Generate_PWS_D_Images.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt \n",
    "import h5py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set File Paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Common path shared between computers for OneDrive\n",
    "subgroupfolder = '\\\\OneDrive - Northwestern University\\\\Documents - Backman Lab - Shared Folders\\\\Subgroup Folders'\n",
    "# Path for the current computer to get to the Cancer Stem Cell OneDrive\n",
    "parentfolder = glob.glob('C:\\\\Users\\\\**'+subgroupfolder)[0]+'\\\\Chromatin\\\\Cancer Stem Cells'\n",
    "# Folder containing all data and figures for the paper\n",
    "paperfolder = parentfolder+'\\\\OCSC Chromatin Transcription Paper 2024'\n",
    "# Folder containing all raw PWS data as image cubes\n",
    "pwsfolder = paperfolder+'\\\\Data\\\\PWS Raw Image Cubes'\n",
    "# Folder containing the LUT generated in MATLAB to convert sigma to D\n",
    "conversionfolder = paperfolder+'\\\\Codes\\\\Sigma to D'\n",
    "# Folder location for saving images\n",
    "savefolder = paperfolder+'\\\\Figure Panels\\\\PWS D and BF Images\\\\All Images'\n",
    "\n",
    "# If the folder for saving images does not exist, the code will create it\n",
    "if not os.path.exists(savefolder):\n",
    "    os.makedirs(savefolder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set Figure Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpl.rcParams[\"figure.dpi\"] = 600\n",
    "mpl.rcParams[\"axes.edgecolor\"] = 'k'\n",
    "mpl.rcParams['text.color'] = 'k'\n",
    "mpl.rcParams['axes.labelcolor'] = 'k'\n",
    "mpl.rcParams['xtick.color'] = 'k'\n",
    "mpl.rcParams['ytick.color'] = 'k'\n",
    "mpl.rcParams['font.weight'] = 'bold'\n",
    "mpl.rcParams[\"axes.labelweight\"] = \"bold\"\n",
    "mpl.rcParams[\"axes.titleweight\"] = \"bold\"\n",
    "mpl.rcParams['errorbar.capsize'] = 2.5\n",
    "mpl.rcParams['legend.frameon'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Custom Colormap for PWS Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a custom colormap that ranges from black to red\n",
    "# Only use the first half of the red colormap (from 0 to 0.5)\n",
    "cmap_colors = [(0, 0, 0), (0.5, 0, 0)]\n",
    "cmap = mpl.colors.LinearSegmentedColormap.from_list('my_colormap', cmap_colors, N=256)\n",
    "\n",
    "# Define a custom colormap that ranges from black to gray\n",
    "graycmap = mpl.colormaps.get_cmap('gray')\n",
    "# Only use the first quarter of the gray colormap (from 0 to 0.25)\n",
    "newgray = mpl.colors.ListedColormap(graycmap(np.linspace(0, 0.25, 256)))\n",
    "\n",
    "# Set the minimum and maximum D values for images\n",
    "minv = 1.5\n",
    "maxv = 3\n",
    "\n",
    "# Create the color vector for the colorbars using the minimum and maximum D values\n",
    "norm = mpl.colors.Normalize(vmin=minv, vmax=maxv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Output Horizontal D Colorbar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create and display a horizontal colorbar for the red D pseudocolor\n",
    "fig, ax = plt.subplots(figsize=(2, 0.5))\n",
    "fig.subplots_adjust(bottom=0.5)\n",
    "cb = mpl.colorbar.ColorbarBase(ax,cmap=cmap,norm=norm,orientation='horizontal')\n",
    "cb.ax.set_xlabel(r'$D$')\n",
    "# Save the colorbar to the parent folder\n",
    "plt.savefig(savefolder+'\\\\..\\\\PWS D Colorbar Horizontal.tif',dpi=600,transparent=True,bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Output Vertical D Scalebar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 300x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create and display a horizontal colorbar for the red D pseudocolor\n",
    "fig, ax = plt.subplots(figsize=(0.5, 2))\n",
    "fig.subplots_adjust(left=0.5)\n",
    "cb = mpl.colorbar.ColorbarBase(ax,cmap=cmap,norm=norm,orientation='vertical')\n",
    "cb.ax.set_ylabel(r'$D$',rotation=0)\n",
    "# Save the colorbar to the parent folder\n",
    "plt.savefig(savefolder+'\\\\..\\\\PWS D Colorbar Vertical.tif',dpi=600,transparent=True,bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Sigma to D Conversion Function from LUT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3840x2880 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read in LUT for converting sigma to D created in MATLAB for the LCPWS1 system\n",
    "convlut = pd.read_csv(conversionfolder+'\\\\SigmaToDLUT_LCPWS1.csv')\n",
    "# Create a polynomial function from the LUT\n",
    "convfunc = np.poly1d(np.polyfit(convlut.RMS,convlut.D,10))\n",
    "# Create a vector of RMS values to evaluate the function\n",
    "rmsvec = np.linspace(0,0.5,100)\n",
    "\n",
    "# Plot the LUT and the function\n",
    "plt.plot(convlut.RMS,convlut.D,marker='o',label='Original Data from MATLAB')\n",
    "plt.plot(rmsvec,convfunc(rmsvec),label='Polynomial Fit in Python')\n",
    "plt.title(r'Sigma to $D$ Conversion Function')\n",
    "plt.xlabel('RMS')\n",
    "plt.ylabel(r'$D$')\n",
    "plt.legend()\n",
    "# Save the plot to the parent folder\n",
    "plt.savefig(savefolder+'\\\\..\\\\Sigma to D Conversion Function.tif',dpi=600,transparent=True,bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loop Through Folders to Generate PWS D Pseudocolor Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PWS Image Generation Completed for the Folder: \\12-16-16\\Afternoon\n",
      "PWS Image Generation Completed for the Folder: \\1-20-17\n",
      "PWS Image Generation Completed for the Folder: \\1-30-18\n",
      "PWS Image Generation Completed for the Folder: \\3-28-18 Stem cells\\OvCar5 Dpos\n"
     ]
    }
   ],
   "source": [
    "# Folders with image cubes to loop through and create images\n",
    "imfolders = ['\\\\OVCAR5 2016-12-16 Afternoon',\n",
    "             '\\\\OVCAR3 2016-12-16 Afternoon',\n",
    "             '\\\\OVCAR5 2017-01-20',\n",
    "             '\\\\OVCAR5 ALDH+ 2018-01-30',\n",
    "             '\\\\COV362 ALDH+CD133+ 2018-03-28']\n",
    "\n",
    "# Loops through folders\n",
    "for fold in imfolders:\n",
    "    # Find all analyzed image cubes to create images for\n",
    "    imlist = [filepath for filepath in glob.glob(pwsfolder+fold+'/**/analysisResults_p0.h5', recursive=True)]\n",
    "    # Loops through image cubes\n",
    "    for file in imlist:\n",
    "        # Find the parent folder containing all files for the current cell\n",
    "        cellfold = file.split('\\\\analyses')[0]\n",
    "        # Get the cell number from the file path\n",
    "        cellid = str.split(cellfold,'\\\\')[-1]\n",
    "        # Path for new subfolder to save images into\n",
    "        savesubfold = cellfold.split('Cubes')[1].split('\\\\Cell')[0]\n",
    "        # New save path to subfolder\n",
    "        fullsavepath = savefolder+savesubfold\n",
    "        # Creates save path to subfolder if it does not exist\n",
    "        if not os.path.exists(fullsavepath):\n",
    "            os.makedirs(fullsavepath)\n",
    "            \n",
    "        # Open h5 file with the RMS image\n",
    "        rmsfile = h5py.File(file, 'r')\n",
    "        # Extract RMS data and multiple by LCPWS1 microscope extra reflection correction factor\n",
    "        rmsim = rmsfile['rms'][()]*2.43\n",
    "        # Close h5 file\n",
    "        rmsfile.close()\n",
    "        # Change extremely high RMS values to boundary of the conversion function\n",
    "        # The conversion function does not fit the LUT well at or above RMS values of 0.5\n",
    "        rmsim[rmsim>0.5] = 0.5\n",
    "        # Convert the RMS image to D\n",
    "        Dim = convfunc(rmsim)\n",
    "        \n",
    "        # Path to the ROIs in the cell folder\n",
    "        roipath = cellfold+'\\\\ROI_nuc.h5'\n",
    "        # Check if there is a file containing the ROIs\n",
    "        if os.path.exists(roipath):\n",
    "            # Open h5 file with the ROIs\n",
    "            roifile = h5py.File(roipath, 'r')\n",
    "            # Create an empty array the same shape as the image to contain the ROIs\n",
    "            allrois = np.empty(Dim.shape)\n",
    "            # Loop through the ROIs in the h5file\n",
    "            for key in roifile.keys():\n",
    "                # Extract the ROI from the h5 file\n",
    "                roimask = roifile[key+'/mask'][()]\n",
    "                # Add the current ROI to the ROI mask image\n",
    "                allrois = allrois + roimask\n",
    "            # Close the h5 file\n",
    "            roifile.close()\n",
    "            # Convert the ROI array to boolean type \n",
    "            allrois = allrois.astype(bool)\n",
    "            # Apply the ROI mask to the D image\n",
    "            maskedim = np.ma.array(Dim, mask=~allrois)\n",
    "            \n",
    "            # Create the red pseudocolor D image\n",
    "            fig, ax = plt.subplots()\n",
    "            # Display the whole image using the custom grayscale colormap\n",
    "            ax.imshow(Dim, cmap=newgray, vmin=minv, vmax=maxv)\n",
    "            # Display the ROIs on top of the whole image using the custom red colormap\n",
    "            ax.imshow(maskedim, cmap=cmap, vmin=minv, vmax=maxv)\n",
    "            # Remove the plot axes\n",
    "            plt.axis('off')\n",
    "            # Save the image to the specified folder\n",
    "            plt.savefig(fullsavepath+'\\\\'+cellid+'_PWS.tif',transparent=True,bbox_inches='tight',pad_inches=0)\n",
    "            plt.close(fig)\n",
    "        \n",
    "        # Read in the brightfield image file\n",
    "        bfim = plt.imread(cellfold+'\\\\image_bd.tif')\n",
    "        # Create the brightfield image\n",
    "        fig, ax = plt.subplots()\n",
    "        # Display the brightfield image using the Matplotlib grayscale colormap\n",
    "        ax.imshow(bfim,cmap='gray')\n",
    "        # Remove the plot axes\n",
    "        plt.axis('off')\n",
    "        # Save the image to the specified folder\n",
    "        plt.savefig(fullsavepath+'\\\\'+cellid+'_BF.tif',transparent=True,bbox_inches='tight',pad_inches=0)\n",
    "        plt.close(fig)\n",
    "    # Display progress to user\n",
    "    print('PWS Image Generation Completed for the Folder: '+fold)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
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  "vscode": {
   "interpreter": {
    "hash": "4da363e065b6b20b1465268775492b7ab8f8d57ac0311758f028673938ac6a28"
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 },
 "nbformat": 4,
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}