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‎TrackMateQuant_perField.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import seaborn as sns\n",
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"%matplotlib inline\n",
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"import os\n",
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"from aicsimageio import AICSImage\n",
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"os.chdir('Path_To_Directory_With_TIFF_and_CSV_Files')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Define TrackMate_Quant function, can collapse code after running first time\n",
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"\n",
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"def TrackMate_Quant(spots, img1, Z_um, Y_um, X_um, Threshold, Pct_Threshold):\n",
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" # Convert um positions back to pixel coordinates\n",
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" spots['Pixel_Z'] = spots.POSITION_Z / Z_um\n",
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" spots['Pixel_Y'] = spots.POSITION_Y / Y_um\n",
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" spots['Pixel_X'] = spots.POSITION_X / X_um\n",
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" \n",
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" # Round to integers Drop any puncta that lands on edge pixels (indexed Pixel must be > 0 and < -1 than length of the array)\n",
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" print(f'Spots before edge filtering: {spots.shape}')\n",
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" spotsF = spots.loc[:,['Pixel_Z','Pixel_Y','Pixel_X']].round(decimals=0)\n",
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" spotsF = spotsF.loc[((spotsF.Pixel_Z < (len(img1[0])-1)) & (spotsF.Pixel_Z > 0))&\n",
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" ((spotsF.Pixel_Y < (len(img1[0][0])-1)) & (spotsF.Pixel_Y > 0))&\n",
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" ((spotsF.Pixel_X < (len(img1[0][0][0])-1)) & (spotsF.Pixel_X > 0)),:]\n",
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" print(f'Spots after edge filtering: {spotsF.shape}')\n",
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" # Get numpy array of 3D coordinates [Z, Y, X], convert to np.int64\n",
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" spots_coord = spotsF.to_numpy()\n",
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" spots_coord_round = spots_coord.astype(np.int64)\n",
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" \n",
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" # Calcualte image volume and store (exlcuding all edge pixels, so must subtract 2 from each dimension)\n",
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" Pixel_Vol = Z_um*Y_um*X_um\n",
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" Total_Pixels = (len(img1[0])-2)*len(img1[0][0]-2)*len(img1[0][0][0]-2)\n",
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" Total_Vol = Total_Pixels*Pixel_Vol\n",
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" Neurite_Pixels = np.count_nonzero(img1[0] > Threshold)\n",
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" Neurite_Vol = Neurite_Pixels*Pixel_Vol\n",
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" Neurite_Vol_Pct = Neurite_Vol/Total_Vol\n",
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" \n",
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" # Initialize empty list for output\n",
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" RNAlist = []\n",
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" for i in list(np.arange(0,len(spotsF))):\n",
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" Z = spots_coord_round[i][0]\n",
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" Y = spots_coord_round[i][1]\n",
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" X = spots_coord_round[i][2]\n",
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" RNAlist.append([\n",
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" img1[0][Z][Y][X],img1[0][Z][Y-1][X+1],img1[0][Z][Y][X+1],img1[0][Z][Y+1][X+1],img1[0][Z][Y-1][X-1],\n",
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" img1[0][Z][Y][X-1],img1[0][Z][Y+1][X-1],img1[0][Z][Y-1][X],img1[0][Z][Y+1][X],img1[0][Z+1][Y][X],\n",
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" img1[0][Z+1][Y][X+1],img1[0][Z+1][Y][X-1],img1[0][Z+1][Y-1][X],img1[0][Z+1][Y+1][X],img1[0][Z-1][Y][X],\n",
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" img1[0][Z-1][Y][X+1],img1[0][Z-1][Y][X-1],img1[0][Z-1][Y-1][X],img1[0][Z-1][Y+1][X]\n",
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" ])\n",
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" ##\n",
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" RNA_Pct_list = []\n",
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" for i in list(np.arange(0,len(spotsF))):\n",
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" RNA_Pct_list.append(sum(1 for x in RNAlist[i] if (x > Threshold))/len(RNAlist[i]))\n",
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" # Output\n",
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" SpotCount = len(RNAlist)\n",
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" SpotCount_Pos = sum(1 for x in RNA_Pct_list if (x > Pct_Threshold))\n",
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" PosSpots_Percent = SpotCount_Pos/SpotCount\n",
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" SpotCount_Vol = SpotCount/Total_Vol\n",
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" SpotCount_Neurite_Vol = SpotCount/Neurite_Vol\n",
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" #\n",
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" return [SpotCount, SpotCount_Pos, PosSpots_Percent, SpotCount_Vol, SpotCount_Neurite_Vol, Total_Pixels, Total_Vol, Neurite_Pixels, Neurite_Vol, Neurite_Vol_Pct]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Set directory for files\n",
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"directory = r'/Path_To_Directory_With_TIFF_and_CSV_Files'\n",
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"Tif_Paths = []\n",
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"RNA1_Paths = []\n",
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"RNA2_Paths = []\n",
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"\n",
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"#\n",
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"for filename in os.listdir(directory):\n",
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" if filename.endswith(\".tif\"):\n",
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" Tifp = os.path.join(directory, filename)\n",
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" Tif_Paths.append(Tifp)\n",
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" else:\n",
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" if filename.endswith(\"RNA1.csv\"):\n",
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" RNA1_sp = os.path.join(directory, filename)\n",
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" RNA1_Paths.append(RNA1_sp)\n",
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" else:\n",
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" if filename.endswith(\"RNA2.csv\"):\n",
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" RNA2_sp = os.path.join(directory, filename)\n",
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" RNA2_Paths.append(RNA2_sp)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"# YOU MUST ENTER THIS INFORMATION FROM TRACKMATE BEGINNING SCREEN IN ORDER TO CONVERT THE UM BACK TO PIXEL COORDINATES\n",
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"# IT MUST BE THE SAME FOR ALL IMAGES / TRACKMATE SPOT OUTPUT TO BE ANALYZED\n",
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"Z_um = 0.300\n",
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"Y_um = 0.180\n",
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"X_um = 0.180\n",
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"\n",
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"# Set Neurite+ pixel intensity threshold\n",
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"Threshold = 75\n",
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"# Set % of Neurite+ pixels threshold for spot calling\n",
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"Pct_Threshold = 0.6\n",
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"\n",
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"# Initiliaze empty list to append output to\n",
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"RNA1_DataList = []\n",
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"RNA2_DataList = []"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Spots before edge filtering: (379, 33)\n",
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"Spots after edge filtering: (336, 3)\n",
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"Spots before edge filtering: (420, 33)\n",
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"Spots after edge filtering: (351, 3)\n",
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"Spots before edge filtering: (316, 33)\n",
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"Spots after edge filtering: (268, 3)\n",
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"Spots before edge filtering: (395, 33)\n",
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"Spots after edge filtering: (336, 3)\n"
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]
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}
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],
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"source": [
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"for i in list(np.arange(0,len(Tif_Paths))):\n",
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" img = AICSImage(Tif_Paths[i]) # Read in Tiff Image as 6D Numpy Array\n",
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" img1 = img.data[0][0] # Strip extra dimensions (Scene and Time)\n",
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" #\n",
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" # Read in RNA1 Spots first, run TrackMate_Quant, append RNA1 data to list\n",
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" spots = pd.read_csv(RNA1_Paths[i],sep=',',index_col=0) \n",
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" RNA1_store = TrackMate_Quant(spots, img1, Z_um, Y_um, X_um, Threshold, Pct_Threshold) \n",
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" RNA1_DataList.append(RNA1_store)\n",
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" # Read in RNA2 Spots next, run TrackMate_Quant, append RNA2 data to list\n",
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" spots = pd.read_csv(RNA2_Paths[i],sep=',',index_col=0) \n",
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" RNA2_store = TrackMate_Quant(spots, img1, Z_um, Y_um, X_um, Threshold, Pct_Threshold) \n",
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" RNA2_DataList.append(RNA2_store)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"RNA1_Spot_Data = pd.DataFrame(RNA1_DataList,columns=['SpotCount', 'SpotCount_Pos', 'PosSpots_Percent', \n",
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" 'SpotCount_Vol', 'SpotCount_Neurite_Vol', 'Total_Pixels', \n",
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" 'Total_Vol', 'Neurite_Pixels', 'Neurite_Vol', 'Neurite_Vol_Pct'])\n",
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"#\n",
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"RNA2_Spot_Data = pd.DataFrame(RNA2_DataList,columns=['SpotCount', 'SpotCount_Pos', 'PosSpots_Percent', \n",
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" 'SpotCount_Vol', 'SpotCount_Neurite_Vol', 'Total_Pixels', \n",
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" 'Total_Vol', 'Neurite_Pixels', 'Neurite_Vol', 'Neurite_Vol_Pct'])\n",
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"#\n",
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"RNA1_Spot_Data['RNA'] = 'RNA1'\n",
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"RNA2_Spot_Data['RNA'] = 'RNA2'\n",
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"MergePlot = pd.concat([RNA1_Spot_Data, RNA2_Spot_Data])\n",
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"MergePlot['SpotCountPos_Vol_10um3'] = MergePlot.SpotCountPos_Vol * 1e3\n",
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"MergePlot['SpotCountPos_Neurite_Vol_10um3'] = MergePlot.SpotCountPos_Neurite_Vol * 1e3\n",
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"#\n",
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"pattern = \"(.*?).tif\"\n",
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"Filenames = []\n",
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"for i in list(np.arange(0,len(Tif_Paths))):\n",
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" s = Tif_Paths[i]\n",
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" substring = re.search(pattern, s).group(1)\n",
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" Filenames.append(substring)\n",
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"Filenames2 = Filenames + Filenames\n",
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"MergePlot['Files'] = Filenames2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Write Output\n",
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"#MergePlot.to_csv('perField_RNA1_RNA2_MergeOutput.csv',sep=',')"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

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