Add workspace for testing and add PDO calibrations for single Z spots…

… and spots counted from Zs 5-9

WorkSpace/AlexP/SpotCountingByCellAndByZ/merge_dfs.py

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+import pandas as pd
+
+# Load the CSV files
+file1_path = 'dataframe_A549_DUSP1_100nM_10min_062723_spot_counts.csv'
+file2_path = 'dataframe_A549_DUSP1_100nM_10min_062723_spot_counts_z_5_9.csv'
+file3_path = 'dataframe_A549_DUSP1_100nM_10min_062723_spot_counts_z_7.csv'
+
+df1 = pd.read_csv(file1_path)
+df2 = pd.read_csv(file2_path)
+df3 = pd.read_csv(file3_path)
+
+# Rename the "Spot_Count" column in the second dataframe to "Z_5_9"
+df2.rename(columns={'Spot_Count': 'Z_5_9'}, inplace=True)
+
+# Rename the "Spot_Count" column in the third dataframe to "Z_7"
+df3.rename(columns={'Spot_Count': 'Z_7'}, inplace=True)
+
+# Merge the three dataframes on the "Image_Cell_ID" column
+df4 = pd.merge(df1, df2, on='Image_Cell_ID', how='inner')
+merged_df = pd.merge(df4, df3, on='Image_Cell_ID', how='inner')
+
+# Save the merged dataframe to a new CSV file
+merged_file_path = 'merged_dataframe_A549_DUSP1_100nM_10min_062723.csv'
+merged_df.to_csv(merged_file_path, index=False)
+
+merged_file_path

WorkSpace/AlexP/SpotCountingByCellAndByZ/merged_dataframe_A549_DUSP1_100nM_10min_062723.csv

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+Image_Cell_ID,Spot_Count,Z_5_9,Z_7
+"(0, 0)",122,79,25
+"(0, 1)",287,193,62
+"(0, 2)",215,111,36
+"(0, 3)",306,161,52
+"(0, 4)",134,101,29
+"(0, 5)",124,97,28
+"(1, 6)",273,154,18
+"(1, 7)",167,94,11
+"(1, 8)",45,20,1
+"(2, 9)",82,37,8
+"(2, 10)",90,66,24
+"(2, 11)",204,129,55
+"(2, 12)",171,111,34
+"(2, 13)",41,28,5
+"(2, 14)",111,56,11
+"(3, 15)",381,281,85
+"(3, 16)",261,210,47
+"(3, 17)",195,126,31
+"(3, 18)",100,68,18
+"(4, 19)",322,189,67
+"(4, 20)",327,256,64
+"(4, 21)",44,35,6
+"(4, 22)",90,59,14
+"(5, 23)",47,36,9
+"(5, 24)",237,174,37
+"(5, 25)",147,107,29
+"(5, 26)",93,64,16
+"(5, 27)",55,45,9
+"(5, 28)",151,133,26
+"(5, 29)",75,59,11
+"(6, 30)",237,175,51
+"(6, 31)",276,222,60
+"(6, 32)",220,143,18
+"(6, 33)",71,38,6
+"(7, 34)",195,150,29
+"(7, 35)",240,141,40
+"(7, 36)",164,103,25
+"(7, 37)",168,114,22
+"(7, 38)",72,46,10
+"(7, 39)",52,45,15
+"(7, 40)",47,41,12
+"(8, 41)",123,84,18
+"(8, 42)",64,43,11
+"(8, 43)",104,83,30
+"(8, 44)",62,50,11
+"(8, 45)",53,38,11
+"(9, 46)",265,213,75
+"(9, 47)",164,100,31
+"(9, 48)",162,126,32
+"(9, 49)",227,160,55
+"(9, 50)",65,38,10
+"(9, 51)",106,77,25
+"(9, 52)",66,47,15
+"(9, 53)",32,19,4
+"(9, 54)",26,20,6
+"(10, 55)",104,75,13
+"(10, 56)",127,86,15
+"(10, 57)",40,30,10
+"(11, 58)",59,45,5
+"(11, 59)",149,97,21
+"(11, 60)",183,114,19
+"(11, 61)",142,95,18
+"(11, 62)",70,53,10
+"(11, 63)",77,64,11
+"(11, 64)",9,6,2
+"(12, 65)",272,178,24
+"(12, 66)",350,274,58
+"(12, 67)",171,108,14
+"(12, 68)",274,102,14
+"(12, 69)",43,23,4
+"(12, 70)",71,56,7
+"(13, 71)",81,51,14
+"(13, 72)",191,146,21
+"(13, 73)",88,72,13
+"(14, 74)",141,104,17
+"(14, 75)",444,393,80
+"(14, 76)",93,66,20
+"(15, 77)",60,30,3
+"(15, 78)",50,24,1
+"(15, 79)",62,36,1
+"(15, 81)",62,35,1
+"(16, 83)",155,62,1
+"(18, 87)",135,124,31
+"(18, 88)",45,43,5
+"(18, 89)",59,51,12
+"(18, 90)",39,29,6
+"(19, 91)",93,63,22
+"(19, 92)",149,123,46
+"(19, 93)",62,42,15
+"(19, 94)",51,30,13
+"(19, 95)",11,8,3
+"(20, 97)",249,183,57
+"(20, 98)",134,115,48
+"(20, 99)",65,50,21
+"(20, 100)",36,26,3
+"(22, 102)",91,42,2
+"(22, 103)",115,62,11
+"(22, 104)",185,109,21
+"(22, 105)",34,19,3
+"(24, 108)",77,50,11
+"(24, 109)",60,39,5
+"(25, 110)",77,38,7
+"(25, 111)",102,72,7
+"(25, 112)",62,42,15
+"(25, 113)",23,15,5
+"(25, 114)",15,11,2
+"(25, 115)",12,8,2
+"(26, 116)",97,54,9
+"(26, 117)",73,43,1
+"(26, 118)",73,37,9
+"(26, 119)",25,18,1
+"(27, 121)",81,48,13
+"(27, 122)",10,4,2
+"(27, 123)",66,49,10
+"(27, 124)",24,13,3
+"(27, 125)",14,13,3
+"(28, 126)",17,11,2
+"(28, 127)",21,9,2
+"(28, 128)",27,17,6
+"(29, 130)",38,28,5
+"(29, 131)",55,29,5
+"(29, 132)",47,30,5
+"(29, 134)",54,29,5
+"(29, 135)",52,32,6

WorkSpace/AlexP/SpotCountingByCellAndByZ/spot_counting_Zs5thru9.py

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+import pandas as pd
+
+# List of file names
+files = [
+    'dataframe_A549_DUSP1_100nM_10min_062723.csv',
+    'dataframe_A549_DUSP1_100nM_20min_062723.csv',
+    'dataframe_A549_DUSP1_100nM_30min_062723.csv',
+    'dataframe_A549_DUSP1_100nM_40min_062723.csv'
+]
+
+# Initialize a dictionary to store the total "spot_count" for each file
+spot_count_per_file = {}
+
+# Loop through each file
+for file in files:
+    # Load the CSV file
+    df = pd.read_csv(file)
+
+    # Filter the dataframe to include only rows with "z" values between 5 and 9
+    filtered_df = df[(df['z'] >= 5) & (df['z'] <= 9)]
+
+    # Initialize a dictionary to store the spot count for each "cell_id" in the file
+    file_spot_counts = {}
+
+    # Loop through each "image_id"
+    for image_id in filtered_df['image_id'].unique():
+        image_df = filtered_df[filtered_df['image_id'] == image_id]
+
+        # Loop through each "cell_id" in the current "image_id"
+        for cell_id in image_df['cell_id'].unique():
+            cell_df = image_df[image_df['cell_id'] == cell_id]
+
+            # Count the number of unique "spot_id" values per "z" value for this cell_id
+            spot_count = cell_df.groupby('z')['spot_id'].nunique().sum()
+
+            # Save the total "spot_count" for this "cell_id"
+            file_spot_counts[(image_id, cell_id)] = spot_count
+
+    # Store the result for the current file
+    spot_count_per_file[file] = file_spot_counts
+
+# Save the spot counts for each file into separate CSVs
+for file, spot_counts in spot_count_per_file.items():
+    # Convert the dictionary to a DataFrame
+    spot_counts_df = pd.DataFrame(list(spot_counts.items()), columns=['Image_Cell_ID', 'Spot_Count'])
+
+    # Save the DataFrame to a new CSV file
+    output_filename = file.replace('.csv', '_spot_counts_z_5_9.csv')
+    spot_counts_df.to_csv(output_filename, index=False)
+
+print("Spot count files for z=5-9 saved successfully.")

WorkSpace/AlexP/SpotCountingByCellAndByZ/spot_counting_singleZ.py

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+import pandas as pd
+
+# List of file names
+files = [
+    'dataframe_A549_DUSP1_100nM_10min_062723.csv',
+    'dataframe_A549_DUSP1_100nM_20min_062723.csv',
+    'dataframe_A549_DUSP1_100nM_30min_062723.csv',
+    'dataframe_A549_DUSP1_100nM_40min_062723.csv'
+]
+
+# Initialize a dictionary to store the total "spot_count" for each file
+spot_count_per_file = {}
+
+# Loop through each file
+for file in files:
+    # Load the CSV file
+    df = pd.read_csv(file)
+
+    # Filter the dataframe to include only rows with "z" values = 7
+    filtered_df = df[(df['z'] == 7)]
+
+    # Initialize a dictionary to store the spot count for each "cell_id" in the file
+    file_spot_counts = {}
+
+    # Loop through each "image_id"
+    for image_id in filtered_df['image_id'].unique():
+        image_df = filtered_df[filtered_df['image_id'] == image_id]
+
+        # Loop through each "cell_id" in the current "image_id"
+        for cell_id in image_df['cell_id'].unique():
+            cell_df = image_df[image_df['cell_id'] == cell_id]
+
+            # Count the number of unique "spot_id" values per "z" value for this cell_id
+            spot_count = cell_df.groupby('z')['spot_id'].nunique().sum()
+
+            # Save the total "spot_count" for this "cell_id"
+            file_spot_counts[(image_id, cell_id)] = spot_count
+
+    # Store the result for the current file
+    spot_count_per_file[file] = file_spot_counts
+
+# Save the spot counts for each file into separate CSVs
+for file, spot_counts in spot_count_per_file.items():
+    # Convert the dictionary to a DataFrame
+    spot_counts_df = pd.DataFrame(list(spot_counts.items()), columns=['Image_Cell_ID', 'Spot_Count'])
+
+    # Save the DataFrame to a new CSV file
+    output_filename = file.replace('.csv', '_spot_counts_z_7.csv')
+    spot_counts_df.to_csv(output_filename, index=False)
+
+print("Spot count files for z=7 saved successfully.")

WorkSpace/AlexP/SpotCountingByCellAndByZ/spot_counting_thru_time_100nM.py

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+# Import necessary libraries
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# File paths
+file_paths = [
+    'dataframe_A549_DUSP1_100nM_10min_062723.csv',
+    'dataframe_A549_DUSP1_100nM_20min_062723.csv',
+    'dataframe_A549_DUSP1_100nM_30min_062723.csv',
+    'dataframe_A549_DUSP1_100nM_40min_062723.csv',
+]
+
+# Corresponding time points
+time_points = ['10min', '20min', '30min', '40min']
+
+# Initialize dictionaries to store spot counts
+total_spot_counts_by_time = {}
+spot_counts_z_0_3_by_time = {}
+spot_counts_z_4_7_by_time = {}
+spot_counts_z_8_11_by_time = {}
+spot_counts_z_12_15_by_time = {}
+spot_counts_z_16_19_by_time = {}
+spot_counts_z_20_23_by_time = {}
+spot_counts_z_24_27_by_time = {}
+
+# Loop through each file and calculate the spot counts
+for file, time_point in zip(file_paths, time_points):
+    df = pd.read_csv(file)
+
+    # Count unique spot_id values per image_id and cell_id for all z-values
+    total_spot_count = df.groupby(['image_id', 'cell_id'])['spot_id'].nunique().sum()
+
+    # Filter for z-slices
+    filtered_df_z_0_3 = df[(df['z'] >= 0) & (df['z'] <= 3)]
+    filtered_df_z_4_7 = df[(df['z'] >= 4) & (df['z'] <= 7)]
+    filtered_df_z_8_11 = df[(df['z'] >= 8) & (df['z'] <= 11)]
+    filtered_df_z_12_15 = df[(df['z'] >= 12) & (df['z'] <= 15)]
+    filtered_df_z_16_19 = df[(df['z'] >= 16) & (df['z'] <= 19)]
+    filtered_df_z_20_23 = df[(df['z'] >= 20) & (df['z'] <= 23)]
+    filtered_df_z_24_27 = df[(df['z'] >= 24) & (df['z'] <= 27)]
+
+    # Count unique spot_id values per image_id and cell_id for z=0-3
+    spot_count_z_0_3 = filtered_df_z_0_3.groupby(['image_id', 'cell_id'])['spot_id'].nunique().sum()
+
+    # Count unique spot_id values per image_id and cell_id for z=4-7
+    spot_count_z_4_7 = filtered_df_z_4_7.groupby(['image_id', 'cell_id'])['spot_id'].nunique().sum()
+
+    # Count unique spot_id values per image_id and cell_id for z=8-11
+    spot_count_z_8_11 = filtered_df_z_8_11.groupby(['image_id', 'cell_id'])['spot_id'].nunique().sum()
+
+    # Count unique spot_id values per image_id and cell_id for z=12-15
+    spot_count_z_12_15 = filtered_df_z_12_15.groupby(['image_id', 'cell_id'])['spot_id'].nunique().sum()
+
+    # Count unique spot_id values per image_id and cell_id for z=16-19
+    spot_count_z_16_19 = filtered_df_z_16_19.groupby(['image_id', 'cell_id'])['spot_id'].nunique().sum()
+
+    # Count unique spot_id values per image_id and cell_id for z=20-23
+    spot_count_z_20_23 = filtered_df_z_20_23.groupby(['image_id', 'cell_id'])['spot_id'].nunique().sum()
+
+     # Count unique spot_id values per image_id and cell_id for z=24-27
+    spot_count_z_24_27 = filtered_df_z_24_27.groupby(['image_id', 'cell_id'])['spot_id'].nunique().sum()
+
+    # Save the total spot counts for this time point
+    total_spot_counts_by_time[time_point] = total_spot_count
+    spot_counts_z_0_3_by_time[time_point] = spot_count_z_0_3
+    spot_counts_z_4_7_by_time[time_point] = spot_count_z_4_7
+    spot_counts_z_8_11_by_time[time_point] = spot_count_z_8_11
+    spot_counts_z_12_15_by_time[time_point] = spot_count_z_12_15
+    spot_counts_z_16_19_by_time[time_point] = spot_count_z_16_19
+    spot_counts_z_20_23_by_time[time_point] = spot_count_z_20_23
+    spot_counts_z_24_27_by_time[time_point] = spot_count_z_24_27
+
+# Prepare data for plotting
+times = list(total_spot_counts_by_time.keys())
+total_spot_counts = list(total_spot_counts_by_time.values())
+spot_counts_z_0_3 = list(spot_counts_z_0_3_by_time.values())
+spot_counts_z_4_7 = list(spot_counts_z_4_7_by_time.values())
+spot_counts_z_8_11 = list(spot_counts_z_8_11_by_time.values())
+spot_counts_z_12_15 = list(spot_counts_z_12_15_by_time.values())
+spot_counts_z_16_19 = list(spot_counts_z_16_19_by_time.values())
+spot_counts_z_20_23 = list(spot_counts_z_20_23_by_time.values())
+spot_counts_z_24_27 = list(spot_counts_z_24_27_by_time.values())
+
+# Create the plot
+plt.figure(figsize=(10,6))
+
+# Plot total spot counts across all z-values
+plt.plot(times, total_spot_counts, marker='o', linestyle='-', color='b', markersize=8, label='Total Spot Counts')
+
+# Plot z=24-27 spot counts
+plt.plot(times, spot_counts_z_24_27, marker='o', linestyle='-', color='c', markersize=8, label='Spot Counts (z=24-27)')
+
+# Plot z=20-23 spot counts
+plt.plot(times, spot_counts_z_20_23, marker='o', linestyle='-', color='c', markersize=8, label='Spot Counts (z=20-23)')
+
+# Plot z=16-19 spot counts
+plt.plot(times, spot_counts_z_16_19, marker='o', linestyle='-', color='c', markersize=8, label='Spot Counts (z=16-19)')
+
+# Plot z=12-15 spot counts
+plt.plot(times, spot_counts_z_12_15, marker='o', linestyle='-', color='c', markersize=8, label='Spot Counts (z=12-15)')
+
+# Plot z=8-11 spot counts
+plt.plot(times, spot_counts_z_8_11, marker='o', linestyle='-', color='c', markersize=8, label='Spot Counts (z=8-11)')
+
+# Plot z=4-7 spot counts
+plt.plot(times, spot_counts_z_4_7, marker='o', linestyle='-', color='r', markersize=8, label='Spot Counts (z=4-7)')
+
+# Plot z=0-3 spot counts
+plt.plot(times, spot_counts_z_0_3, marker='o', linestyle='-', color='r', markersize=8, label='Spot Counts (z=0-3)')
+
+
+# Add labels and title
+plt.title('Spot Counts Through Time (All z-stacks and z-slice subsets) 100nM Dex')
+plt.xlabel('Time (min)')
+plt.ylabel('Spot Count')
+plt.grid(True)
+
+# Adjust the legend placement to move it slightly up from the lower right corner
+plt.legend(loc='upper left', bbox_to_anchor=(0, 1))
+
+# Display the plot
+plt.show()