = dfsize.cell"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set defaults for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.rcParams['font.family'] = 'DejaVu Sans'\n",
    "plt.rcParams['savefig.dpi'] = 100\n",
    "sns.set(style='whitegrid', context='paper', \n",
    "        rc={'patch.linewidth':1,\n",
    "            'patch.edgecolor':(.25,.25,.25),\n",
    "            'lines.linewidth': 1})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataframe and bins initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dfvol = pd.DataFrame({'Vol': dfsize.loc[:, 'Vol'],\n",
    "                      'mitolen': df.loc[:, 'mito_totlen'],\n",
    "                      'media': df.loc[:, 'media']})\n",
    "\n",
    "dfvol['mitovol'] = np.pi * (.15)**2 * dfvol.mitolen\n",
    "dfvol['Vol Ratio'] = dfvol.mitovol / dfvol.Vol\n",
    "\n",
    "#   Topology  (by cell)\n",
    "dfcellcon = df.ix[:, ['mito_cell_avedyr',\n",
    "                      'cell_coefvar_r',\n",
    "                      'mito_beta_geo',\n",
    "                      'mito_beta_top',\n",
    "                      'mito_phi',\n",
    "                      'mito_pk3',\n",
    "                      'mito_avgdeg',\n",
    "                      'mito_edgenum']]\n",
    "dfcellcon = pd.concat([dfcellcon,\n",
    "                       dfvol.loc[:, 'Vol Ratio'],\n",
    "                       df.loc[:, 'media']], axis=1)\n",
    "dfcellcon = dfcellcon[dfcellcon.mito_cell_avedyr <= 2000]  # exclude hi YPE's\n",
    "dfcellcon[r'$\\Delta \\Psi$ Unscaled'] = dfcellcon[\"mito_cell_avedyr\"]\n",
    "dfcellcon['Number of Edges'] = dfcellcon.mito_edgenum\n",
    "dfcellcon['Average Degree'] = dfcellcon.mito_avgdeg\n",
    "dfcellcon['O2 per mito vol'] = ''\n",
    "dfcellcon['OCR per cell'] = ''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting O2 respiration rate and mitochondrial volume ratio as a function of carbon sources show that cells in fermentation conditions (YPD) have lower mitochondrial respiration rate and content. For cells grown in respiratory conditions (YPE, YPL and YPR) the relationship between mitochondrial content and respiration levels is mixed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1f2d1cc0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xhAkTcOjQIQwbNgw3b95EXl4egoKCcOPGDRw/fhwxMTHQarVITk7GlClTjI6JKhcTXTVa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\n/BHtRQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1edb2550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax0) = plt.subplots(2, 1,\n",
    "                             figsize=(4.5, 3.5),\n",
    "                             sharex=True)\n",
    "sns.violinplot('media',\n",
    "               'Vol Ratio',\n",
    "               data=dfcellcon,\n",
    "               hue='media',\n",
    "               ax=ax0)\n",
    "ax0.set_xlabel('')\n",
    "ax0.set_ylabel('Volume Ratio')\n",
    "ax0.get_legend().set_visible(False)\n",
    "\n",
    "A = dfo2.groupby('type').quantile(0.025).values.flatten()\n",
    "B = dfo2.groupby('type').quantile(0.975).values.flatten()\n",
    "C = dfo2.groupby('type').quantile(0.5).values.flatten()\n",
    "g = sns.barplot(x='type',\n",
    "                y='OCRmito',\n",
    "                estimator=np.median,\n",
    "                ci=None,\n",
    "                ecolor=[.25, .25, .25],\n",
    "                data=dfo2,\n",
    "                yerr=[C-A, B-C],\n",
    "                ax=ax1)\n",
    "ax1.set_ylabel(r'OCR per mito vol /$\\mu m^{3}$')\n",
    "ax1.set_xlabel('')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cells in fermentation conditions (YPD) have lower mitochondrial ΔΨ, O2 and mitochondrial content compared to those in respiration. For cells grown in respiratory conditions (YPE, YPL and YPR) the relationship between mitochondrial content, respiration and ΔΨ is mixed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GDBmCn376yep209LSEB0djcjISKxateqB57VaLcaPH4+oqChMnDgRhYWFAACDwYAFCxYg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\nPKIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x164ce860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ([ax0, ax1], [ax3, ax2]) = plt.subplots(2, 2,\n",
    "                                  figsize=(5.5, 4.25),\n",
    "                                  sharex=True)\n",
    "ax0.yaxis.set_visible(False)\n",
    "ax0.grid(False)\n",
    "\n",
    "\n",
    "g = sns.barplot(x='type',\n",
    "                y='OCRmito',\n",
    "                estimator=np.median,\n",
    "                ci=None,\n",
    "                ecolor=[.25, .25, .25],\n",
    "                data=dfo2,\n",
    "                yerr=[C-A, B-C],\n",
    "                ax=ax1)\n",
    "\n",
    "ax1.set_ylabel(r'OCR per mito vol /$\\mu m^{3}$')\n",
    "ax1.set_xlabel('')\n",
    "ax1.set_yticks(np.arange(0, .015, .005))\n",
    "\n",
    "sns.boxplot('media',\n",
    "               'mito_cell_avedyr',\n",
    "               data=dfcellcon,\n",
    "               notch=True,\n",
    "               #hue='media',\n",
    "               ax=ax2)\n",
    "ax2.set_ylabel(r'$\\Delta \\Psi$ ')\n",
    "ax2.set_ylim(0, 2000)\n",
    "ax2.set_xlabel('')\n",
    "#ax2.get_legend().set_visible(False)\n",
    "\n",
    "sns.boxplot('media',\n",
    "               'Vol Ratio',\n",
    "               data=dfcellcon,\n",
    "               #hue='media',\n",
    "               notch=True,\n",
    "               #estimator=np.median,\n",
    "               ax=ax3)\n",
    "ax3.set_ylabel('Vol Ratio')\n",
    "ax3.set_ylim(0)\n",
    "ax3.set_xlabel('')\n",
    "#ax3.get_legend().set_visible(False)\n",
    "sns.despine(bottom=True)\n",
    "plt.tight_layout(w_pad=3)\n",
    "plt.savefig('panel.svg')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "    \n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}