merged behavior data with quantitaitve dti values and corrolated between them with corrections

code/correlation_between_bahavior_and_DTI.py

@@ -0,0 +1,69 @@
+import os
+import pandas as pd
+from scipy.stats import pearsonr
+import numpy as np
+
+# Get the directory where the code file is located
+code_dir = os.path.dirname(os.path.abspath(__file__))
+
+# Get the parent directory of the code directory
+parent_dir = os.path.dirname(code_dir)
+
+# Step 4: Save the resulting dataframe to a CSV file
+input_file_path = os.path.join(parent_dir, 'output', "Final_Quantitative_output",'Final_Quantitative_output&behavior_data.csv')
+
+df = pd.read_csv(input_file_path)
+
+df_f1 =  (df["dialation_amount"] == 0)
+
+# Empty list to store the results
+results = []
+
+# Iterate over unique values of 'Group', 'merged_timepoint', 'Qtype', and 'mask_name'
+for ss in df["Group"].unique():
+    for time_point in df["merged_timepoint"].unique():
+        for qq in df["Qtype"].unique():
+            for mm in df["mask_name"].unique():
+                # Filter the DataFrame for the current combination of 'Group', 'merged_timepoint', 'Qtype', and 'mask_name'
+                df_f2 = df[df_f1 & (df["Group"] == ss) & (df["merged_timepoint"] == time_point) & (df["Qtype"] == qq) & (df["mask_name"] == mm)]
+                
+                # Remove rows with NaN values in 'Value' or 'averageScore' columns
+                df_f2 = df_f2.dropna(subset=['Value', 'averageScore'])
+                
+                if not df_f2.empty:
+                    # Calculate Pearson correlation coefficient between 'Value' and 'averageScore' columns
+                    correlation_coefficient, p_value = pearsonr(df_f2["Value"], df_f2["averageScore"])
+                    
+                    # Store the results in a dictionary
+                    result = {'Group': ss, 'merged_timepoint': time_point, 'Qtype': qq, 'mask_name': mm, 'Pval_corr': p_value, 'R_corr': correlation_coefficient}
+                    
+                    # Append the dictionary to the results list
+                    results.append(result)
+                else:
+                    print(f"No valid data found for Group: {ss}, merged_timepoint: {time_point}, Qtype: {qq}, and mask_name: {mm}. Skipping.")
+
+# Create a DataFrame from the results list
+correlation_results_df = pd.DataFrame(results)
+
+# Apply Holm-Bonferroni correction to p-values
+p_values = correlation_results_df['Pval_corr']
+num_tests = len(p_values)
+sorted_indices = np.argsort(p_values)
+
+# Initialize an empty array to store the corrected p-values
+p_values_corrected = np.ones(num_tests)
+
+# Loop through the sorted p-values and apply the Holm-Bonferroni correction
+for i, idx in enumerate(sorted_indices):
+    p_values_corrected[idx] = np.min([1, p_values[idx] * (num_tests - i)])
+
+# Assign the corrected p-values to the DataFrame
+correlation_results_df['Pval_corr_corrected'] = p_values_corrected
+
+# Define the output file path
+output_file_path = os.path.join(parent_dir, 'output', "Correlation_with_behavior",'correlation_results.csv')
+
+# Save the correlation results DataFrame to a CSV file
+correlation_results_df.to_csv(output_file_path, index=False)
+
+print("Correlation results with corrected p-values saved successfully to 'correlation_results.csv' in the output folder.")

code/incooparate_behavior_data.py

@@ -0,0 +1,34 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Apr  8 13:23:12 2024
+
+@author: arefks
+"""
+
+import os
+import pandas as pd
+
+# Get the directory where the code file is located
+code_dir = os.path.dirname(os.path.abspath(__file__))
+
+# Get the parent directory of the code directory
+parent_dir = os.path.dirname(code_dir)
+
+# Read the behavior data CSV file into a Pandas DataFrame
+input_file_quant = os.path.join(parent_dir, 'output',"Final_Quantitative_output" ,'Quantitative_results_from_dwi_processing.csv')
+df_q = pd.read_csv(input_file_quant)
+
+# Read the behavior data CSV file into a Pandas DataFrame
+input_file_behav = os.path.join(parent_dir, 'output',"behavior_analysis" ,'behavior_data_processed.csv')
+df_b = pd.read_csv(input_file_behav)
+
+# Merge the columns from df_b into df_q based on subjectID and merged_timepoint columns
+merged_df = pd.merge(df_q, df_b[['subjectID', 'merged_timepoint', 'paw_dragZ-score', 'hindlimb_dropZ-score', 'foot_faultsZ-score', 'averageScore', '2 Cluster', '3 Cluster', '4 Cluster', '5 Cluster', '6 Cluster']], 
+                     on=['subjectID', 'merged_timepoint'], how='left')
+
+
+# Define the output file path
+output_file_path = os.path.join(parent_dir, 'output', 'Final_Quantitative_output&behavior_data.csv')
+
+# Save the merged DataFrame as a CSV file
+merged_df.to_csv(output_file_path, index=False)

output/behavior_analysis/behavior_data_processed.csv

@@ -0,0 +1,760 @@
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+448,446,sub-GVsT3c6m2,8,44.44,1.67,13.1,12/02/2019,Stroke,7,No,1.454067988,0.557085568,0.741795221,0.917649592,1,1,1,4,4
+449,447,sub-GVsT3c7m1,8,,,16.22,12/02/2019,Stroke,7,Yes,,,1.244245402,1.244245402,0,2,2,2,2
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+464,462,sub-GVsT3c14m2,13,,0.67,6,26/08/2019,Stroke,14,Yes,,-0.232092383,-0.401601024,-0.316846703,0,2,2,2,2
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+492,490,sub-GVsT3c16m1,14,,0.25,7,30/09/2019,Stroke,14,Yes,,-0.563547122,-0.240559299,-0.402053211,0,2,2,2,2
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+498,496,sub-SPsT1c11m1,14,45,0,23.08,24/04/2018,Stroke,14,No,1.484178636,-0.76084161,2.348991633,1.024109553,1,1,1,1,1
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+527,525,sub-SPsT1c4m2,14,17.65,2,10.66,23/01/2018,Stroke,14,No,0.013596097,0.817514292,0.348853413,0.393321267,0,2,2,2,2
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+529,527,sub-SPsT1c6m1,14,30,2,12.66,29/01/2018,Stroke,14,No,0.677643423,0.817514292,0.670936862,0.722031525,0,0,2,2,2
+530,528,sub-SPsT1c7m1,14,12.5,1,25.69,29/01/2018,Stroke,14,No,-0.263314326,0.028336341,2.769310535,0.844777516,1,1,1,1,1
+531,529,sub-SPsT1.2.2c1m1,14,0,,,20/06/2022,Stroke,14,No,-0.935427004,,,-0.935427004,1,2,2,4,4
+532,530,sub-SPsT1.2.2c1m2,14,0,,,20/06/2022,Stroke,14,No,-0.935427004,,,-0.935427004,0,0,0,0,5
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+534,532,sub-SPsV1c1m1,14,25,2.67,23.46,26/10/2017,Stroke,14,No,0.408798351,1.346263519,2.410187489,1.388416453,0,2,2,2,2
+535,533,sub-SPsV1c1m3,14,33.33,0.5,19.59,26/10/2017,Stroke,14,No,0.85669424,-0.366252634,1.786956014,0.75913254,0,2,2,2,2
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+556,554,sub-SPsV1c9m1,14,22.22,1,14.19,13/08/2018,Stroke,14,No,0.259320492,0.028336341,0.917330701,0.401662511,0,0,0,0,5
+557,555,sub-SPsV1c9m2,14,57.14,1.25,4.17,13/08/2018,Stroke,14,No,2.136934469,0.225630829,-0.69630738,0.555419306,0,2,2,2,2
+558,556,sub-SPsV1c9m3,14,0,0.5,,13/08/2018,Sham,14,Not analyzed,-0.935427004,-0.366252634,,-0.650839819,S,S,S,S,S
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+560,558,sub-GVsT3c1m3,15,,1.33,12.77,04/12/2018,Stroke,14,Yes,,0.288765065,0.688651452,0.488708258,0,2,2,4,4
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+619,617,sub-SPsT1c27m2,21,,,19.7,22/04/2019,Stroke,21,Yes,,,1.804670604,1.804670604,0,0,0,0,0
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+626,624,sub-SPsT1c29m3,21,0,,5.8,22/04/2019,Stroke,21,No,-0.935427004,,-0.433809369,-0.684618187,0,2,2,2,2
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+631,629,sub-SPsT1c6m1,21,29.41,0.67,10,05/02/2018,Stroke,21,No,0.645919704,-0.232092383,0.242565875,0.218797732,0,0,2,2,2
+632,630,sub-SPsT1c7m1,21,15.38,1.75,18.39,05/02/2018,Stroke,21,No,-0.108459565,0.620219804,1.593705944,0.701822061,1,1,1,1,1
+633,631,sub-SPsT1.2.2c1m1,21,0,,,27/06/2022,Stroke,21,No,-0.935427004,,,-0.935427004,1,2,2,4,4
+634,632,sub-SPsT1.2.2c1m2,21,0,,,27/06/2022,Stroke,21,No,-0.935427004,,,-0.935427004,0,0,0,0,5
+635,633,sub-SPsT1.2.2c1m3,21,0,,,27/06/2022,Stroke,21,No,-0.935427004,,,-0.935427004,1,2,2,4,4
+636,634,sub-SPsV1c1m1,21,40,2,16.67,02/11/2017,Stroke,21,No,1.215333565,0.817514292,1.316714178,1.116520678,0,2,2,2,2
+637,635,sub-SPsV1c1m3,21,40,0.67,17.24,02/11/2017,Stroke,21,No,1.215333565,-0.232092383,1.408507961,0.797249714,0,2,2,2,2
+638,636,sub-SPsV1c1m4,21,0,0,6.63,02/11/2017,Sham,21,No,-0.935427004,-0.76084161,-0.300144738,-0.665471117,S,S,S,S,S
+639,637,sub-SPsV1c1m5,21,12.5,0.33,5.33,02/11/2017,Sham,21,No,-0.263314326,-0.500412886,-0.50949898,-0.424408731,S,S,S,S,S
+640,638,sub-SPsV1c11m1,21,100,,10,15/10/2018,Stroke,21,No,4.441474418,,0.242565875,2.342020146,0,2,2,2,2
+641,639,sub-SPsV1c11m2,21,40,,4,15/10/2018,Stroke,21,No,1.215333565,,-0.723684473,0.245824546,0,0,3,3,3
+642,640,sub-SPsV1c11m3,21,33.33,,6,15/10/2018,Stroke,21,No,0.85669424,,-0.401601024,0.227546608,0,0,0,0,0
+643,641,sub-SPsV1c11m4,21,0,,3,15/10/2018,Stroke,21,No,-0.935427004,,-0.884726198,-0.910076601,0,2,2,2,2
+644,642,sub-SPsV1c11m5,21,40,,4,15/10/2018,Stroke,21,No,1.215333565,,-0.723684473,0.245824546,1,1,1,4,4
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