update

.gitignore

@@ -23,11 +23,13 @@
 # RStudio files
 .Rproj.user/
 
+/figure01
 /training_data/dapi/images/*.tif
 /training_data/dapi/masks/*.tif
 
 /training_data/cytosol/images/*.tif
 /training_data/cytosol/masks/*.tif
+/training_data/cytosol/weight_maps/*.tif
 
 # produced vignettes
 vignettes/*.html

.quarto/xref/693ec055

@@ -1 +1 @@
-{"headings":["create-a-table-of-raw-data-files","import-the-data","make-a-master-data-frame-with-all-data.","make-a-plot"],"entries":[]}
+{"entries":[],"headings":["create-a-table-of-raw-data-files","import-the-data","make-a-master-data-frame-with-all-data","make-a-plot","same-for-ms-data","peak-detection-and-annotation-in-ms","bargraph-of-tfp-ester-labeling-gels","absorption-spectrum"]}

.quarto/xref/INDEX

@@ -5,5 +5,12 @@
   "supplementary_fig02.qmd": {
     "supplementary_fig02.html": "54dbd63a",
     "supplementary_fig02.md": "693ec055"
+  },
+  "presentations/20230915/index.qmd": {
+    "index.html": "f1b442c2"
+  },
+  "figure01.qmd": {
+    "figure01.html": "9560ce1b",
+    "figure01.md": "0453fe3b"
   }
 }

augment.py

@@ -1,5 +1,54 @@
 import os
 import cv2
+import numpy as np
+
+from skimage.segmentation import find_boundaries
+
+w0 = 10
+sigma = 5
+
+def make_weight_map(mask):
+    """
+    Generate the weight map for a binary mask as specified in the UNet paper.
+    
+    Parameters
+    ----------
+    mask: array-like
+        A 2D binary mask of shape (image_height, image_width).
+
+    Returns
+    -------
+    array-like
+        A 2D weight map of shape (image_height, image_width).
+    
+    """
+    nrows, ncols = mask.shape
+    mask = (mask > 0).astype(int)
+
+    # Compute the distance map
+    X1, Y1 = np.meshgrid(np.arange(nrows), np.arange(ncols))
+    bounds = find_boundaries(mask, mode='inner')
+    X2, Y2 = np.nonzero(bounds)
+    xSum = (X2.reshape(-1, 1) - X1.reshape(1, -1)) ** 2
+    ySum = (Y2.reshape(-1, 1) - Y1.reshape(1, -1)) ** 2
+    distMap = np.sqrt(xSum + ySum)
+
+    # Calculate the border loss map
+    w0 = 10.0  # Adjust this value as needed
+    sigma = 5.0  # Adjust this value as needed
+    border_loss_map = w0 * np.exp((-1 * (distMap) ** 2) / (2 * (sigma ** 2)))
+
+    # Compute the class weight map
+    loss = np.zeros((nrows, ncols))
+    w_1 = 1 - mask.sum() / loss.size
+    w_0 = 1 - w_1
+    loss[mask == 1] = w_1
+    loss[mask == 0] = w_0
+
+    # Combine border loss and class weight to get the final weight map
+    weight_map = border_loss_map + loss
+
+    return weight_map
 
 # Set the paths for the input folders
 mask_org_folder = "./training_data/masks_cytosol"
@@ -8,10 +57,12 @@
 # Set the paths for the output folders
 mask_folder = "./training_data/cytosol/masks"
 images_folder = "./training_data/cytosol/images"
+weights_folder = "./training_data/cytosol/weight_maps"
 
 # Create the output folders if they don't exist
 os.makedirs(mask_folder, exist_ok=True)
 os.makedirs(images_folder, exist_ok=True)
+os.makedirs(weights_folder, exist_ok=True)
 
 # Set the ROI size and step
 roi_size = (256, 256)
@@ -25,10 +76,58 @@
     # Read the mask image
     mask_path = os.path.join(mask_org_folder, file_name)
     mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
+    mask_image = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
+
+    # Convert the image to float32 format for Laplacian operation
+    mask = mask.astype(np.float32)
+
+    # Apply Laplacian filter
+    B = cv2.Laplacian(mask, cv2.CV_32F)
+
+    # Take the absolute value in-place
+    B = np.abs(B)
+
+    # Divide by B (this will produce a divide by zero warning that you can safely ignore)
+    B /= B
+    B = np.nan_to_num(B, nan=0)
+
+    # Multiply by mask
+    B *= mask
+
+    mask = mask - B
+
+    # Convert B back to uint8 if needed
+    mask = mask.astype(np.uint8)
+
+    # Define the kernel (structuring element) for erosion
+    kernel_size = 3  # Adjust the size as needed
+    kernel = np.ones((kernel_size, kernel_size), dtype=np.uint8)
+    # Perform erosion
+    mask = cv2.erode(mask, kernel, iterations=4)    
+    # 
+    # # Define the new width and height (25% of the original size)
+    # new_width = int(mask.shape[1] * 0.25)
+    # new_height = int(mask.shape[0] * 0.25)
+    # 
+    # # Resize the image using bicubic interpolation
+    # mask = cv2.resize(mask, (new_width, new_height), interpolation=cv2.INTER_CUBIC)
+    # 
+    _, mask = cv2.threshold(mask, 0, 255, cv2.THRESH_BINARY)
+    _, thresholded_mask2 = cv2.threshold(mask_image, 0, 255, cv2.THRESH_BINARY)
+
+
+    boundary = thresholded_mask2-mask
+    boundary = boundary.astype(np.uint8)
+
+    dist = cv2.distanceTransform(255-boundary, cv2.DIST_L2, cv2.DIST_MASK_5)
+
+    weight_map = w0 * np.exp((-1 * (dist) ** 2) / (2 * (sigma ** 2)))
+    weight_map = weight_map.astype(np.uint8)    
 
     # Read the corresponding input image
     image_path = os.path.join(images_org_folder, file_name)
     image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE | cv2.IMREAD_ANYDEPTH)
+    #image = cv2.resize(image, (new_width, new_height), interpolation=cv2.INTER_CUBIC)
 
     # Get the dimensions of the images
     mask_height, mask_width = mask.shape[:2]
@@ -42,11 +141,18 @@
 
             # Extract the ROI from the image
             roi_image = image[y:y+roi_size[0], x:x+roi_size[1]]
-
+
+            # Extract the ROI from the weight
+            roi_weight = weight_map[y:y+roi_size[0], x:x+roi_size[1]]
+
             # Save the ROI as a new image in the mask folder
             new_mask_path = os.path.join(mask_folder, f"{file_name}_{y}_{x}.tif")
             cv2.imwrite(new_mask_path, roi_mask)
 
             # Save the ROI as a new image in the images folder
             new_image_path = os.path.join(images_folder, f"{file_name}_{y}_{x}.tif")
             cv2.imwrite(new_image_path, roi_image)
+
+            # Save the ROI as a new image in the images folder
+            new_weight_path = os.path.join(weights_folder, f"{file_name}_{y}_{x}.tif")
+            cv2.imwrite(new_weight_path, roi_weight)

augment_dapi.py

@@ -0,0 +1,52 @@
+import os
+import cv2
+
+# Set the paths for the input folders
+mask_org_folder = "./training_data/masks_dapi"
+images_org_folder = "./training_data/images_dapi"
+
+# Set the paths for the output folders
+mask_folder = "./training_data/dapi/masks"
+images_folder = "./training_data/dapi/images"
+
+# Create the output folders if they don't exist
+os.makedirs(mask_folder, exist_ok=True)
+os.makedirs(images_folder, exist_ok=True)
+
+# Set the ROI size and step
+roi_size = (256, 256)
+roi_step = 128
+
+# Get the list of file names in the mask_org folder
+file_names = [file for file in os.listdir(mask_org_folder) if file.lower().endswith(('.tif', '.tiff'))]
+
+# Iterate over the file names
+for file_name in file_names:
+    # Read the mask image
+    mask_path = os.path.join(mask_org_folder, file_name)
+    mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
+
+    # Read the corresponding input image
+    image_path = os.path.join(images_org_folder, file_name)
+    image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE | cv2.IMREAD_ANYDEPTH)
+
+    # Get the dimensions of the images
+    mask_height, mask_width = mask.shape[:2]
+    image_height, image_width = image.shape[:2]
+
+    # Iterate over the ROI positions
+    for y in range(0, mask_height - roi_size[0] + 1, roi_step):
+        for x in range(0, mask_width - roi_size[1] + 1, roi_step):
+            # Extract the ROI from the mask
+            roi_mask = mask[y:y+roi_size[0], x:x+roi_size[1]]
+
+            # Extract the ROI from the image
+            roi_image = image[y:y+roi_size[0], x:x+roi_size[1]]
+
+            # Save the ROI as a new image in the mask folder
+            new_mask_path = os.path.join(mask_folder, f"{file_name}_{y}_{x}.tif")
+            cv2.imwrite(new_mask_path, roi_mask)
+
+            # Save the ROI as a new image in the images folder
+            new_image_path = os.path.join(images_folder, f"{file_name}_{y}_{x}.tif")
+            cv2.imwrite(new_image_path, roi_image)

augment_weight.py

@@ -0,0 +1,43 @@
+import os
+import cv2
+import numpy as np
+
+# Set the paths for the input folders
+weights_org_folder = "./training_data/weights_cytosol"
+
+# Set the paths for the output folders
+weights_folder = "./training_data/cytosol/weight_maps"
+
+# Create the output folders if they don't exist
+os.makedirs(weights_folder, exist_ok=True)
+
+# Set the ROI size and step
+roi_size = (256, 256)
+roi_step = 128
+
+# Get the list of file names in the mask_org folder
+file_names = [file for file in os.listdir(weights_org_folder) if file.lower().endswith(('.tif', '.tiff'))]
+
+# Iterate over the file names
+for file_name in file_names:
+    # Read the mask image
+    print(file_name)
+    weight_path = os.path.join(weights_org_folder, file_name)
+    weight = cv2.imread(weight_path, cv2.IMREAD_UNCHANGED)
+
+    # Convert the image to float32 format for Laplacian operation
+    weight = weight.astype(np.float32)
+
+    # Get the dimensions of the images
+    weight_height, weight_width = weight.shape[:2]
+
+    # Iterate over the ROI positions
+    for y in range(0, weight_height - roi_size[0] + 1, roi_step):
+        for x in range(0, weight_width - roi_size[1] + 1, roi_step):
+
+            # Extract the ROI from the weight
+            roi_weight = weight[y:y+roi_size[0], x:x+roi_size[1]]
+
+            # Save the ROI as a new image in the images folder
+            new_weight_path = os.path.join(weights_folder, f"{file_name}_{y}_{x}.tif")
+            cv2.imwrite(new_weight_path, roi_weight)

data/csv/fig02_cytosol_cMycIsotype_Results.csv

@@ -0,0 +1,9 @@
+ ,Label,Mean,Min,Max,Median
+1,A:0003-0435-0456,168.495,0,531,199
+2,A:0003-0177-0985,160.733,0,480,198
+3,A:0003-0444-1366,160.783,0,383,198
+4,A:0003-0852-1427,160.357,0,422,205
+5,A:0003-1455-0942,172.318,0,360,201
+6,A:0003-1071-0381,177.828,0,371,200
+7,A:0003-0739-0646,181.174,0,411,202
+8,A:0003-1041-0991,167.445,0,368,207

data/csv/fig02_cytosol_intensity_Results.csv

@@ -0,0 +1,29 @@
+ ,Label,Mean,Min,Max,Median
+1,A:0003-0435-0456,168.495,0,531,199
+2,A:0003-0177-0985,160.733,0,480,198
+3,A:0003-0444-1366,160.783,0,383,198
+4,A:0003-0852-1427,160.357,0,422,205
+5,A:0003-1455-0942,172.318,0,360,201
+6,A:0003-1071-0381,177.828,0,371,200
+7,A:0003-0739-0646,181.174,0,411,202
+8,A:a,167.445,0,368,207
+9,cMycMax:0003-0525-0460,167.621,0,706,218
+10,cMycMax:0003-0237-1005,187.198,0,664,226
+11,cMycMax:0003-0427-1131,176.311,0,863,227
+12,cMycMax:0003-0757-1365,192.152,0,716,228
+13,cMycMax:0003-0747-1041,158.532,0,665,247
+14,cMycMax:0003-0805-0802,171.970,0,940,242
+15,cMycMax:0003-1008-0588,171.795,0,467,245
+16,cMycMax:0003-1282-1510,151.439,0,604,227
+17,cMycMax:0003-1300-0888,197.114,0,879,231
+18,cMycMax:b,190.831,0,522,222
+19,isotype:0003-0841-0262,170.476,0,403,207
+20,isotype:0003-0511-0694,176.406,0,684,211
+21,isotype:0003-0225-0988,183.841,0,453,213
+22,isotype:0003-0103-1492,162.702,0,365,206
+23,isotype:0003-0532-1595,210.358,0,373,214
+24,isotype:0003-0675-1117,171.045,0,536,217
+25,isotype:0003-0939-0679,178.793,0,666,218
+26,isotype:0003-1008-1426,167.730,0,432,211
+27,isotype:0003-1293-0927,176.555,0,411,211
+28,isotype:0003-1243-0300,171.558,0,665,207

data/csv/fig02_nuclei_intensity_Results.csv

@@ -0,0 +1,30 @@
+ ,Label,Mean,Min,Max,Median
+1,C3-MAX_PC3_230925_CMYC_igg_ON_wash_1_MMStack_Pos0.ome-1-1-1.tif:0001-0144,246.735,192,475,244
+2,C3-MAX_PC3_230925_CMYC_igg_ON_wash_1_MMStack_Pos0.ome-1-1-1.tif:0002-0429,245.010,189,645,242
+3,C3-MAX_PC3_230925_CMYC_igg_ON_wash_1_MMStack_Pos0.ome-1-1-1.tif:0004-0491,243.300,187,608,239
+4,C3-MAX_PC3_230925_CMYC_igg_ON_wash_1_MMStack_Pos0.ome-1-1-1.tif:0006-0766,275.857,207,865,271
+5,C3-MAX_PC3_230925_CMYC_igg_ON_wash_1_MMStack_Pos0.ome-1-1-1.tif:0007-0875,244.401,195,502,241
+6,C3-MAX_PC3_230925_CMYC_igg_ON_wash_1_MMStack_Pos0.ome-1-1-1.tif:0008-1056,268.981,202,614,264
+7,C3-MAX_PC3_230925_CMYC_igg_ON_wash_1_MMStack_Pos0.ome-1-1-1.tif:0009-1087,262.395,195,512,260
+8,C3-MAX_PC3_230925_CMYC_igg_ON_wash_1_MMStack_Pos0.ome-1-1-1.tif:a,257.058,198,509,254
+9,C3-MAX_PC3_230925_MycMAX_ON_wash_10_MMStack_Pos0.ome-1-1.tif:0001-0223,348.271,227,1502,336
+10,C3-MAX_PC3_230925_MycMAX_ON_wash_10_MMStack_Pos0.ome-1-1.tif:0002-0470,317.606,217,904,308
+11,C3-MAX_PC3_230925_MycMAX_ON_wash_10_MMStack_Pos0.ome-1-1.tif:0003-0478,317.967,220,1247,308
+12,C3-MAX_PC3_230925_MycMAX_ON_wash_10_MMStack_Pos0.ome-1-1.tif:0004-0760,432.118,250,1648,418
+13,C3-MAX_PC3_230925_MycMAX_ON_wash_10_MMStack_Pos0.ome-1-1.tif:0005-0740,358.088,255,804,357
+14,C3-MAX_PC3_230925_MycMAX_ON_wash_10_MMStack_Pos0.ome-1-1.tif:0006-0846,329.041,221,983,319
+15,C3-MAX_PC3_230925_MycMAX_ON_wash_10_MMStack_Pos0.ome-1-1.tif:0007-0851,315.532,232,524,314
+16,C3-MAX_PC3_230925_MycMAX_ON_wash_10_MMStack_Pos0.ome-1-1.tif:0008-1022,332.078,249,581,329
+17,C3-MAX_PC3_230925_MycMAX_ON_wash_10_MMStack_Pos0.ome-1-1.tif:0009-1211,351.233,224,1086,346
+18,C3-MAX_PC3_230925_MycMAX_ON_wash_10_MMStack_Pos0.ome-1-1.tif:0010-1307,331.643,233,1038,324
+19,C3-MAX_PC3_230925_MycMAX_ON_wash_10_MMStack_Pos0.ome-1-1.tif:b,313.757,228,906,307
+20,C3-MAX_PC3_230925_IGG_On_wash_1_MMStack_Pos0.ome-1-1-1.tif:0001-0048,258.937,204,478,257
+21,C3-MAX_PC3_230925_IGG_On_wash_1_MMStack_Pos0.ome-1-1-1.tif:0002-0308,275.938,208,730,270
+22,C3-MAX_PC3_230925_IGG_On_wash_1_MMStack_Pos0.ome-1-1-1.tif:0003-0552,267.029,207,996,262
+23,C3-MAX_PC3_230925_IGG_On_wash_1_MMStack_Pos0.ome-1-1-1.tif:0004-0629,286.298,211,1287,276
+24,C3-MAX_PC3_230925_IGG_On_wash_1_MMStack_Pos0.ome-1-1-1.tif:0005-0738,253.333,197,593,251
+25,C3-MAX_PC3_230925_IGG_On_wash_1_MMStack_Pos0.ome-1-1-1.tif:0006-0714,252.963,213,352,252
+26,C3-MAX_PC3_230925_IGG_On_wash_1_MMStack_Pos0.ome-1-1-1.tif:0007-0958,285.719,212,644,279
+27,C3-MAX_PC3_230925_IGG_On_wash_1_MMStack_Pos0.ome-1-1-1.tif:0008-1026,266.693,198,654,263
+28,C3-MAX_PC3_230925_IGG_On_wash_1_MMStack_Pos0.ome-1-1-1.tif:0009-1296,256.807,195,715,254
+29,C3-MAX_PC3_230925_IGG_On_wash_1_MMStack_Pos0.ome-1-1-1.tif:0010-1327,252.591,202,507,250

figure01.qmd

@@ -99,6 +99,7 @@ for (sample_name in unique_samples) {
 
 axis(1, at=seq(300,800,by=50))
 
+peak.max
 ```
 
 Save the plot.

get_data.sh

@@ -0,0 +1,23 @@
+#!/bin/bash
+
+# Specify the folder containing TIF images
+image_folder="./figure01/230913"
+
+# Specify the Python script to run
+python_script="figure01.py"
+
+# Check if the folder exists
+if [ ! -d "$image_folder" ]; then
+  echo "Folder not found: $image_folder"
+  exit 1
+fi
+
+# Iterate through TIF images in the folder
+for image_file in "$image_folder"/*.tif; do
+  if [ -f "$image_file" ]; then
+    echo "Processing image: $image_file"
+    python "$python_script" "$image_file"
+  else
+    echo "No TIF images found in $image_folder"
+  fi
+done