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{
"cells": [
{
"cell_type": "markdown",
"source": [
"# Create plots for panels used in figure S1"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from scipy.optimize import curve_fit\n",
"\n",
"import warnings\n",
"warnings.filterwarnings('ignore')"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
"outputs": [],
"source": [
"# folder to save all panels for figure S1\n",
"savefolder = r\"plots\\SI\\fig_S1\"\n",
"\n",
"# file containing the data for the controls\n",
"results_ctrl_file = r\"data\\shape_analysis\\histograms_HealthyControl_deformed_undeformed.txt\""
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "markdown",
"source": [
"## A"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
}
},
{
"cell_type": "code",
"outputs": [],
"source": [
"#define a color seed for each patient\n",
"color_dict = {'VS': 'C0', 'VL': 'C1', 'RS': 'C2',\n",
" 'KM': 'C3', 'LM': 'C4'}"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"source": [
"def asymptotic_exponential_growth(x, lambda_):\n",
" \"\"\"(Inverted) exponential growth function with maximum at 1 for x->infinity:\n",
" f(x) = 1 - exp(-lambda * x)\"\"\"\n",
" return 1 - np.exp(-lambda_ * x)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
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"source": [
"def weighted_means_and_errors(counts, counts_abs):\n",
" \"\"\"Calculate weighted mean values and error from the input arrays\"\"\"\n",
"\n",
" weights = np.zeros_like(counts)\n",
" weights_normed = np.zeros_like(counts)\n",
"\n",
" # sum up number of cell in each velocity range, for every measurement\n",
" counts_sum = np.sum(counts_abs, axis = 2)\n",
" sum_counts = np.sum(counts_sum, axis = 1)\n",
"\n",
" # calculate absolute and relative weights\n",
" for k in range(len(weights[0,:,0])):\n",
" weights[:,k,:] = np.sum(counts_abs, axis = 1)\n",
" weights_normed[:,k,:] = weights[:,k,:] / sum_counts[k] #needed for calculation of the error\n",
"\n",
" # calculate weighted averages, mask nans\n",
" means = np.ma.average(counts, axis = 2, weights = weights)\n",
" means = np.array(means) #remove mask again\n",
"\n",
" # calculate weighted variance and from that the error\n",
" var = np.zeros_like(means)\n",
"\n",
" for l in range(len(counts[0,0,:])):\n",
" var += (counts[:,:,l]-means)**2 * weights_normed[:,:,l]\n",
"\n",
" errs = np.sqrt(var)\n",
"\n",
" # make sure that probability sums up to 1\n",
" for i in range(len(means)):\n",
" means[i,:] = means[i,:]/sum(means[i,:])\n",
"\n",
" return means, errs\n",
"\n",
"def deformed_probability_curve(df, v_min=0, v_max=3, binsize=.25):\n",
" \"\"\"Compute the values for the shape probability diagram to find a cell\n",
" in a deformed state for velocities between v_min and v_max in the DataFrame df\n",
"\n",
" returns: *tuple* (deformed_bins, deformed_hist_normal)\n",
" - deformed_bins: *array* limits for the bin ranges of the histogram\n",
" - normalized counts for each velocity range\n",
" \"\"\"\n",
"\n",
" bins = int(v_max/binsize) #number of Bins in histogram\n",
" #find index of cells in a deformed state. Class definitions are:\n",
" #1-parachute, 2-slipper, 3-asym. parachute, 5-multilobe, 7-undefined deformed\n",
" #4-discocyte/undeformed, 6-tumbler\n",
" deformed_index = ((df['shape'] == 1)\n",
" | (df['shape'] == 2)\n",
" | (df['shape'] == 3)\n",
" | (df['shape'] == 5)\n",
" | (df['shape'] == 7))\n",
"\n",
" #create new column in df that is True for deformed state\n",
" df['deformed'] = False\n",
" df['deformed'][deformed_index] = True\n",
"\n",
" df_deformed = df[deformed_index]\n",
"\n",
" deformed_hist, deformed_bins = np.histogram(np.array(df_deformed['velocity']),\n",
" range = (v_min,v_max),\n",
" bins = bins)\n",
" #get the counts for all events to use for normalization\n",
" all_hist, all_bins = np.histogram(np.array(df['velocity']),\n",
" range = (v_min,v_max),\n",
" bins = bins)\n",
"\n",
" #normalize the deformed histogram\n",
" deformed_hist_normal = deformed_hist/all_hist\n",
"\n",
" return deformed_bins, deformed_hist_normal"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"text/plain": "<Figure size 504x432 with 1 Axes>",
"image/png": 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\n"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"warnings.filterwarnings('ignore')\n",
"\n",
"patients = ['VS', 'VL', 'RS', 'LM', 'KM']\n",
"labels = [\"P1\", \"P2\", \"P3\", \"P4\", \"P5\"]\n",
"\n",
"result_summary_folder = r\"data\\shape_analysis\\result_summaries\"\n",
"xlabel = \"Velocity [mm/s]\"\n",
"ylabel = \"Probability for deformed shape\"\n",
"v_min = 0.\n",
"v_max = 3.\n",
"binsize = 0.25\n",
"\n",
"with sns.axes_style('darkgrid'):\n",
"\n",
" #plot control curves\n",
" #load data from txt file\n",
" results_ctrl = np.loadtxt(results_ctrl_file)\n",
"\n",
" v_ctrl = results_ctrl[:,0]\n",
" probs_ctrl = results_ctrl[:,3]\n",
" probs_ctrl_err = results_ctrl[:,4]\n",
"\n",
" #fit the control data\n",
"\n",
" ind_vmax = v_ctrl <= v_max\n",
" v_ctrl = v_ctrl[ind_vmax]\n",
" probs_ctrl = probs_ctrl[ind_vmax]\n",
" probs_ctrl_err = probs_ctrl_err[ind_vmax]\n",
"\n",
" popt_ctrl, pcov_ctrl = curve_fit(asymptotic_exponential_growth, v_ctrl, probs_ctrl,\n",
" sigma = probs_ctrl_err, absolute_sigma=False,\n",
" bounds = fit_bounds)\n",
" ax = plt.subplot(111)\n",
" plt.errorbar(v_ctrl, probs_ctrl, probs_ctrl_err,\n",
" color=color_ctrl, lw=lw, alpha=alpha_ctrl, label='CTRL', zorder=1)\n",
" plt.plot(v_ctrl_plot_fit, asymptotic_exponential_growth(v_ctrl_plot_fit, *popt_ctrl),\n",
" '--', color=color_ctrl_fit, lw=lw_ctrl,# zorder=0,\n",
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" alpha=alpha_ctrl_fit, label='CTRL fit')\n",
"\n",
" for ii, patient in enumerate(patients):\n",
"\n",
" result_file = os.path.join(result_summary_folder, patient + \"_results_MCFM.tsv\")\n",
" df_results = pd.read_csv(result_file, sep='\\t')\n",
"\n",
" dates = np.unique(df_results['date'])\n",
" dates = np.sort(dates)\n",
"\n",
" day0 = dates[0]\n",
"\n",
" color = color_dict[patient]\n",
"\n",
" df_date = df_results[df_results['date']==day0]\n",
" #create new Dataframe to work with, leave out skipped cells\n",
" df = df_date[df_date['shape'] != 0]\n",
"\n",
" bins, deformed_curve = deformed_probability_curve(df, v_min=v_min, v_max=v_max, binsize=binsize)\n",
" bins_plot = bins[:-1]+binsize/2\n",
"\n",
" ax.plot(bins_plot, deformed_curve, c=color, lw=lw, label=labels[ii])\n",
"\n",
" ax.set_xlim(0,3)\n",
" ax.set_ylim(0,1.1)\n",
" ax.set_xlabel(xlabel, fontsize = fontsize)\n",
" ax.set_ylabel(ylabel, fontsize = fontsize)\n",
" ax.tick_params(axis='both', which='both', labelsize=fontsize)\n",
" ax.set_title('All RBCs', fontsize = fontsize+2)\n",
" ax.legend(ncol=2, fontsize=fontsize-6)\n",
"\n",
" handles, labels = ax.get_legend_handles_labels()\n",
" handles = np.roll(handles,-1)\n",
" labels = np.roll(labels, -1)\n",
" ax.legend(handles=list(handles), labels=list(labels), fontsize=fontsize-2, ncol=2)\n",
" savename = \"fig_S1A_shape_analysis_baseline_all_rbcs\"\n",
" savepath = os.path.join(savefolder,savename)\n",
"\n",
" plt.savefig(savepath+\".pdf\", dpi=900, format='pdf')"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "markdown",
"source": [
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
}
},
{
"cell_type": "code",
"outputs": [],
"source": [
"#define dict to store fit values\n",
"dict_fitvalues = {}\n",
"\n",
"def dict_fit_values_patient(patient, dict_fitvalues):\n",
" result_summary_folder = r\"data\\shape_analysis\\result_summaries\"\n",
"\n",
" v_min = 0.\n",
" v_max = 3.\n",
" binsize = 0.25\n",
"\n",
" # bounds of the parameters in the exponential growth function\n",
" fit_bounds=(0, np.inf)\n",
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"\n",
" result_file = os.path.join(result_summary_folder, patient + \"_results_MCFM.tsv\")\n",
" df_results = pd.read_csv(result_file, sep='\\t')\n",
"\n",
" dates = np.unique(df_results['date'])\n",
" dates = np.sort(dates)\n",
" day0 = pd.to_datetime(dates[0])\n",
"\n",
" #create dataframes to save fit parameters\n",
" df_fit_all = pd.DataFrame()\n",
" df_fit_healthy = pd.DataFrame()\n",
" df_fit_unhealthy = pd.DataFrame()\n",
"\n",
" for num, date in enumerate(dates):\n",
" df_date = df_results[df_results['date']==date]\n",
" #create new Dataframe to work with, leave out skipped cells\n",
" df = df_date[df_date['shape'] != 0]\n",
"\n",
" healthy_index = df['health'] == 0\n",
" df_healthy = df[healthy_index]\n",
" unhealthy_index = df['health'] == 1\n",
" df_unhealthy = df[unhealthy_index]\n",
"\n",
" #calculate percentage of healthy cells in sample\n",
" percentage_healthy = len(df_healthy)/len(df)\n",
"\n",
" bins, deformed_curve = deformed_probability_curve(df, v_min=v_min, v_max=v_max, binsize=binsize)\n",
" bins_healthy, deformed_curve_healthy = deformed_probability_curve(df_healthy,\n",
" v_min=v_min, v_max=v_max, binsize=binsize)\n",
" bins_unhealthy, deformed_curve_unhealthy = deformed_probability_curve(df_unhealthy,\n",
"\n",
" bins_plot = bins[:-1]+binsize/2\n",
"\n",
" #exclude nan values before fitting\n",
" ind_nonnan_all = ~np.isnan(deformed_curve)\n",
" ind_nonnan_healthy = ~np.isnan(deformed_curve_healthy)\n",
" ind_nonnan_unhealthy = ~np.isnan(deformed_curve_unhealthy)\n",
"\n",
" x_all = bins_plot[ind_nonnan_all]\n",
" y_all = deformed_curve[ind_nonnan_all]\n",
" x_healthy = bins_plot[ind_nonnan_healthy]\n",
" y_healthy = deformed_curve_healthy[ind_nonnan_healthy]\n",
" x_unhealthy = bins_plot[ind_nonnan_unhealthy]\n",
" y_unhealthy = deformed_curve_unhealthy[ind_nonnan_unhealthy]\n",
"\n",
" popt_all_exp, pcov_all_exp = curve_fit(asymptotic_exponential_growth,\n",
" x_all, y_all,\n",
" bounds=fit_bounds\n",
" )\n",
" popt_healthy_exp, pcov_healthy_exp = curve_fit(asymptotic_exponential_growth,\n",
" x_healthy, y_healthy,\n",
" bounds=fit_bounds\n",
" )\n",
" popt_unhealthy_exp, pcov_unhealthy_exp = curve_fit(asymptotic_exponential_growth,\n",
" x_unhealthy, y_unhealthy,\n",
" bounds=fit_bounds\n",
" )\n",
" #days since treatment start\n",
" treatment_days = (pd.to_datetime(date) - day0).days\n",
"\n",
" df_fit_all = df_fit_all.append({'lambda': popt_all_exp[0], 'lambda_err': np.sqrt(pcov_all_exp[0,0]),\n",
" 'days': treatment_days,\n",
" 'percent healthy': percentage_healthy\n",
" },\n",
" df_fit_healthy = df_fit_healthy.append({'lambda': popt_healthy_exp[0], 'lambda_err': np.sqrt(pcov_healthy_exp[0,0]),\n",
" 'days': treatment_days\n",
" },\n",
" ignore_index=True)\n",
" df_fit_unhealthy = df_fit_unhealthy.append({'lambda': popt_unhealthy_exp[0], 'lambda_err': np.sqrt(pcov_unhealthy_exp[0,0]),\n",
" 'days': treatment_days\n",
" },\n",
" ignore_index=True)\n",
"\n",
" dict_fitvalues[patient] = {'all': df_fit_all, 'healthy': df_fit_healthy, 'unhealthy': df_fit_unhealthy}\n",
"\n",
" return dict_fitvalues"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "markdown",
"source": [
"Fill dictionary with patient data"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"outputs": [],
"source": [
"patients = ['VS', 'VL', 'RS', 'LM', 'KM']\n",
"labels = [\"P1\", \"P2\", \"P3\", \"P4\", \"P5\"]\n",
"\n",
"for patient in patients:\n",
" dict_fitvalues = dict_fit_values_patient(patient, dict_fitvalues)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"image/png": 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\n"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"markersize = 12\n",
"errbar_width = 5\n",
"xlabel = 'Day since treatment start'\n",
"\n",
"# color for the control interval\n",
"ctrl_clr = 'darkslategray'\n",
"\n",
"# compute control fit values\n",
"results_ctrl = np.loadtxt(results_ctrl_file)\n",
"\n",
"v_ctrl = results_ctrl[:,0]\n",
"probs_ctrl = results_ctrl[:,3]\n",
"probs_ctrl_err = results_ctrl[:,4]\n",
"\n",
"v_min = 0.\n",
"v_max = 3.\n",
"binsize = 0.25\n",
"\n",
"ind_vmax = v_ctrl <= v_max\n",
"v_ctrl = v_ctrl[ind_vmax]\n",
"probs_ctrl = probs_ctrl[ind_vmax]\n",
"probs_ctrl_err = probs_ctrl_err[ind_vmax]\n",
"\n",
"fit_bounds = [0, np.inf]\n",
"popt_ctrl, pcov_ctrl = curve_fit(asymptotic_exponential_growth, v_ctrl, probs_ctrl,\n",
" sigma = probs_ctrl_err, absolute_sigma=False,\n",
" bounds=fit_bounds\n",
" )\n",
"perr_ctrl = np.sqrt(np.diag(pcov_ctrl))\n",
"# limits of the 95% confidence interval\n",
"ci_lower = float(popt_ctrl - perr_ctrl)\n",
"ci_upper = float(popt_ctrl + perr_ctrl)\n",
" plot_title = 'All RBCs'\n",
" para = 'lambda'\n",
" para_label = r'$\\lambda$'\n",
" ylim = [0, 2.7]\n",
" health = 'all'\n",
"\n",
" for jj, patient in enumerate(patients):\n",
" data = dict_fitvalues[patient]\n",
" color = color_dict[patient]\n",
"\n",
" ax=plt.subplot(111)\n",
"\n",
" df_plot = data[health]\n",
" xdata = df_plot['days']\n",
" ydata = df_plot[para]\n",
" yerr = df_plot[para + \"_err\"]\n",
"\n",
" # plot data on treatment\n",
" plt.errorbar(xdata[:-1], ydata[:-1], yerr=yerr[:-1],\n",
" c=color, label=labels[jj],\n",
" ls='-', lw=linewidth, marker='X', markersize=markersize,\n",
" ecolor='gray', elinewidth=errbar_width)\n",
"\n",
" # plot data off treatment\n",
" plt.errorbar(xdata[-2:], ydata[-2:], yerr=yerr[-2:],\n",
" c=color, ls='--', lw=linewidth, marker='X', markersize=markersize,\n",
" ecolor='gray', elinewidth=errbar_width)\n",
"\n",
" plt.ylim(ylim)\n",
" plt.xlabel(xlabel, fontsize=fontsize)\n",
" plt.tick_params(axis='both', which='both', labelsize=fontsize-2)\n",
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" plt.title(r'{} - {}'.format(para_label, plot_title), fontsize=fontsize+2)\n",
"\n",
" # plot control region at end only\n",
" if patient==patients[-1]:\n",
" if health=='unhealthy':\n",
" ax.axhline(ci_lower, ls='--', lw=.5, c=ctrl_clr, zorder=0)\n",
" ax.axhline(ci_upper, ls='--', lw=.5, c=ctrl_clr, zorder=0)\n",
" axis_limits = ax.get_xlim()\n",
" ax.add_patch(patches.Rectangle((axis_limits[0], ci_lower),\n",
" np.diff(axis_limits), ci_upper-ci_lower,\n",
" color=ctrl_clr, alpha=0.15, zorder=0,\n",
" label = 'CTRL'\n",
" )\n",
" )\n",
" ax.get_yaxis().set_ticklabels([])\n",
"\n",
" else:\n",
" ax.axhline(ci_lower, ls='--', lw=.5, c=ctrl_clr, zorder=0)\n",
" ax.axhline(ci_upper, ls='--', lw=.5, c=ctrl_clr, zorder=0)\n",
" axis_limits = ax.get_xlim()\n",
" ax.add_patch(patches.Rectangle((axis_limits[0], ci_lower),\n",
" np.diff(axis_limits), ci_upper-ci_lower,\n",
" color=ctrl_clr, alpha=0.1, zorder=0,\n",
" )\n",
" )\n",
" # set alpha of errorbars\n",
" for collection in ax.collections:\n",
" collection.set_alpha(.4)\n",
"\n",
" fig.supylabel(\"Growth rate [(mm/s)$^{-1}$]\", fontsize=fontsize)\n",
" plt.legend(loc='lower right', ncol=2, fontsize=fontsize-2)\n",
" savepath = os.path.join(savefolder,savename)\n",
" plt.savefig(savepath+\".pdf\", dpi=900, format='pdf')"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
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"outputs": [],
"source": [],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}