1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071727374757677787980818283848586878889 |
- 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')
- df = pd.read_csv(input_file_path)
- df = df[(df["merged_timepoint"]<40)]
- tests = ['paw_dragZ-score', 'hindlimb_dropZ-score', 'foot_faultsZ-score','paw_drag', 'hindlimb_drop', 'foot_faults',"averageScore"]
- #%%
- # Empty list to store the results
- results = []
- for tt in tests:
- for dd in df["dialation_amount"].unique():
- # 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) & (df["dialation_amount"] == dd)]
-
- # Remove rows with NaN values in 'Value' or 'averageScore' columns
- df_f2 = df_f2.dropna(subset=['Value', tt])
-
- if not df_f2.empty:
-
- shapiro_statValue, shapiro_pvalueQValue = shapiro(df_f2["Value"])
- shapiro_statScore, shapiro_pvalueBehavior = shapiro(df_f2[tt])
-
- if shapiro_pvalueQValue < 0.05 or shapiro_pvalueBehavior < 0.05:
- correlation_coefficient, p_value = pearsonr(df_f2["Value"], df_f2[tt])
- else:
- correlation_coefficient, p_value = pearsonr(df_f2["Value"], df_f2[tt])
-
-
- # Store the results in a dictionary
- result = {'Group': ss, 'merged_timepoint': time_point, 'Qtype': qq, 'mask_name': mm,
- 'Pval': p_value, 'R': correlation_coefficient,
- "Behavior_test":tt,"dialation_amount":dd}
-
- # 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)
- unique_groups = df["Group"].unique()
- unique_masks = df["mask_name"].unique()
- unique_tp = df["merged_timepoint"].unique()
- unique_dd=df["dialation_amount"].unique()
- # =============================================================================
- # for dd in unique_dd:
- # for tt in unique_tp:
- # for mask in unique_masks:
- # for group in unique_groups:
- # time_mask = correlation_results_df['merged_timepoint'] == tt
- # mask_mask = correlation_results_df['mask_name'] == mask
- # group_mask = correlation_results_df['Group'] == group
- # dd_mask = correlation_results_df["dialation_amount"] == dd
- # combined_mask = time_mask & mask_mask & group_mask & dd_mask
- #
- # p_values = correlation_results_df[combined_mask]['Pval']
- # rejected, p_values_corrected, _, _ = multipletests(p_values, method='fdr_bh')
- #
- # # Assign the corrected p-values to the DataFrame
- # correlation_results_df.loc[combined_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_behavior.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.")
|