temp stage

code/correlation_between_bahavior_and_DTI_anovaSpecific.py

@@ -0,0 +1,101 @@
+import os
+import pandas as pd
+from scipy.stats import spearmanr, shapiro, pearsonr
+import numpy as np
+from statsmodels.stats.multitest import multipletests
+
+# 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", 'Quantitative_results_from_dwi_processing_merged_with_behavior_data.csv')
+
+input_anova_file = os.path.join(parent_dir, 'output', "Final_Quantitative_output","mixed_model_analysis",'mixed_model_results.csv')
+df_annova = pd.read_csv(input_anova_file)
+df_annova2 = df_annova[df_annova["p_value"]<=0.05]
+
+
+
+
+
+
+df_temp = pd.read_csv(input_file_path)
+# Filter df based on condition
+df_f1 = df_temp[df_temp["dialation_amount"] == 0]
+
+# Merge df_f1 and df_annova2 on common columns "Qtype" and "mask_name"
+merged_df = pd.merge(df_f1, df_annova2[['Qtype', 'mask_name', 'p_value']], on=['Qtype', 'mask_name'], how='left')
+
+# Display the merged DataFrame
+print(merged_df)
+
+
+#%%
+# Empty list to store the results
+results = []
+
+df = merged_df[(merged_df["p_value"]<=0.05) & (merged_df["merged_timepoint"]<40)]
+df.dropna(subset=['p_value'], inplace=True)
+
+
+# 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["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:
+                    
+                    shapiro_statValue, shapiro_pvalueQValue = shapiro(df_f2["Value"])
+                    shapiro_statScore, shapiro_pvalueBehavior = shapiro(df_f2["averageScore"])
+                    
+                    if shapiro_pvalueQValue < 0.05 or shapiro_pvalueBehavior < 0.05:
+                        correlation_coefficient, p_value = spearmanr(df_f2["Value"], df_f2["averageScore"])
+                    else:
+                        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': p_value, 'R': correlation_coefficient,
+                              'shapiro-wilk_pvalue_qtype': shapiro_pvalueQValue,'shapiro-wilk_pvalue_behavior': shapiro_pvalueBehavior}
+
+                    # 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 FDR correction to p-values within each Qtype and each Group separately
+unique_qtypes = correlation_results_df['Qtype'].unique()
+unique_groups = df["Group"].unique()
+unique_masks = df["mask_name"].unique()
+unique_tp = df["merged_timepoint"].unique()
+for qtype in unique_qtypes:
+    for tt in unique_tp:
+        time_mask = correlation_results_df['merged_timepoint'] == tt
+        qtype_group_mask = time_mask  & (correlation_results_df['Qtype'] == qtype)
+        p_values = correlation_results_df[qtype_group_mask]['Pval']
+        rejected, p_values_corrected, _, _ = multipletests(p_values, method='fdr_bh')
+    
+        # Assign the corrected p-values to the DataFrame
+        correlation_results_df.loc[qtype_group_mask, 'Pval_corrected'] = p_values_corrected
+        
+# Define the output file path
+output_file_path = os.path.join(parent_dir, 'output', "Correlation_with_behavior", 'correlation_dti_with_behavior6.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/register_masks.py

@@ -57,6 +57,38 @@ def main():
                 
             progress_bar.update(1)
     
+    registered_masks_path = os.path.join(args.input,"**","RegisteredTractMasks", "*.nii.gz")
+    registered_masks_files = glob.glob(registered_masks_path, recursive=True)
+
+    total_iterations = len(registered_masks_files)
+    progress_bar = tqdm(total=total_iterations, desc='Flipping Masks', unit='files')
+
+    for mask_file in registered_masks_files:
+        # Load the mask file
+        mask_nifti = nib.load(mask_file)
+        mask_data = mask_nifti.get_fdata()
+
+        # Flip the mask along the Y and Z axes
+        #flipped_mask = np.flip(np.flip(mask_data, axis=0), axis=1)
+        flipped_mask = np.flip(mask_data, axis=0)
+        
+
+        # Determine the directory to save the adjusted mask
+        mask_dir = os.path.dirname(os.path.dirname(mask_file))
+        adjusted_masks_dir = os.path.join(mask_dir, "RegisteredTractMasks")
+
+
+
+        # Define the new file name and save the flipped mask
+        if "anat" in mask_file:
+            adjusted_mask_file = os.path.join(adjusted_masks_dir, os.path.basename(mask_file).replace(".nii.gz",".nii.gz"))
+            nib.save(nib.Nifti1Image(flipped_mask, mask_nifti.affine), adjusted_mask_file)
+
+        progress_bar.update(1)
+
+    progress_bar.close()
+    print("Flipping of RegisteredTractMasks and saving to RegisteredTractMasks_adjusted: DONE\n")
+        
     progress_bar.close()
     print("Registered all masks to t2w space of individual mice: DONE\n")
     

output/Correlation_with_behavior/correlation_dti_with_behavior6.csv

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