plots_fig_S5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Create plots for panels used in figure S5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import dclab\n",
    "from lin_mixed_model import linmixmod\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [],
   "source": [
    "result_folder = r\"data\\RTfDC\\WBC\"\n",
    "savefolder = r\"plots\\SI\\fig_S5\""
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Manually add means for missing data for LM-9w-20171107"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [],
   "source": [
    "data_LM_9w_lympho = {'date': '2017-11-07','events': 77, 'flow rate': 0.08, '% gated': 8.43,\n",
    "                     'median area_um': 39., 'mean area_um': 38., 'sd area_um': 5.5,\n",
    "                     'median deform': 0.0264, 'mean deform': 0.0316, 'sd deform': 0.0154}\n",
    "data_LM_9w_myelo = {'date': '2017-11-07','events': 47, 'flow rate': 0.08, '% gated': 5.15,\n",
    "                     'median area_um': 63.3, 'mean area_um': 64.1, 'sd area_um': 5.68,\n",
    "                     'median deform': 0.0319, 'mean deform': 0.0378, 'sd deform': 0.017}"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Plot results with gates from brighntess plot (created 20200521)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [],
   "source": [
    "lympho_ctrl_medians = {'area_um': 37.88789749145508,\n",
    "                       'deform': 0.04369187355041504,\n",
    "                       'emodulus': 0.7527109307300333}\n",
    "myelo_ctrl_medians = {'area_um': 64.04239654541016,\n",
    "                      'deform': 0.08041012287139893,\n",
    "                      'emodulus': 0.7098496091718063}"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "def plot_errorbars(patients, labels, cell_type, y_value, err_type='std', fontsize= 28, figsize=(8,6)):\n",
    "\n",
    "    flow_rate = 0.08\n",
    "\n",
    "    linewidth = 6\n",
    "    markersize = 15\n",
    "    #errorbar formatting\n",
    "    errbar_width = 8\n",
    "\n",
    "    color_dict = {'VS': 'C0', 'VL': 'C1', 'RS': 'C2',\n",
    "                  'KM': 'C3', 'LM': 'C4'}\n",
    "\n",
    "    if cell_type == \"lymphos\":\n",
    "        plot_title = \"Lymphocytes\"\n",
    "        ctrl_value = lympho_ctrl_medians[y_value]\n",
    "    elif cell_type == \"myelos\":\n",
    "        plot_title = \"Myelocytes\"\n",
    "        ctrl_value = myelo_ctrl_medians[y_value]\n",
    "\n",
    "    with sns.axes_style('darkgrid'):\n",
    "        fig = plt.figure(0, figsize)\n",
    "        ax1 = plt.subplot(111)\n",
    "\n",
    "        ax_xlabel = \"Day since treatment start\"\n",
    "        ax_ylabel = dclab.dfn.feature_name2label[y_value]\n",
    "\n",
    "        ax1.set_xlabel(ax_xlabel, fontsize=fontsize)\n",
    "        ax1.set_ylabel(ax_ylabel, fontsize=fontsize)\n",
    "\n",
    "        ax1.axhline(ctrl_value, c='darkgray', alpha=.75, ls='--', lw=1.5*linewidth, label='CTRL')\n",
    "\n",
    "        for ind, patient in enumerate(patients):\n",
    "\n",
    "            result_file = \"20200522_results_\" + patient + \"_\" + cell_type + \".tsv\"\n",
    "            result_path = os.path.join(result_folder, result_file)\n",
    "\n",
    "            df = pd.read_csv(result_path, sep='\\t')\n",
    "\n",
    "            if y_value=='emodulus':\n",
    "                emod = dclab.features.emodulus.get_emodulus(area_um=df['area_um'],\n",
    "                                                            deform=df['deform'],\n",
    "                                                            medium=\"CellCarrier\",\n",
    "                                                            channel_width=20.0,\n",
    "                                                            flow_rate=flow_rate)\n",
    "                df['emodulus'] = emod\n",
    "\n",
    "            df_date_grouped_mean = df.groupby(['date', 'flow rate']).median().reset_index()\n",
    "            if err_type=='std':\n",
    "                df_date_grouped_std = df.groupby(['date', 'flow rate']).std().reset_index()\n",
    "            if err_type=='sem':\n",
    "                df_date_grouped_sem = df.groupby(['date', 'flow rate']).sem().reset_index()\n",
    "\n",
    "            dates = pd.to_datetime(df_date_grouped_mean.reset_index()['date'])\n",
    "            day0=dates[0]\n",
    "\n",
    "            time = dates - day0\n",
    "\n",
    "            df_date_grouped_mean['delta_days'] = time.dt.days #convert datetime in ns to days\n",
    "            if err_type=='std':\n",
    "                df_date_grouped_std['delta_days'] = time.dt.days\n",
    "            if err_type=='sem':\n",
    "                df_date_grouped_sem['delta_days'] = time.dt.days\n",
    "\n",
    "            color = color_dict[patient]\n",
    "\n",
    "            #flow rate range from 0.004 - 0.02 ul/s\n",
    "            ii = np.round(df_date_grouped_mean['flow rate'], 3) == flow_rate\n",
    "            x = df_date_grouped_mean['delta_days'][ii]\n",
    "            y = df_date_grouped_mean[y_value][ii]\n",
    "            if err_type=='std':\n",
    "                yerr = df_date_grouped_std[y_value][ii]\n",
    "            if err_type=='sem':\n",
    "                yerr = df_date_grouped_sem[y_value][ii]\n",
    "\n",
    "            x = np.array(x)\n",
    "            y = np.array(y)\n",
    "            yerr = np.array(yerr)\n",
    "\n",
    "            if patient=='LM':\n",
    "                if cell_type == 'lymphos':\n",
    "                    dict_LM_9w = data_LM_9w_lympho\n",
    "                if cell_type == 'myelos':\n",
    "                    dict_LM_9w = data_LM_9w_myelo\n",
    "\n",
    "                date_LM_9w = pd.to_datetime(dict_LM_9w['date'])\n",
    "                time_LM_9w = (date_LM_9w - day0).days\n",
    "\n",
    "                if y_value=='emodulus':\n",