Temporal analysis of MS lesions

data_analysis/data_analysis.py

@@ -0,0 +1,298 @@
+"""
+This python files performs data analysis on the canproco dataset.
+It details the number of subjects and the files available for each subject (for different time points and contrasts).
+It generates a text file containing the results of the analysis and a json file containing the information for each subject.
+It also looks into the participants.tsv file to get the list of healthy controls and see which contrats they have.
+
+Args:
+    -d, --dataset-path: path to the dataset
+    -o, --output-path: path to the output directory
+    -p, --participants-tsv-path: path to the participants.tsv file
+
+Returns:
+    - a csv file containing the results of the analysis
+
+Example:
+    python data_analysis.py -d /path/to/dataset -o /path/to/output -p /path/to/participants.tsv
+
+To do:
+    *
+
+Pierre-Louis Benveniste
+"""
+
+
+import argparse
+import os
+import json
+
+
+def get_parser():
+    """
+    This function parses the arguments given to the script.
+
+    Args:
+        None
+
+    Returns:
+        parser: parser containing the arguments
+    """
+
+    parser = argparse.ArgumentParser(description='Perform data analysis on the canproco dataset')
+    parser.add_argument('-d', '--dataset-path', type=str, required=True, help='path to the dataset')
+    parser.add_argument('-o', '--output-path', type=str, required=True, help='path to the output directory')
+    parser.add_argument('-p', '--participants-tsv-path', type=str, required=True, help='path to the participants.tsv file')
+
+    return parser
+
+
+def get_healthy_control(participants_tsv_path):
+    """
+    This function gets the list of healthy controls.
+
+    Args:
+        participants_tsv_path: path to the participants.tsv file
+
+    Returns:
+        subjects: list of healthy controls
+    """
+    #initialize the list of subjects
+    subjects = []
+
+    #open the participants.tsv file
+    with open(participants_tsv_path, 'r') as f:
+        lines = f.readlines()
+        #iterate over the lines
+        for line in lines[1:]:
+            line = line.split('\t')
+            #if the subject is a healthy control
+            if line[3] == 'HC':
+                subjects.append(line[0])
+    return subjects
+
+
+def main():
+    """
+    This function performs the data analysis.
+
+    Args:
+        None
+
+    Returns:
+        None
+    """
+    # Get the parser
+    parser = get_parser()
+    args = parser.parse_args()
+
+    # Get the arguments
+    dataset_path = args.dataset_path
+    output_path = args.output_path
+    participants_tsv_path = args.participants_tsv_path
+
+    # Get the list of subjects
+    healthy_controls = get_healthy_control(participants_tsv_path)
+
+    #time points (for now we only work on M0)
+    time_points = ['ses-M0', 'ses-M12']
+
+    # Get the list of subjects
+    subjects = os.listdir(dataset_path)
+    subjects = [subject for subject in subjects if 'sub-' in subject]
+    #print("Total number of subjects: {}".format(len(subjects)))
+
+    #initialize lists
+    subjects_all_time_points = []
+    subjects_no_M0 = []
+    subjects_no_M12 = []
+    subjects_PSIR = []
+    subjects_STIR = []
+    subjects_PSIR_STIR = []
+    subjects_no_PSIR_no_STIR = []
+    subjects_no_PSIR_no_STIR_once = []
+    subjects_hc_psir = []
+    subjects_hc_stir = []
+
+    subjects_info = {}
+
+    #Iterate over the subjects
+    for subject in subjects:
+        #iterate over the time_points
+        #print("Subject: {}".format(subject))
+        sub_time_points = []
+        for time_point in time_points:
+             #if time_point exists for the subject
+            if os.path.exists(os.path.join(dataset_path, subject, time_point)):
+                sub_time_points.append(time_point)
+        #print("Time points available: {}".format(sub_time_points))
+        #initialize the contrast_subject dictionary
+        contrast_subject = {}
+        for time_point in sub_time_points:
+            contrast_subject[time_point] = []
+        #iterate over the time points
+        for time_point in sub_time_points:
+            #print("Time point: {}".format(time_point))
+            #get the MRI files for the subject
+            subject_path = os.path.join(dataset_path, subject, time_point, 'anat')
+            subject_files = os.listdir(subject_path)
+            subject_files = [file for file in subject_files if '.nii.gz' in file]
+            #we get the contrast for each file
+            for file in subject_files:
+                contrast_subject[time_point].append(file.split('_')[2].split('.')[0])
+            #we print the contrasts available for the subject
+            #print("Contrasts available: {}".format(sorted(contrast_subject[time_point])))
+        #print(contrast_subject)
+        #print("-----------------------------------")
+        sub_hc = False
+        if subject in healthy_controls:
+            sub_hc = True
+        subject_info = {'subject': subject, 'time_points': sub_time_points, 'contrasts': contrast_subject, 'healthy_control': sub_hc}
+        subjects_info[subject] = subject_info
+
+        #we get the list of the subjects with all the time points
+        if len(sub_time_points) == len(time_points):
+            subjects_all_time_points.append(subject)
+        #we get the list of the subjects with no M0
+        if 'ses-M0' not in sub_time_points:
+            subjects_no_M0.append(subject)
+        #we get the list of the subjects with no M12
+        if 'ses-M12' not in sub_time_points:
+            subjects_no_M12.append(subject)
+
+        #we get the list of the subjects with PSIR at every time point that they have
+        psir_present = True
+        for time_point in sub_time_points:
+            if 'PSIR' not in contrast_subject[time_point]:
+                psir_present = False
+        if psir_present:
+            subjects_PSIR.append(subject)
+        #we get the list of the subjects with STIR at every time point that they have
+        stir_present = True
+        for time_point in sub_time_points:
+            if 'STIR' not in contrast_subject[time_point]:
+                stir_present = False
+        if stir_present:
+            subjects_STIR.append(subject)
+        #we get the list of the subjects with PSIR and STIR at every time point that they have
+        psir_stir_present = True
+        for time_point in sub_time_points:
+            if 'PSIR' not in contrast_subject[time_point] or 'STIR' not in contrast_subject[time_point]:
+                psir_stir_present = False
+        if psir_stir_present:
+            subjects_PSIR_STIR.append(subject)
+        #we get the list of the subjects with no PSIR and no STIR at every time point that they have
+        psir_stir_not_present = True
+        for time_point in sub_time_points:
+            if 'PSIR' in contrast_subject[time_point] or 'STIR' in contrast_subject[time_point]:
+                psir_stir_not_present = False
+        if psir_stir_not_present:
+            subjects_no_PSIR_no_STIR.append(subject)
+        #we get the list of the subjects with no PSIR and no STIR at least once
+        psir_stir_not_present_once = False
+        for time_point in sub_time_points:
+            if 'PSIR' not in contrast_subject[time_point] and 'STIR' not in contrast_subject[time_point]:
+                psir_stir_not_present_once = True
+        if psir_stir_not_present_once:
+            subjects_no_PSIR_no_STIR_once.append(subject)
+        #we get the number of subjects with PSIR at every time point that they have
+        hc_psir_present = True
+        for time_point in sub_time_points:
+            if 'PSIR' not in contrast_subject[time_point] or subject not in healthy_controls:
+                hc_psir_present = False
+        if hc_psir_present:
+            subjects_hc_psir.append(subject)
+        #we get the number of subjects with STIR at every time point that they have
+        hc_stir_present = True
+        for time_point in sub_time_points:
+            if 'STIR' not in contrast_subject[time_point] or subject not in healthy_controls:
+                hc_stir_present = False
+        if hc_stir_present:
+            subjects_hc_psir.append(subject)
+
+    #we print the results
+    print("Total number of subjects: {}".format(len(subjects)))
+    print("Number of healthy controls: {}".format(len(healthy_controls)))
+    print("Number of subjects with all time points: {}".format(len(subjects_all_time_points)))
+    print("Number of subjects with no M0: {}".format(len(subjects_no_M0)))
+    print("Number of subjects with no M12: {}".format(len(subjects_no_M12)))
+    print("Number of subjects with PSIR at every time point they have: {}".format(len(subjects_PSIR)))
+    print("Number of subjects with STIR at every time point they have: {}".format(len(subjects_STIR)))
+    print("Number of subjects with PSIR and STIR at every time point they have: {}".format(len(subjects_PSIR_STIR)))
+    print("Number of subjects with no PSIR and no STIR at every time point they have: {}".format(len(subjects_no_PSIR_no_STIR)))
+    print("Number of subjects with no PSIR and no STIR at least once: {}".format(len(subjects_no_PSIR_no_STIR_once)))
+    print("Number of healthy control with PSIR at every time point they have: {}".format(len(subjects_hc_psir)))
+    print("Number of healthy control with STIR at every time point they have: {}".format(len(subjects_hc_stir)))
+    print("-----------------------------------")
+
+    #we save the subjects_info dictionary in a json file
+    with open(os.path.join(output_path, 'subjects_info.json'), 'w') as fp:
+        json.dump(subjects_info, fp, indent=4)
+
+    #we write a txt file with the results
+    with open(os.path.join(output_path, 'results.txt'), 'w') as f:
+        f.write("Total number of subjects: {}\n".format(len(subjects)))
+        f.write("Number of healthy controls: {}".format(len(healthy_controls)))
+        f.write("Number of subjects with all time points: {}\n".format(len(subjects_all_time_points)))
+        f.write("Number of subjects with no M0: {}\n".format(len(subjects_no_M0)))
+        f.write("Number of subjects with no M12: {}\n".format(len(subjects_no_M12)))
+        f.write("Number of subjects with PSIR at every time point they have: {}\n".format(len(subjects_PSIR)))
+        f.write("Number of subjects with STIR at every time point they have: {}\n".format(len(subjects_STIR)))
+        f.write("Number of subjects with PSIR and STIR at every time point they have: {}\n".format(len(subjects_PSIR_STIR)))
+        f.write("Number of subjects with no PSIR and no STIR at every time point they have: {}\n".format(len(subjects_no_PSIR_no_STIR)))
+        f.write("Number of subjects with no PSIR and no STIR at least once: {}\n".format(len(subjects_no_PSIR_no_STIR_once)))
+        f.write("Number of healthy control with PSIR at every time point they have: {}\n".format(len(subjects_hc_psir)))
+        f.write("Number of healthy control with STIR at every time point they have: {}\n".format(len(subjects_hc_stir)))
+        f.write("-----------------------------------\n")
+        f.write("Subjects with all time points:\n")
+        for subject in subjects_all_time_points:
+            f.write("{}\n".format(subject))
+        f.write("-----------------------------------\n")
+        f.write("Healthy controls:\n")
+        for subject in healthy_controls:
+            f.write("{}\n".format(subject))
+        f.write("-----------------------------------\n")
+        f.write("Subjects with no M0:\n")
+        for subject in subjects_no_M0:
+            f.write("{}\n".format(subject))
+        f.write("-----------------------------------\n")
+        f.write("Subjects with no M12:\n")
+        for subject in subjects_no_M12:
+            f.write("{}\n".format(subject))
+        f.write("-----------------------------------\n")
+        f.write("Subjects with PSIR at every time point they have:\n")
+        for subject in subjects_PSIR:
+            f.write("{}\n".format(subject))
+        f.write("-----------------------------------\n")
+        f.write("Subjects with STIR at every time point they have:\n")
+        for subject in subjects_STIR:
+            f.write("{}\n".format(subject))
+        f.write("-----------------------------------\n")
+        f.write("Subjects with PSIR and STIR at every time point they have:\n")
+        for subject in subjects_PSIR_STIR:
+            f.write("{}\n".format(subject))
+        f.write("-----------------------------------\n")
+        f.write("Subjects with no PSIR and no STIR at every time point they have:\n")
+        for subject in subjects_no_PSIR_no_STIR:
+            f.write("{}\n".format(subject))
+        f.write("-----------------------------------\n")
+        f.write("Subjects with no PSIR and no STIR at least once:\n")
+        for subject in subjects_no_PSIR_no_STIR_once:
+            f.write("{}\n".format(subject))
+        f.write("-----------------------------------\n")
+        f.write("Healthy control with PSIR at every time point they have:\n")
+        for subject in subjects_hc_psir:
+            f.write("{}\n".format(subject))
+        f.write("-----------------------------------\n")
+        f.write("Healthy control with STIR at every time point they have:\n")
+        for subject in subjects_hc_stir:
+            f.write("{}\n".format(subject))
+        f.write("-----------------------------------\n")
+
+    return None
+
+
+if __name__ == '__main__':
+
+    main()
+
+

etc/exclude.yml

@@ -3,3 +3,5 @@
 - sub-cal209_ses-M0   # Missing T2w file
 # MT
 - sub-cal161          # Missing MT data
+# PSIR
+- sub-mon118          # poor image quality