THN20180917_MAT_STE12doseresponse_triplicates.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# compile triplicates\n",
    "# STE12 dose triplicates\n",
    "# dilution, sample every 25 minutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from FlowCytometryTools import *\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import glob\n",
    "import seaborn as sns\n",
    "import pickle as pkl\n",
    "from scipy.interpolate import interp1d\n",
    "from scipy import stats\n",
    "\n",
    "import os\n",
    "import sys\n",
    "\n",
    "path = \"C:/Users/tayng/Documents/PythonScripts/cytometer\"\n",
    "\n",
    "if not path in sys.path:\n",
    "    sys.path.insert(1, path)\n",
    "    os.chdir(path)\n",
    "    from AHN_FlowTools import *\n",
    "del path\n",
    "\n",
    "sns.set_style('white')\n",
    "sns.set_style('ticks')\n",
    "%matplotlib inline\n",
    "\n",
    "os.chdir(\"C:/Users/tayng/Documents/PythonScripts/cytometer/CAGEKEY\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# update data directory\n",
    "datadir = os.path.normpath(\"C:/Users/tayng/Documents/Data/cytometer/CAGEKEY/THN20180917_MAT_STE12doseresponse_triplicates/\")+os.sep\n",
    " \n",
    "allfiles = {}\n",
    "date1 = '20180815'\n",
    "date2 = '20180829'\n",
    "date3 = '20180917'\n",
    "experiment = [date1, date2, date3]\n",
    "date = '20180815'\n",
    "\n",
    "for idx, exp in enumerate(experiment): \n",
    "    filepath = datadir + os.sep +'*.fcs'\n",
    "    files = glob.glob(filepath)\n",
    "    assert len(files)>0, \"No filenames recovered - check file path: \" + filepath\n",
    "    allfiles[exp] = [files[idx]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "allexp = {}\n",
    "\n",
    "for exp in experiment: # iterate through experiments (fcs files)\n",
    "    for f in allfiles[exp]:\n",
    "        allexp[exp] = FCMeasurement(ID = exp, datafile = f)\n",
    "        \n",
    "    allexp[exp].data = allexp[exp].data.loc[:, ['Time','SSC-H','FITC-H','mCherry-H','FSC-H']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# settings\n",
    "og_num = 1;        # number of outgrowth timepoints\n",
    "samp_pd = 10;      # length of time b/w samples in min\n",
    "summary_idx  = 1   # check summaryfile from labview\n",
    "tot_reactors = pd.Series(range(1,9)) \n",
    "\n",
    "# read summary file\n",
    "summaryfilepath = {}\n",
    "summaryfile = {}\n",
    "paths = {}\n",
    "tot_timepoints = {}\n",
    "paths[date1] = \"THN20180815_summaryfile_edited.csv\"\n",
    "paths[date2] = \"THN20180829_summaryfile.csv\"\n",
    "paths[date3] = \"THN20180917_summaryfile.csv\"\n",
    "\n",
    "for exp in experiment:\n",
    "    summaryfilepath[exp] = os.path.normpath(datadir + paths[exp])\n",
    "    summaryfile[exp] = pd.read_csv(summaryfilepath[exp], names=['timepoint','valve','start','end'])\n",
    "    summaryfile[exp]['valve'] = summaryfile[exp]['valve'].astype(int)\n",
    "    summaryfile[exp]['timepoint'] = summaryfile[exp]['timepoint'].astype(int)\n",
    "\n",
    "tot_timepoints = pd.Series(range(0,31))\n",
    "\n",
    "# create sample_order (valve # : order #)\n",
    "valves = [5, 4, 7, 6, 11, 12, 2, 1]\n",
    "order = [1, 2, 3, 4, 5, 6, 7, 8] # update\n",
    "sample_order = {}\n",
    "\n",
    "for i in range(len(valves)):\n",
    "    sample_order[valves[i]] = order[i]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# legend \n",
    "labels = ['CL pFIG1 25nM',\n",
    "          'CL pFIG1 12.5nM',\n",
    "          'CL pFIG1 6.25nM',\n",
    "          'CL pFIG1 3.13nM',\n",
    "          'OL pREV1 25nM',\n",
    "          'OL pREV1 12.5nM',\n",
    "          'OL pREV1 6.25nM',\n",
    "          'OL pREV1 3.13nM'];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# create binned_data\n",
    "binned_data = {}\n",
    "for exp in experiment:\n",
    "    binned_data[exp] = pd.DataFrame(index=tot_reactors,columns=tot_timepoints)\n",
    "\n",
    "for exp in experiment:\n",
    "    for i, row in enumerate(summaryfile[exp].values): # iterate through summary file\n",
    "        d = allexp[exp].data[allexp[exp].data.Time.between(summaryfile[exp].iloc[i,2],summaryfile[exp].iloc[i,3])]\n",
    "        timepoint = summaryfile[exp].iloc[i,0] - summary_idx \n",
    "        if timepoint < 31:\n",
    "            reactor_num = sample_order[summaryfile[exp].iloc[i,1]] # sample order gives the order of reactor (indexed at valve num)\n",
    "            binned_data[exp].set_value(reactor_num, timepoint, d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot some things!!! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# delete outgrowth timepoint(s)\n",
    "t = ((tot_timepoints[:-(1)])*samp_pd)/60.0 \n",
    "t_cut = t[0:30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_color():\n",
    "    color=iter(sns.color_palette('hls',8))\n",
    "    return(color)\n",
    "\n",
    "def get_YFP_color():\n",
    "    CL_color = iter(sns.color_palette(sns.color_palette(\"YlOrRd_r\", 8)))\n",
    "    return(CL_color)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot norm data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_norm_data(exp, reactor_num, timepoint, datatype):\n",
    "    tp_data = binned_data[exp].loc[reactor_num, timepoint]\n",
    "    tp_data = tp_data[tp_data[datatype] > 0] # filter out cells that have no FITC output\n",
    "    tp_data = tp_data[tp_data['SSC-H'] > 1000]\n",
    "    \n",
    "    tot_events = tp_data.size\n",
    "    time = np.median(tp_data.loc[:,'Time'])\n",
    "    reading = tp_data.loc[:, datatype]\n",
    "    \n",
    "    raw_data = np.median(reading)\n",
    "    ssc = tp_data.loc[:,'SSC-H']\n",
    "    d = pd.Series(reading/ssc)\n",
    "            \n",
    "    d.dropna(how='any')    #to drop if all values in the row are nan\n",
    "    yerr = np.std(d)\n",
    "    d = np.median(d)\n",
    "    return(d, raw_data, time, yerr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# get normalized data data\n",
    "rows = tot_reactors\n",
    "cols = tot_timepoints\n",
    "empty = pd.DataFrame(index = rows, columns = cols)\n",
    "\n",
    "FITC = {}\n",
    "for exp in experiment:\n",
    "    FITC[exp] = pd.Panel({'time':empty, 'raw':empty, 'med':empty, 'sd':empty, 'ravg':empty})\n",
    "\n",
    "    for reactor_num in tot_reactors:    \n",
    "        for timepoint in tot_timepoints:\n",
    "        \n",
    "            data, raw_data, time, yerr = get_norm_data(exp, reactor_num, timepoint, 'FITC-H')\n",
    "            FITC[exp].time.set_value(reactor_num, timepoint, time)\n",
    "            FITC[exp].raw.set_value(reactor_num, timepoint, raw_data)\n",
    "            FITC[exp].med.set_value(reactor_num, timepoint, data)\n",
    "            FITC[exp].sd.set_value(reactor_num, timepoint, yerr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# get mean and sd data\n",
    "FITC_all = {}\n",
    "FITC_all = pd.Panel({'time':empty, 'med':empty, 'sd':empty, 'ravg':empty})\n",
    "\n",
    "for reactor_num in [1,2,3,4,5,6,7,8]:\n",
    "    for timepoint in tot_timepoints:\n",
    "        d1 = FITC[date1].med.loc[reactor_num, timepoint]\n",
    "        d2 = FITC[date2].med.loc[reactor_num, timepoint]\n",
    "        d3 = FITC[date3].med.loc[reactor_num, timepoint]\n",
    "        davg = np.mean([d1, d2, d3], dtype=np.float64)\n",
    "        dyerr = np.std([d1, d2, d3], dtype=np.float64)\n",
    "\n",
    "        FITC_all.med.set_value(reactor_num, timepoint, davg)\n",
    "        FITC_all.sd.set_value(reactor_num, timepoint, dyerr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tayng\\Anaconda2\\lib\\site-packages\\ipykernel_launcher.py:5: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=3,center=True).mean()\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "def rolling_mean(readout, reactor_num, N):\n",
    "    t_last = len(t)\n",
    "\n",
    "    if readout == 'FITC-H':\n",
    "        ravg = pd.rolling_mean(FITC_all.med.loc[reactor_num, :], N, center=True)\n",
    "        ravg[0] = (FITC_all.med.loc[reactor_num, 0]+FITC_all.med.loc[reactor_num, 1])/2\n",
    "        ravg[t_last] = (FITC_all.med.loc[reactor_num, t_last-1]+FITC_all.med.loc[reactor_num, t_last])/2\n",
    "        \n",
    "    return(ravg)\n",
    "\n",
    "window = 3;\n",
    "for reactor_num in range(1,9):\n",
    "    FITC_all.ravg.loc[reactor_num, :] = rolling_mean('FITC-H',reactor_num, window)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot on log10 yscale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9OT5NVxBpgbbvJk804fBfgWEvbFOuBZGyEleplOKt0Kl0f8YyIlT5LUYiXk5H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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6176048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot on log 10 scale output over time\n",
    "fig, ax = plt.subplots(4, 1, sharex = True, sharey = False, figsize = (2.5, 8))\n",
    "ax = ax.ravel()\n",
    "\n",
    "YFP_color = get_YFP_color()\n",
    "YFP_color_OL = iter((sns.color_palette(\"Greys_r\", 6)))\n",
    "\n",
    "for i in range(4):\n",
    "    c_YFP = next(YFP_color)\n",
    "    \n",
    "for i in range(2):\n",
    "    c_YFP_OL = next(YFP_color_OL)\n",
    "\n",
    "ms = 4; lw = 1.; mew = 1; cs = 1.5; ct = 1.;\n",
    "\n",
    "for i in range(1,5):     \n",
    "\n",
    "    c_YFP = next(YFP_color)\n",
    "    ax[i-1].errorbar(t_cut, FITC_all.med.loc[i, 1:30], FITC_all.sd.loc[reactor_num, 1:30], linestyle='none', marker='o', \n",
    "                     markersize=ms, capsize=cs, capthick=ct, color = c_YFP)\n",
    "    ax[i-1].plot(t, FITC_all.ravg.loc[i, 1:33], label='_nolegend_', linewidth = lw, color=c_YFP)\n",
    "\n",
    "    \n",
    "    c_YFP_OL = next(YFP_color_OL)\n",
    "    ax[i-1].errorbar(t_cut, FITC_all.med.loc[i+4, 1:30], FITC_all.sd.loc[reactor_num, 1:30], linestyle='none', \n",
    "                    marker='o', markersize=ms, fillstyle = 'none', capsize=cs, capthick=ct, color = c_YFP_OL,  \n",
    "                    markeredgewidth=mew, markerfacecolor = 'none', markeredgecolor=c_YFP_OL)\n",
    "    ax[i-1].plot(t, FITC_all.ravg.loc[i+4, 1:33], label='_nolegend_', linewidth = lw, color=c_YFP_OL)\n",
    "\n",
    "    \n",
    "for i in range(4):\n",
    "    ax[i].set_yscale('log')\n",
    "    ax[i].spines['right'].set_visible(False)\n",
    "    ax[i].spines['top'].set_visible(False)\n",
    "    ax[i].set_xlim([-0.5, 5.2])            \n",
    "\n",
    "ax[1].set_xticks(np.arange(0, 6, step=1))\n",
    "ax[3].set_xlabel('Time (hrs)')  \n",
    "ax[3].set_ylabel('Normalized YFP')\n",
    "\n",
    "#ax[0].legend(['FB 25nM','no FB 25nM'], bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "#ax[1].legend(['FB 12.5nM','no FB 12.5nM'], bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "#ax[2].legend(['FB 6.25nM','no FB 6.25nM'], bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "#ax[3].legend(['FB 3.13nM','no FB 3.13nM'], bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "\n",
    "\n",
    "dest = datadir + 'pyfig\\\\' + date + '_forExtendedDataFig2'\n",
    "plt.savefig(dest + '.png', bbox_inches='tight',dpi=300)\n",
    "plt.savefig(dest + '.svg', bbox_inches='tight',dpi=300)\n",
    "\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZCZ29ctagvfIt33ugXPv9/nPQYyCTHleWUNc1erFipS87Pek1zg5UFIWGhoaID7AcNeEs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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x417496a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot normalized to OL control\n",
    "fig, ax = plt.subplots(figsize = (2.2, 2))\n",
    "YFP_color = get_YFP_color()\n",
    "\n",
    "for i in range(4):\n",
    "    c_YFP = next(YFP_color)\n",
    "\n",
    "ms = 5; lw = 1.5; mew = 1; cs = 2; ct = 1.5;\n",
    "\n",
    "reactors_to_plot = [5,1];\n",
    "for reactor_num in reactors_to_plot:     \n",
    "    OL_ctrl = FITC_all.med.loc[5, 0];\n",
    "    \n",
    "    if reactor_num == 1:\n",
    "        c_YFP = next(YFP_color)\n",
    "        ax.errorbar(t_cut, FITC_all.med.loc[reactor_num, 1:30]/OL_ctrl, FITC_all.sd.loc[reactor_num, 1:30]/OL_ctrl, elinewidth=1,\n",
    "                    linestyle='none', \n",
    "                    marker='o', markersize=ms, capsize=cs, capthick=ct, color = c_YFP)\n",
    "        ax.plot(t, FITC_all.ravg.loc[reactor_num, 1:33]/OL_ctrl, label='_nolegend_', linewidth = lw, color=c_YFP)\n",
    "\n",
    "    if reactor_num == 5: \n",
    "        ax.errorbar(t_cut, FITC_all.med.loc[reactor_num, 1:30]/OL_ctrl, FITC_all.sd.loc[reactor_num, 1:30]/OL_ctrl, elinewidth=1, \n",
    "                    linestyle='none', marker='o', capsize=cs, capthick=ct, color = 'grey', markersize = ms, markeredgewidth=1, \n",
    "                    markerfacecolor = 'none', markeredgecolor='grey')\n",
    "        ax.plot(t, FITC_all.ravg.loc[reactor_num, 1:33]/OL_ctrl, label='_nolegend_', linewidth = lw, color='grey')\n",
    "\n",
    "#ax.set_yscale('log')\n",
    "ax.set_xticks(np.arange(0, 6, step=1))\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "#ax.set_ylabel('Normalized YFP')\n",
    "ax.set_xlim([-0.5, 5.2])  \n",
    "#ax.set_ylim([.05, 9])\n",
    "#ax.legend(['CL 25nM','CL 6.25nM','CL 3.13nM','OL 25nM'], bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "\n",
    "dest = datadir + 'pyfig\\\\' + date + '_HIdose_OLnorm_YFPtriplicates'\n",
    "plt.savefig(dest + '.png', bbox_inches='tight',dpi=300)\n",
    "plt.savefig(dest + '.svg', bbox_inches='tight',dpi=300)\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x409feda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot on linear scale\n",
    "fig, ax = plt.subplots(figsize = (2., 1.5))\n",
    "YFP_color = get_YFP_color()\n",
    "\n",
    "for i in range(4):\n",
    "    c_YFP = next(YFP_color)\n",
    "\n",
    "ms = 4; lw = 1.5; mew = 1; cs = 2; ct = 1.5;\n",
    "\n",
    "reactors_to_plot = [5,1];\n",
    "for reactor_num in reactors_to_plot:     \n",
    "    \n",
    "    if reactor_num == 1:\n",
    "        c_YFP = next(YFP_color)\n",
    "        ax.errorbar(t_cut, FITC_all.med.loc[reactor_num, 1:30], FITC_all.sd.loc[reactor_num, 1:30], elinewidth=1,\n",
    "                    linestyle='none', \n",
    "                    marker='o', markersize=ms, capsize=cs, capthick=ct, color = c_YFP)\n",
    "        ax.plot(t, FITC_all.ravg.loc[reactor_num, 1:33], label='_nolegend_', linewidth = lw, color=c_YFP)\n",
    "\n",
    "    if reactor_num == 5: \n",
    "        ax.errorbar(t_cut, FITC_all.med.loc[reactor_num, 1:30], FITC_all.sd.loc[reactor_num, 1:30], elinewidth=1, \n",
    "                    linestyle='none', marker='o', capsize=cs, capthick=ct, color = 'grey', markersize = ms, markeredgewidth=1, \n",
    "                    markerfacecolor = 'none', markeredgecolor='grey')\n",
    "        ax.plot(t, FITC_all.ravg.loc[reactor_num, 1:33], label='_nolegend_', linewidth = lw, color='grey')\n",
    "\n",
    "#ax.set_yscale('log')\n",
    "ax.set_xticks(np.arange(0, 6, step=1))\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "#ax.set_ylabel('Normalized YFP')\n",
    "ax.set_xlim([-0.5, 5.2])  \n",
    "#ax.set_ylim([.05, 9])\n",
    "#ax.legend(['CL 25nM','CL 6.25nM','CL 3.13nM','OL 25nM'], bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "\n",
    "dest = datadir + 'pyfig\\\\' + date + '_HIdose_YFPtriplicates'\n",
    "plt.savefig(dest + '.png', bbox_inches='tight',dpi=300)\n",
    "plt.savefig(dest + '.svg', bbox_inches='tight',dpi=300)\n",
    "plt.show()\n",
    "plt.close()"
   ]
  }
 ],
 "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": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}