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p3/preprocess/Bipolar_disorder/clinical_data/GSE67311.csv

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+,GSM1644447,GSM1644448,GSM1644449,GSM1644450,GSM1644451,GSM1644452,GSM1644453,GSM1644454,GSM1644455,GSM1644456,GSM1644457,GSM1644458,GSM1644459,GSM1644460,GSM1644461,GSM1644462,GSM1644463,GSM1644464,GSM1644465,GSM1644466,GSM1644467,GSM1644468,GSM1644469,GSM1644470,GSM1644471,GSM1644472,GSM1644473,GSM1644474,GSM1644475,GSM1644476,GSM1644477,GSM1644478,GSM1644479,GSM1644480,GSM1644481,GSM1644482,GSM1644483,GSM1644484,GSM1644485,GSM1644486,GSM1644487,GSM1644488,GSM1644489,GSM1644490,GSM1644491,GSM1644492,GSM1644493,GSM1644494,GSM1644495,GSM1644496,GSM1644497,GSM1644498,GSM1644499,GSM1644500,GSM1644501,GSM1644502,GSM1644503,GSM1644504,GSM1644505,GSM1644506,GSM1644507,GSM1644508,GSM1644509,GSM1644510,GSM1644511,GSM1644512,GSM1644513,GSM1644514,GSM1644515,GSM1644516,GSM1644517,GSM1644518,GSM1644519,GSM1644520,GSM1644521,GSM1644522,GSM1644523,GSM1644524,GSM1644525,GSM1644526,GSM1644527,GSM1644528,GSM1644529,GSM1644530,GSM1644531,GSM1644532,GSM1644533,GSM1644534,GSM1644535,GSM1644536,GSM1644537,GSM1644538,GSM1644539,GSM1644540,GSM1644541,GSM1644542,GSM1644543,GSM1644544,GSM1644545,GSM1644546,GSM1644547,GSM1644548,GSM1644549,GSM1644550,GSM1644551,GSM1644552,GSM1644553,GSM1644554,GSM1644555,GSM1644556,GSM1644557,GSM1644558,GSM1644559,GSM1644560,GSM1644561,GSM1644562,GSM1644563,GSM1644564,GSM1644565,GSM1644566,GSM1644567,GSM1644568,GSM1644569,GSM1644570,GSM1644571,GSM1644572,GSM1644573,GSM1644574,GSM1644575,GSM1644576,GSM1644577,GSM1644578,GSM1644579,GSM1644580,GSM1644581,GSM1644582,GSM1644583,GSM1644584,GSM1644585,GSM1644586,GSM1644587,GSM1644588
+Bipolar_disorder,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,,0.0,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,,0.0,0.0,0.0,0.0,0.0,0.0,,0.0,,0.0,0.0,0.0,0.0,,0.0,0.0,0.0,0.0,0.0,0.0,1.0,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,,0.0,0.0,0.0

p3/preprocess/Bipolar_disorder/clinical_data/GSE92538.csv

@@ -0,0 +1,4 @@
+,GSM2431718,GSM2431721,GSM2431722,GSM2431723,GSM2431726,GSM2431727,GSM2431728,GSM2431730,GSM2431731,GSM2431733,GSM2431734,GSM2431735,GSM2431737,GSM2431738,GSM2431739,GSM2431740,GSM2431743,GSM2431745,GSM2431749,GSM2431750,GSM2431751,GSM2431752,GSM2431753,GSM2431755,GSM2431756,GSM2431758,GSM2431759,GSM2431761,GSM2431762,GSM2431763,GSM2431765,GSM2431768,GSM2431771,GSM2431772,GSM2431773,GSM2431776,GSM2431777,GSM2431780,GSM2431783,GSM2431784,GSM2431785,GSM2431786,GSM2431787,GSM2431788,GSM2431789,GSM2431790,GSM2431791,GSM2431792,GSM2431794,GSM2431795,GSM2431796,GSM2431797,GSM2431800,GSM2431801,GSM2431803,GSM2431804,GSM2431807,GSM2431810,GSM2431811,GSM2431812,GSM2431813,GSM2431814,GSM2431815,GSM2431819,GSM2431820,GSM2431821,GSM2431822,GSM2431824,GSM2431827,GSM2431828,GSM2431830,GSM2431831,GSM2431832,GSM2431833,GSM2431834,GSM2431835,GSM2431836,GSM2431838,GSM2431839,GSM2431840,GSM2431841,GSM2431842,GSM2431844,GSM2431846,GSM2431847,GSM2431850,GSM2431851,GSM2431854,GSM2431855,GSM2431856,GSM2431857,GSM2431859,GSM2431860,GSM2431861,GSM2431862,GSM2431863,GSM2431865,GSM2431869,GSM2431870,GSM2431871,GSM2431872,GSM2431875,GSM2431878,GSM2431879,GSM2431880,GSM2431881,GSM2431882,GSM2431883,GSM2431884,GSM2431885,GSM2431886,GSM2431887,GSM2431888,GSM2431889,GSM2431890,GSM2431891,GSM2431892,GSM2431893,GSM2431894,GSM2431895,GSM2431896,GSM2431897,GSM2431898,GSM2431899,GSM2431902,GSM2431906,GSM2431908,GSM2431909,GSM2431912,GSM2431913,GSM2431914,GSM2431916,GSM2431918,GSM2431919,GSM2431920,GSM2431921,GSM2431923,GSM2431924,GSM2431926,GSM2431927,GSM2431929,GSM2431934,GSM2431935,GSM2431936,GSM2431937,GSM2431938,GSM2431939,GSM2431941,GSM2431942,GSM2431946,GSM2431947,GSM2431949,GSM2431950,GSM2431951,GSM2431954,GSM2431955,GSM2431960,GSM2431961,GSM2431962,GSM2431964,GSM2431965,GSM2431966,GSM2431967,GSM2431972,GSM2431973,GSM2431974,GSM2431975,GSM2431976,GSM2431977,GSM2431978,GSM2431979,GSM2431980,GSM2431981,GSM2431982,GSM2431983,GSM2431986,GSM2431987,GSM2431988,GSM2431989,GSM2431992,GSM2431993,GSM2431994,GSM2431996,GSM2431997,GSM2432001,GSM2432003,GSM2432004,GSM2432006,GSM2432007,GSM2432008,GSM2432009,GSM2432011,GSM2432012,GSM2432013,GSM2432015,GSM2432016,GSM2432019,GSM2432020,GSM2432022,GSM2432023,GSM2432024,GSM2432025,GSM2432026,GSM2432027,GSM2432028,GSM2432030,GSM2432031,GSM2432032,GSM2432033,GSM2432034,GSM2432035,GSM2432036,GSM2432038,GSM2432043,GSM2432044,GSM2432046,GSM2432049,GSM2432050,GSM2432051,GSM2432053,GSM2432056,GSM2432057,GSM2432059,GSM2432061,GSM2432062,GSM2432067,GSM2432072,GSM2432073,GSM2432075,GSM2432080,GSM2432085,GSM2432086,GSM2432088,GSM2432090,GSM2432092
+Bipolar_disorder,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0
+Age,39.0,32.0,63.0,70.0,71.0,44.0,66.0,59.0,65.0,69.0,52.0,49.0,58.0,45.0,72.0,73.0,44.0,64.0,70.0,52.0,77.0,59.0,55.0,49.0,49.0,53.0,62.0,47.0,67.0,57.0,35.0,48.0,19.0,54.0,73.0,48.0,69.0,50.0,48.0,65.0,69.0,56.0,63.0,80.0,60.0,55.0,40.0,39.0,59.0,48.0,39.0,67.0,66.0,18.0,41.0,47.0,50.0,40.0,40.0,41.0,72.0,72.0,64.0,48.0,34.0,77.0,63.0,50.0,40.0,84.0,32.0,58.0,46.0,70.0,73.0,58.0,70.0,23.0,63.0,52.0,39.0,19.0,48.0,64.0,47.0,49.0,64.0,50.0,59.0,25.0,45.0,60.0,78.0,52.0,65.0,68.0,71.0,81.0,54.0,34.0,68.0,43.0,35.0,47.0,26.0,57.0,39.0,59.0,44.0,64.0,59.0,47.0,48.0,48.0,50.0,48.0,40.0,64.0,48.0,48.0,59.0,54.0,43.0,57.0,39.0,32.0,63.0,70.0,71.0,44.0,66.0,59.0,69.0,69.0,49.0,49.0,58.0,58.0,45.0,44.0,64.0,70.0,52.0,77.0,59.0,55.0,55.0,49.0,49.0,53.0,62.0,47.0,67.0,57.0,35.0,48.0,19.0,54.0,54.0,73.0,48.0,69.0,50.0,48.0,65.0,69.0,56.0,56.0,63.0,80.0,60.0,60.0,55.0,40.0,39.0,59.0,48.0,39.0,67.0,66.0,18.0,18.0,41.0,47.0,50.0,40.0,41.0,72.0,72.0,72.0,64.0,48.0,34.0,77.0,50.0,50.0,40.0,84.0,32.0,46.0,70.0,73.0,58.0,58.0,70.0,23.0,23.0,63.0,52.0,39.0,19.0,19.0,48.0,49.0,49.0,64.0,59.0,25.0,45.0,60.0,78.0,52.0,52.0,65.0,65.0,71.0,81.0,54.0,34.0,43.0,35.0,47.0,26.0,26.0,57.0
+Gender,1.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0

p3/preprocess/Bipolar_disorder/clinical_data/GSE93114.csv

@@ -0,0 +1,2 @@
+,GSM2444026,GSM2444027,GSM2444028,GSM2444029,GSM2444030,GSM2444031,GSM2444032,GSM2444033,GSM2444034,GSM2444035,GSM2444036,GSM2444037,GSM2444038,GSM2444039,GSM2444040,GSM2444041
+Bipolar_disorder,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

p3/preprocess/Bipolar_disorder/code/GSE120340.py

@@ -0,0 +1,158 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Bipolar_disorder"
+cohort = "GSE120340"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Bipolar_disorder"
+in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE120340"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Bipolar_disorder/GSE120340.csv"
+out_gene_data_file = "./output/preprocess/3/Bipolar_disorder/gene_data/GSE120340.csv"
+out_clinical_data_file = "./output/preprocess/3/Bipolar_disorder/clinical_data/GSE120340.csv"
+json_path = "./output/preprocess/3/Bipolar_disorder/cohort_info.json"
+
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Create dictionary of unique values for each feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for feature, values in unique_values_dict.items():
+    print(f"\n{feature}:")
+    print(values)
+# 1. Gene Expression Data Availability 
+# From background info, this is an "expression microarray" study
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+trait_row = 0  # 'disease state' contains BD/control info
+age_row = None  # Age not available in sample characteristics
+gender_row = None  # Gender mentioned as matched but not provided
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    if not isinstance(x, str):
+        return None
+    value = x.split(': ')[-1].strip().upper()
+    if value == 'BD(+)' or value == 'BD(-)':
+        return 1  # Any BD type maps to case
+    elif value == 'CONTROL':
+        return 0  # Control maps to control
+    elif value == 'SCZ':
+        return None  # Not relevant for bipolar study
+    return None
+
+def convert_age(x):
+    return None  # No age data
+
+def convert_gender(x):
+    return None  # No gender data
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(is_final=False, 
+                                 cohort=cohort,
+                                 info_path=json_path,
+                                 is_gene_available=is_gene_available,
+                                 is_trait_available=is_trait_available)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(clinical_data, 
+                                                      trait=trait,
+                                                      trait_row=trait_row,
+                                                      convert_trait=convert_trait)
+    
+    # Preview data
+    preview = preview_df(selected_clinical_df)
+    print("Clinical data preview:", preview)
+    
+    # Save clinical features
+    selected_clinical_df.to_csv(out_clinical_data_file)
+# Extract genetic data from the matrix file
+try:
+    genetic_data = get_genetic_data(matrix_file_path)
+    print("First 20 row IDs:")
+    print(genetic_data.index[:20])
+except Exception as e:
+    print(f"Error reading genetic data: {e}")
+    
+# Looking at the row IDs which appear to be gene/transcript identifiers,
+# and considering the background info mentioning "expression microarrays",
+# maintain original assessment that this is gene expression data
+is_gene_available = True  
+
+# Save metadata with correct gene availability info
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path, 
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+# The ids contain "_at" suffix which is characteristic of Affymetrix probe IDs
+# These need to be mapped to gene symbols for consistent analysis
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file_path) 
+
+# Preview column names and first few values
+preview = preview_df(gene_metadata)
+print("\nGene annotation columns and sample values:")
+print(preview)
+# 1. Description column contains gene names 
+prob_col = 'ID'
+gene_col = 'Description'
+
+# 2. Get mapping between probe IDs and gene names
+mapping_data = get_gene_mapping(gene_metadata, prob_col, gene_col)
+
+# 3. Apply mapping to convert probe data to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Preview results
+print("\nFirst few rows and columns of mapped gene expression data:")
+print(gene_data.iloc[:5, :5])
+# 1. Normalize gene symbols and save gene data
+gene_data = normalize_gene_symbols_in_index(gene_data) 
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Judge bias in features and remove biased ones
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Expression microarray data from post-mortem brain samples (BA46) of bipolar disorder patients and controls."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Bipolar_disorder/code/GSE120342.py

@@ -0,0 +1,114 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Bipolar_disorder"
+cohort = "GSE120342"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Bipolar_disorder"
+in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE120342"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Bipolar_disorder/GSE120342.csv"
+out_gene_data_file = "./output/preprocess/3/Bipolar_disorder/gene_data/GSE120342.csv"
+out_clinical_data_file = "./output/preprocess/3/Bipolar_disorder/clinical_data/GSE120342.csv"
+json_path = "./output/preprocess/3/Bipolar_disorder/cohort_info.json"
+
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Create dictionary of unique values for each feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for feature, values in unique_values_dict.items():
+    print(f"\n{feature}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# Based on series title mentioning "transcriptomes", this dataset likely contains gene expression data
+is_gene_available = True 
+
+# 2. Variable Analysis
+# 2.1 Data Availability
+# Trait (BD status) is available in index 0 
+trait_row = 0
+# Age data is not available
+age_row = None  
+# Gender data is not available
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    if not isinstance(value, str):
+        return None
+    value = value.lower().strip()
+    if 'disease state:' in value:
+        value = value.split('disease state:')[1].strip()
+        if value == 'bd(+)' or value == 'bd(-)':  # Both BD+ and BD- are cases
+            return 1
+        elif value == 'control':
+            return 0
+    return None
+
+def convert_age(value):
+    return None  # Not available
+
+def convert_gender(value): 
+    return None  # Not available
+
+# 3. Save metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the processed clinical data
+    preview = preview_df(clinical_features)
+    print("Preview of processed clinical features:")
+    print(preview)
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# List all files to check for gene expression data
+all_files = os.listdir(in_cohort_dir)
+print("All files in directory:")
+for f in all_files:
+    print(f)
+
+# Since we found this is methylation data, and no other matrix file contains gene expression,
+# we need to revise our earlier assessment
+is_gene_available = False  
+
+# Save updated metadata with corrected gene availability info
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort, 
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+print("\nThis dataset contains methylation data rather than gene expression data.")

p3/preprocess/Bipolar_disorder/code/GSE45484.py

@@ -0,0 +1,167 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Bipolar_disorder"
+cohort = "GSE45484"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Bipolar_disorder"
+in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE45484"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Bipolar_disorder/GSE45484.csv"
+out_gene_data_file = "./output/preprocess/3/Bipolar_disorder/gene_data/GSE45484.csv"
+out_clinical_data_file = "./output/preprocess/3/Bipolar_disorder/clinical_data/GSE45484.csv"
+json_path = "./output/preprocess/3/Bipolar_disorder/cohort_info.json"
+
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Create dictionary of unique values for each feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for feature, values in unique_values_dict.items():
+    print(f"\n{feature}:")
+    print(values)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on series description, this is a gene expression study from blood samples
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+trait_row = 2  # 'responder' indicates treatment response status
+age_row = 4  # Contains age information
+gender_row = 3  # Contains sex information
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert treatment response to binary (0: non-responder, 1: responder)"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip().upper()
+    if value == 'NO':
+        return 0
+    elif value == 'YES':
+        return 1
+    return None
+
+def convert_age(value: str) -> float:
+    """Convert age to continuous numeric value"""
+    if not value or ':' not in value:
+        return None
+    try:
+        return float(value.split(':')[1].strip())
+    except:
+        return None
+
+def convert_gender(value: str) -> int:
+    """Convert gender to binary (0: female, 1: male)"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip().upper()
+    if value == 'F':
+        return 0
+    elif value == 'M':
+        return 1
+    return None
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_data,
+        trait="Treatment_Response",
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the processed data
+    preview = preview_df(selected_clinical_df)
+    print("Preview of processed clinical data:")
+    print(preview)
+    
+    # Save to CSV
+    selected_clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs and some data preview to verify structure
+print("First 20 gene/probe IDs:")
+print(list(genetic_data.index[:20]))
+
+print("\nData preview:")
+preview_subset = genetic_data.iloc[:5, :5]
+print(preview_subset)
+# The gene identifiers are ILMN_ probe IDs used in Illumina microarrays
+# These need to be mapped to gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file_path) 
+
+# Preview column names and first few values
+preview = preview_df(gene_metadata)
+print("\nGene annotation columns and sample values:")
+print(preview)
+# Extract gene mapping from annotation
+prob_col = 'ID'  # Column containing probe IDs (ILMN_*)
+gene_col = 'Symbol'  # Column containing gene symbols
+mapping_df = get_gene_mapping(gene_metadata, prob_col, gene_col)
+
+# Apply mapping to convert probe expressions to gene expressions
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Preview the mapped data
+print("First few genes and their expression values:")
+preview = preview_df(gene_data)
+print(preview)
+# 1. Normalize gene symbols and save gene data
+gene_data = normalize_gene_symbols_in_index(gene_data) 
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, "Treatment_Response")
+
+# 4. Judge bias in features and remove biased ones
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, "Treatment_Response")
+
+# 5. Final validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Gene expression data from blood samples of bipolar disorder patients receiving lithium treatment vs non-responders."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Bipolar_disorder/code/GSE46416.py

@@ -0,0 +1,267 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Bipolar_disorder"
+cohort = "GSE46416"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Bipolar_disorder"
+in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE46416"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Bipolar_disorder/GSE46416.csv"
+out_gene_data_file = "./output/preprocess/3/Bipolar_disorder/gene_data/GSE46416.csv"
+out_clinical_data_file = "./output/preprocess/3/Bipolar_disorder/clinical_data/GSE46416.csv"
+json_path = "./output/preprocess/3/Bipolar_disorder/cohort_info.json"
+
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Create dictionary of unique values for each feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for feature, values in unique_values_dict.items():
+    print(f"\n{feature}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# From the series title and summary, this study analyzed gene expression profiles
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+# Trait (bipolar disorder) can be derived from disease status and bd phase
+trait_row = 1  
+# Age and gender not available in sample characteristics
+age_row = None
+gender_row = None
+
+# 2.2 Data Type Conversion
+def convert_trait(value):
+    """Convert disease status to binary: control=0, BD=1"""
+    if pd.isna(value):
+        return None
+    value = value.split(": ")[1].lower()
+    if "control" in value:
+        return 0
+    elif "bipolar disorder" in value or "bd" in value:
+        return 1
+    return None
+
+def convert_age(value):
+    return None  # Not used since age not available
+
+def convert_gender(value):
+    return None  # Not used since gender not available
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+
+# 4. Clinical Feature Extraction 
+# Since trait_row is not None, we extract clinical features
+selected_clinical = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Preview the extracted clinical data
+print("Preview of clinical data:")
+print(preview_df(selected_clinical))
+
+# Save clinical data
+selected_clinical.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs and some data preview to verify structure
+print("First 20 gene/probe IDs:")
+print(list(genetic_data.index[:20]))
+
+print("\nData preview:")
+preview_subset = genetic_data.iloc[:5, :5]
+print(preview_subset)
+# The gene identifiers are numeric codes, not standard human gene symbols
+# These appear to be probe IDs that will need to be mapped to gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data with common GEO gene annotation prefixes
+gene_metadata = get_gene_annotation(soft_file_path, prefixes=['ID_REF', 'GENE', 'GENE_SYMBOL', 'Symbol'])
+
+# Preview the gene metadata to verify we got the annotation data
+preview = preview_df(gene_metadata)
+print("\nGene annotation columns and sample values:")
+print(preview)
+# Let's properly extract platform annotation data from the SOFT file
+with gzip.open(soft_file_path, 'rt') as file:
+    platform_section = False
+    platform_data = []
+    for line in file:
+        if '!Platform_table_begin' in line:
+            platform_section = True
+            # Skip the header line
+            next(file)
+            continue
+        if '!Platform_table_end' in line:
+            platform_section = False
+            continue
+        if platform_section and line.strip():
+            platform_data.append(line.strip().split('\t'))
+
+# Convert to DataFrame
+gene_metadata = pd.DataFrame(platform_data[1:], columns=platform_data[0])
+print("\nPlatform annotation columns:")
+print(gene_metadata.columns.tolist())
+print("\nFirst few rows:")
+print(gene_metadata.head())
+
+# Get mapping between probe IDs and gene symbols
+mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Symbol')
+
+# Convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Preview mapped gene data
+print("\nPreview of gene expression data after mapping:")
+preview_subset = gene_data.iloc[:5, :5]
+print(preview_subset)
+# First read and print some content of the SOFT file to understand its structure
+print("Previewing SOFT file structure:")
+with gzip.open(soft_file_path, 'rt') as file:
+    platform_data = []
+    in_table = False
+    header = None
+    
+    # Read through file line by line
+    for line in file:
+        line = line.strip()
+        
+        # Start collecting data when we reach the platform table
+        if line.startswith('^PLATFORM'):
+            in_table = True
+            continue
+            
+        # Only process lines when we're in the platform section
+        if in_table:
+            # Skip empty lines and comment lines
+            if not line or line.startswith('!') or line.startswith('#'):
+                continue
+                
+            # First non-comment line is the header
+            if header is None:
+                header = line.split('\t')
+                continue
+                
+            # Add data lines to our collection
+            platform_data.append(line.split('\t'))
+            
+        # Stop when we reach the end of platform section    
+        if line.startswith('^SERIES'):
+            break
+
+# Convert to DataFrame
+gene_metadata = pd.DataFrame(platform_data, columns=header)
+
+print("\nPlatform annotation columns:")
+print(gene_metadata.columns.tolist())
+
+print("\nFirst few rows:")
+print(gene_metadata.head())
+
+# Get mapping between probe IDs and gene symbols
+mapping_data = get_gene_mapping(gene_metadata, prob_col='ID_REF', gene_col='Gene Symbol')
+
+# Convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+print("\nPreview of mapped gene data:")
+print(gene_data.iloc[:5, :5])
+
+# Save gene data
+gene_data.to_csv(out_gene_data_file)
+# First read and extract platform annotation data
+platform_data = []
+header = None
+with gzip.open(soft_file_path, 'rt') as file:
+    in_platform = False
+    for line in file:
+        line = line.strip()
+        if line.startswith('!platform_table_begin'):
+            in_platform = True
+            continue
+        elif line.startswith('!platform_table_end'):
+            break
+        elif in_platform:
+            if header is None:
+                header = line.split('\t')
+            else:
+                fields = line.split('\t')
+                if len(fields) == len(header):  # Only add rows matching header length
+                    platform_data.append(fields)
+
+if header is None:
+    raise ValueError("Could not find platform annotation data in SOFT file")
+
+# Convert to DataFrame
+gene_metadata = pd.DataFrame(platform_data, columns=header)
+
+# Get mapping between probe IDs and gene symbols 
+mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Symbol')
+
+# Convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Normalize gene symbols and save gene data
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# Link clinical and genetic data
+clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# Judge bias in features and remove biased ones  
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# Final validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available, 
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Gene expression data from blood samples comparing bipolar disorder patients in manic vs euthymic phases with controls."
+)
+
+# Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file_path) 
+
+# Preview column names and first few values
+preview = preview_df(gene_metadata)
+print("\nGene annotation columns and sample values:")
+print(preview)

p3/preprocess/Bipolar_disorder/code/GSE46449.py

@@ -0,0 +1,157 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Bipolar_disorder"
+cohort = "GSE46449"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Bipolar_disorder"
+in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE46449"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Bipolar_disorder/GSE46449.csv"
+out_gene_data_file = "./output/preprocess/3/Bipolar_disorder/gene_data/GSE46449.csv"
+out_clinical_data_file = "./output/preprocess/3/Bipolar_disorder/clinical_data/GSE46449.csv"
+json_path = "./output/preprocess/3/Bipolar_disorder/cohort_info.json"
+
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Create dictionary of unique values for each feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for feature, values in unique_values_dict.items():
+    print(f"\n{feature}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# The dataset uses Affymetrix microarrays for gene expression, so gene data is available
+is_gene_available = True
+
+# 2.1 Data Row Identification
+trait_row = 1  # 'genotype' contains trait information
+age_row = 2    # 'age' data is available
+gender_row = 3 # 'gender' data is available but shows only male
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    if not isinstance(x, str):
+        return None
+    x = x.lower().split(': ')[-1]
+    if 'bipolar' in x:
+        return 1
+    elif 'control' in x:
+        return 0
+    return None
+
+def convert_age(x):
+    if not isinstance(x, str):
+        return None
+    try:
+        return float(x.split(': ')[-1])
+    except:
+        return None
+
+def convert_gender(x):
+    if not isinstance(x, str):
+        return None
+    x = x.lower().split(': ')[-1]
+    if x == 'male':
+        return 1
+    elif x == 'female':
+        return 0
+    return None
+
+# 3. Save Metadata
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=trait_row is not None)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(clinical_df=clinical_data,
+                                                      trait=trait,
+                                                      trait_row=trait_row,
+                                                      convert_trait=convert_trait,
+                                                      age_row=age_row,
+                                                      convert_age=convert_age,
+                                                      gender_row=gender_row, 
+                                                      convert_gender=convert_gender)
+    
+    # Preview the data
+    preview = preview_df(selected_clinical_df)
+    print("Preview of selected clinical features:")
+    print(preview)
+    
+    # Save to CSV
+    selected_clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs and some data preview to verify structure
+print("First 20 gene/probe IDs:")
+print(list(genetic_data.index[:20]))
+
+print("\nData preview:")
+preview_subset = genetic_data.iloc[:5, :5]
+print(preview_subset)
+# These appear to be probe IDs from Affymetrix arrays (e.g. "1007_s_at" format)
+# They need to be mapped to human gene symbols for analysis
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file_path) 
+
+# Preview column names and first few values
+preview = preview_df(gene_metadata)
+print("\nGene annotation columns and sample values:")
+print(preview)
+# Get gene mapping between probe IDs and gene symbols
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')
+
+# Apply gene mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Preview the gene expression data
+print("Preview of mapped gene expression data:")
+preview_subset = gene_data.iloc[:5, :5] 
+print(preview_subset)
+# 1. Normalize gene symbols and save gene data
+gene_data = normalize_gene_symbols_in_index(gene_data) 
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Judge bias in features and remove biased ones
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Gene expression data from whole blood. Samples include SJIA patients treated with canakinumab/placebo and healthy controls."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Bipolar_disorder/code/GSE53987.py

@@ -0,0 +1,161 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Bipolar_disorder"
+cohort = "GSE53987"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Bipolar_disorder"
+in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE53987"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Bipolar_disorder/GSE53987.csv"
+out_gene_data_file = "./output/preprocess/3/Bipolar_disorder/gene_data/GSE53987.csv"
+out_clinical_data_file = "./output/preprocess/3/Bipolar_disorder/clinical_data/GSE53987.csv"
+json_path = "./output/preprocess/3/Bipolar_disorder/cohort_info.json"
+
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Create dictionary of unique values for each feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for feature, values in unique_values_dict.items():
+    print(f"\n{feature}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# The background info states "hybridized to U133_Plus2 Affymetrix chips" which is a gene expression microarray
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+# Disease state (trait) is in row 7
+trait_row = 7
+# Age is in row 0 
+age_row = 0 
+# Gender is in row 1
+gender_row = 1
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    # Extract value after colon and strip whitespace
+    value = value.split(":")[-1].strip()
+    # Map disease states to binary (control=0, bipolar=1)
+    if value == "control":
+        return 0
+    elif value == "bipolar disorder":
+        return 1
+    # Skip other disease states
+    return None
+
+def convert_age(value):
+    # Extract numeric age value
+    try:
+        age = float(value.split(":")[-1].strip())
+        return age
+    except:
+        return None
+
+def convert_gender(value):
+    # Extract gender value and convert to binary
+    value = value.split(":")[-1].strip()
+    if value == "F":
+        return 0
+    elif value == "M":
+        return 1
+    return None
+
+# 3. Save Metadata
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=trait_row is not None)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(clinical_data, 
+                                                   trait=trait,
+                                                   trait_row=trait_row,
+                                                   convert_trait=convert_trait,
+                                                   age_row=age_row,
+                                                   convert_age=convert_age,  
+                                                   gender_row=gender_row,
+                                                   convert_gender=convert_gender)
+    
+    # Preview the processed clinical data
+    preview = preview_df(clinical_features)
+    print("Preview of clinical features:")
+    print(preview)
+    
+    # Save clinical data
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs and some data preview to verify structure
+print("First 20 gene/probe IDs:")
+print(list(genetic_data.index[:20]))
+
+print("\nData preview:")
+preview_subset = genetic_data.iloc[:5, :5]
+print(preview_subset)
+# These appear to be Affymetrix probe IDs (####_at format) rather than gene symbols
+# They will need to be mapped to official human gene symbols for analysis
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file_path) 
+
+# Preview column names and first few values
+preview = preview_df(gene_metadata)
+print("\nGene annotation columns and sample values:")
+print(preview)
+# Get mapping between probe IDs and gene symbols
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')
+
+# Apply mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Preview gene data after mapping
+print("Data preview after mapping to gene symbols:")  
+preview_subset = gene_data.iloc[:5, :5]
+print(preview_subset)
+# 1. Normalize gene symbols and save gene data
+gene_data = normalize_gene_symbols_in_index(gene_data) 
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Judge bias in features and remove biased ones
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Gene expression data from whole blood. Samples include SJIA patients treated with canakinumab/placebo and healthy controls."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Bipolar_disorder/code/GSE62191.py

@@ -0,0 +1,158 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Bipolar_disorder"
+cohort = "GSE62191"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Bipolar_disorder"
+in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE62191"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Bipolar_disorder/GSE62191.csv"
+out_gene_data_file = "./output/preprocess/3/Bipolar_disorder/gene_data/GSE62191.csv"
+out_clinical_data_file = "./output/preprocess/3/Bipolar_disorder/clinical_data/GSE62191.csv"
+json_path = "./output/preprocess/3/Bipolar_disorder/cohort_info.json"
+
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Create dictionary of unique values for each feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for feature, values in unique_values_dict.items():
+    print(f"\n{feature}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# This is a gene expression study according to title and summary
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Row identification
+trait_row = 1  # Disease state is recorded in row 1
+age_row = 2    # Age is recorded in row 2
+gender_row = 6  # Gender is recorded in row 6
+
+# 2.2 Conversion functions
+def convert_trait(x):
+    if pd.isna(x):
+        return None
+    value = x.split(": ")[1].lower()
+    if "bipolar disorder" in value:
+        return 1
+    elif "healthy control" in value:
+        return 0
+    return None
+
+def convert_age(x):
+    if pd.isna(x):
+        return None
+    try:
+        return int(x.split(": ")[1].split(" ")[0])
+    except:
+        return None
+
+def convert_gender(x):
+    if pd.isna(x):
+        return None
+    value = x.split(": ")[1].lower()
+    if value == "male":
+        return 1
+    elif value == "female":
+        return 0
+    return None
+
+# 3. Save initial validation result
+is_validated = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=trait_row is not None
+)
+
+# 4. Extract clinical features
+selected_clinical = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Preview the extracted features
+preview_result = preview_df(selected_clinical)
+
+# Save clinical data
+selected_clinical.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs and some data preview to verify structure
+print("First 20 gene/probe IDs:")
+print(list(genetic_data.index[:20]))
+
+print("\nData preview:")
+preview_subset = genetic_data.iloc[:5, :5]
+print(preview_subset)
+# The identifiers appear to be numeric probe IDs rather than human gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file_path) 
+
+# Preview column names and first few values
+preview = preview_df(gene_metadata)
+print("\nGene annotation columns and sample values:")
+print(preview)
+# Extract probe_id and gene_symbol mapping from annotation data
+mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# Apply gene mapping to convert probe-level data to gene-level data 
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Preview the gene expression data
+print("\nGene expression data preview:")
+preview_subset = gene_data.iloc[:5, :5]
+print(preview_subset)
+# 1. Normalize gene symbols and save gene data
+gene_data = normalize_gene_symbols_in_index(gene_data) 
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Judge bias in features and remove biased ones
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Gene expression data from whole blood. Samples include SJIA patients treated with canakinumab/placebo and healthy controls."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Bipolar_disorder/code/GSE67311.py

@@ -0,0 +1,154 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Bipolar_disorder"
+cohort = "GSE67311"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Bipolar_disorder"
+in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE67311"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Bipolar_disorder/GSE67311.csv"
+out_gene_data_file = "./output/preprocess/3/Bipolar_disorder/gene_data/GSE67311.csv"
+out_clinical_data_file = "./output/preprocess/3/Bipolar_disorder/clinical_data/GSE67311.csv"
+json_path = "./output/preprocess/3/Bipolar_disorder/cohort_info.json"
+
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Create dictionary of unique values for each feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for feature, values in unique_values_dict.items():
+    print(f"\n{feature}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# Yes, according to the background info this is a gene expression dataset using Affymetrix® Human Gene 1.1 ST arrays
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# Trait (Bipolar disorder) is recorded in row 7
+trait_row = 7
+
+def convert_trait(x):
+    if pd.isna(x):
+        return None
+    value = x.split(': ')[1]
+    if value == 'Yes':
+        return 1
+    elif value == 'No':
+        return 0
+    return None
+
+# Age is not available
+age_row = None 
+convert_age = None
+
+# Gender is not available
+gender_row = None
+convert_gender = None
+
+# 3. Save initial filtering result
+note = "Dataset is primarily focused on fibromyalgia with bipolar disorder only recorded as comorbidity. Very few bipolar cases (~2/140) make it unsuitable for bipolar disorder research."
+
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None),
+    note=note
+)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    clinical_df = geo_select_clinical_features(
+        clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age, 
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the extracted features
+    preview = preview_df(clinical_df)
+    print("Preview of clinical features:", preview)
+    
+    # Save to CSV
+    clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs and some data preview to verify structure
+print("First 20 gene/probe IDs:")
+print(list(genetic_data.index[:20]))
+
+print("\nData preview:")
+preview_subset = genetic_data.iloc[:5, :5]
+print(preview_subset)
+# These numeric identifiers appear to be microarray probe IDs, not gene symbols
+# They are likely Illumina or Affymetrix probe IDs that need to be mapped to gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file_path) 
+
+# Preview column names and first few values
+preview = preview_df(gene_metadata)
+print("\nGene annotation columns and sample values:")
+print(preview)
+# Get gene mapping from annotation data
+# 'ID' column contains probe IDs matching gene expression data
+# Extract gene symbols directly from gene_assignment field using extract_human_gene_symbols
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='gene_assignment')
+
+# Convert probe-level measurements to gene expression data using the library's function
+# The apply_gene_mapping function already includes the extract_human_gene_symbols logic
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Preview results
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows and columns of gene expression data:")
+print(gene_data.iloc[:5, :5])
+# 1. Normalize gene symbols and save gene data
+gene_data = normalize_gene_symbols_in_index(gene_data) 
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Judge bias in features and remove biased ones
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Gene expression data from whole blood. Samples include SJIA patients treated with canakinumab/placebo and healthy controls."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Bipolar_disorder/code/GSE92538.py

@@ -0,0 +1,168 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Bipolar_disorder"
+cohort = "GSE92538"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Bipolar_disorder"
+in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE92538"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Bipolar_disorder/GSE92538.csv"
+out_gene_data_file = "./output/preprocess/3/Bipolar_disorder/gene_data/GSE92538.csv"
+out_clinical_data_file = "./output/preprocess/3/Bipolar_disorder/clinical_data/GSE92538.csv"
+json_path = "./output/preprocess/3/Bipolar_disorder/cohort_info.json"
+
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Create dictionary of unique values for each feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for feature, values in unique_values_dict.items():
+    print(f"\n{feature}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# Based on background info, this dataset contains gene expression data from Affymetrix microarrays
+is_gene_available = True
+
+# 2.1 Data Availability 
+# Trait (Bipolar Disorder) data is in row 2 under 'diagnosis'
+trait_row = 2
+
+# Age data is in row 8
+age_row = 8
+
+# Gender data is in row 6
+gender_row = 6
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x: str) -> Optional[int]:
+    """Convert diagnosis to binary indicating bipolar disorder"""
+    if not x or 'diagnosis:' not in x:
+        return None
+    val = x.split('diagnosis:')[1].strip()
+    if 'Bipolar Disorder' in val:
+        return 1
+    elif val in ['Control', 'Schizophrenia', 'Major Depressive Disorder']:
+        return 0
+    return None
+
+def convert_age(x: str) -> Optional[float]:
+    """Convert age to continuous value"""
+    if not x or 'age:' not in x:
+        return None
+    try:
+        return float(x.split('age:')[1].strip())
+    except:
+        return None
+
+def convert_gender(x: str) -> Optional[int]:
+    """Convert gender to binary (0=female, 1=male)"""
+    if not x or 'gender:' not in x:
+        return None
+    val = x.split('gender:')[1].strip()
+    if val == 'F':
+        return 0
+    elif val == 'M':
+        return 1
+    return None
+
+# 3. Save Metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# 4. Clinical Feature Extraction
+# Extract clinical features using helper function
+selected_clinical = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age, 
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Preview the extracted features
+print("Preview of extracted clinical features:")
+print(preview_df(selected_clinical))
+
+# Save clinical data
+os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+selected_clinical.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs and some data preview to verify structure
+print("First 20 gene/probe IDs:")
+print(list(genetic_data.index[:20]))
+
+print("\nData preview:")
+preview_subset = genetic_data.iloc[:5, :5]
+print(preview_subset)
+# These IDs are in Affymetrix ID format (_at suffix), not gene symbols
+# They need to be mapped to official gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file_path) 
+
+# Preview column names and first few values
+preview = preview_df(gene_metadata)
+print("\nGene annotation columns and sample values:")
+print(preview)
+# 1. Identify relevant columns: 'ID' contains probe IDs and 'SYMBOL' contains gene symbols
+prob_col = 'ID'
+gene_col = 'SYMBOL'
+
+# 2. Get gene mapping dataframe
+mapping_data = get_gene_mapping(gene_metadata, prob_col, gene_col)
+
+# 3. Apply gene mapping to convert probe-level data to gene expression data 
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+# 1. Normalize gene symbols and save gene data
+gene_data = normalize_gene_symbols_in_index(gene_data) 
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Judge bias in features and remove biased ones
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Gene expression data from whole blood. Samples include SJIA patients treated with canakinumab/placebo and healthy controls."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Bipolar_disorder/code/GSE93114.py

@@ -0,0 +1,94 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Bipolar_disorder"
+cohort = "GSE93114"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Bipolar_disorder"
+in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE93114"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Bipolar_disorder/GSE93114.csv"
+out_gene_data_file = "./output/preprocess/3/Bipolar_disorder/gene_data/GSE93114.csv"
+out_clinical_data_file = "./output/preprocess/3/Bipolar_disorder/clinical_data/GSE93114.csv"
+json_path = "./output/preprocess/3/Bipolar_disorder/cohort_info.json"
+
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Create dictionary of unique values for each feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for feature, values in unique_values_dict.items():
+    print(f"\n{feature}:")
+    print(values)
+# 1. Check gene expression data availability
+is_gene_available = True  # Series title mentions "Gene expression data"
+
+# 2.1 Data Availability
+# All samples are bipolar disorder cases - no controls, so trait data not usable
+trait_row = None  # No trait variability
+age_row = None  # Age not available  
+gender_row = None  # Gender not available
+
+# 2.2 Convert functions 
+def convert_trait(val: str) -> int:
+    """Convert bipolar disorder status to binary value"""
+    if not isinstance(val, str):
+        return None
+    val = val.lower()
+    if 'bipolar disorder' in val:
+        return 1
+    elif 'control' in val:
+        return 0
+    return None
+
+def convert_age(val: str) -> float:
+    """Convert age to float - not used since age not available"""
+    return None
+
+def convert_gender(val: str) -> int:
+    """Convert gender to binary - not used since gender not available"""
+    return None
+
+# 3. Save metadata
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# 4. Skip clinical feature extraction since trait_row is None
+# Extract gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs and some data preview to verify structure
+print("First 20 gene/probe IDs:")
+print(list(genetic_data.index[:20]))
+
+print("\nData preview:")
+preview_subset = genetic_data.iloc[:5, :5]
+print(preview_subset)
+# These identifiers appear to be probe IDs from a microarray platform, not human gene symbols
+# They need to be mapped to standard gene symbols for analysis
+requires_gene_mapping = True
+# Update metadata with correct gene availability assessment
+is_gene_available = False  # Dataset contains miRNA, not gene expression data
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)

p3/preprocess/Bipolar_disorder/code/TCGA.py

@@ -0,0 +1,31 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Bipolar_disorder"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Bipolar_disorder/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Bipolar_disorder/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Bipolar_disorder/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Bipolar_disorder/cohort_info.json"
+
+# Check if directory exists for bipolar disorder
+directories = os.listdir(tcga_root_dir)
+relevant_dirs = [d for d in directories if not d.startswith('.') and os.path.isdir(os.path.join(tcga_root_dir, d))]
+
+# No relevant cohort exists for bipolar disorder in TCGA (cancer database)
+is_gene_available = False
+is_trait_available = False
+
+# Record this information and exit pipeline
+validate_and_save_cohort_info(
+    is_final=False, 
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)

p3/preprocess/Bipolar_disorder/gene_data/GSE45484.csv

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+size 21930805

p3/preprocess/Bipolar_disorder/gene_data/GSE46449.csv

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+size 23288499

p3/preprocess/Bipolar_disorder/gene_data/GSE53987.csv

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+size 34613190

p3/preprocess/Bipolar_disorder/gene_data/GSE62191.csv

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+size 16039825

p3/preprocess/Bladder_Cancer/GSE138118.csv

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+size 15335998

p3/preprocess/Bladder_Cancer/GSE222073.csv

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+size 23285441

p3/preprocess/Bladder_Cancer/clinical_data/GSE138118.csv

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p3/preprocess/Bladder_Cancer/clinical_data/GSE162253.csv

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+Bladder_Cancer,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0

p3/preprocess/Bladder_Cancer/clinical_data/GSE245953.csv

@@ -0,0 +1,2 @@
+,GSM7851882,GSM7851883,GSM7851884,GSM7851885,GSM7851886,GSM7851887,GSM7851888,GSM7851889,GSM7851890,GSM7851891,GSM7851892,GSM7851893,GSM7851894,GSM7851895,GSM7851896,GSM7851897,GSM7851898,GSM7851899,GSM7851900,GSM7851901,GSM7851902,GSM7851903,GSM7851904,GSM7851905,GSM7851906,GSM7851907,GSM7851908,GSM7851909,GSM7851910,GSM7851911,GSM7851912,GSM7851913,GSM7851914,GSM7851915,GSM7851916,GSM7851917,GSM7851918,GSM7851919,GSM7851920,GSM7851921,GSM7851922,GSM7851923,GSM7851924,GSM7851925,GSM7851926,GSM7851927,GSM7851928,GSM7851929,GSM7851930,GSM7851931,GSM7851932,GSM7851933,GSM7851934,GSM7851935,GSM7851936,GSM7851937,GSM7851938,GSM7851939,GSM7851940,GSM7851941,GSM7851942,GSM7851943,GSM7851944,GSM7851945,GSM7851946,GSM7851947,GSM7851948,GSM7851949,GSM7851950,GSM7851951,GSM7851952,GSM7851953,GSM7851954,GSM7851955,GSM7851956,GSM7851957,GSM7851958,GSM7851959,GSM7851960,GSM7851961,GSM7851962,GSM7851963,GSM7851964,GSM7851965,GSM7851966,GSM7851967,GSM7851968,GSM7851969,GSM7851970,GSM7851971,GSM7851972,GSM7851973,GSM7851974,GSM7851975,GSM7851976,GSM7851977,GSM7851978,GSM7851979,GSM7851980,GSM7851981,GSM7851982,GSM7851983,GSM7851984,GSM7851985,GSM7851986,GSM7851987,GSM7851988,GSM7851989,GSM7851990,GSM7851991,GSM7851992,GSM7851993,GSM7851994,GSM7851995,GSM7851996,GSM7851997,GSM7851998,GSM7851999,GSM7852000,GSM7852001,GSM7852002,GSM7852003,GSM7852004,GSM7852005,GSM7852006,GSM7852007,GSM7852008,GSM7852009,GSM7852010,GSM7852011,GSM7852012,GSM7852013,GSM7852014,GSM7852015,GSM7852016,GSM7852017,GSM7852018,GSM7852019,GSM7852020,GSM7852021,GSM7852022,GSM7852023,GSM7852024,GSM7852025,GSM7852026,GSM7852027,GSM7852028,GSM7852029,GSM7852030,GSM7852031,GSM7852032,GSM7852033,GSM7852034,GSM7852035,GSM7852036,GSM7852037,GSM7852038,GSM7852039,GSM7852040,GSM7852041,GSM7852042,GSM7852043,GSM7852044,GSM7852045,GSM7852046,GSM7852047,GSM7852048,GSM7852049,GSM7852050,GSM7852051,GSM7852052,GSM7852053,GSM7852054,GSM7852055,GSM7852056,GSM7852057,GSM7852058,GSM7852059,GSM7852060,GSM7852061,GSM7852062,GSM7852063,GSM7852064,GSM7852065,GSM7852066,GSM7852067,GSM7852068,GSM7852069,GSM7852070,GSM7852071,GSM7852072,GSM7852073,GSM7852074,GSM7852075,GSM7852076,GSM7852077,GSM7852078,GSM7852079,GSM7852080,GSM7852081,GSM7852082,GSM7852083,GSM7852084,GSM7852085,GSM7852086,GSM7852087,GSM7852088,GSM7852089,GSM7852090,GSM7852091,GSM7852092,GSM7852093,GSM7852094,GSM7852095,GSM7852096,GSM7852097,GSM7852098,GSM7852099,GSM7852100,GSM7852101,GSM7852102,GSM7852103,GSM7852104,GSM7852105,GSM7852106,GSM7852107,GSM7852108,GSM7852109,GSM7852110,GSM7852111,GSM7852112,GSM7852113,GSM7852114,GSM7852115,GSM7852116,GSM7852117,GSM7852118,GSM7852119,GSM7852120,GSM7852121,GSM7852122,GSM7852123,GSM7852124,GSM7852125,GSM7852126,GSM7852127,GSM7852128,GSM7852129,GSM7852130,GSM7852131,GSM7852132,GSM7852133,GSM7852134,GSM7852135,GSM7852136,GSM7852137,GSM7852138,GSM7852139,GSM7852140,GSM7852141,GSM7852142,GSM7852143,GSM7852144,GSM7852145,GSM7852146,GSM7852147,GSM7852148,GSM7852149,GSM7852150,GSM7852151,GSM7852152,GSM7852153,GSM7852154,GSM7852155,GSM7852156,GSM7852157,GSM7852158,GSM7852159,GSM7852160,GSM7852161,GSM7852162,GSM7852163,GSM7852164,GSM7852165,GSM7852166,GSM7852167,GSM7852168,GSM7852169,GSM7852170,GSM7852171,GSM7852172,GSM7852173,GSM7852174,GSM7852175,GSM7852176,GSM7852177,GSM7852178,GSM7852179,GSM7852180,GSM7852181,GSM7852182,GSM7852183,GSM7852184,GSM7852185,GSM7852186,GSM7852187,GSM7852188,GSM7852189,GSM7852190,GSM7852191
+Bladder_Cancer,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

p3/preprocess/Bladder_Cancer/clinical_data/GSE253531.csv

@@ -0,0 +1,2 @@
+,GSM8022612,GSM8022613,GSM8022614,GSM8022615,GSM8022616,GSM8022617,GSM8022618,GSM8022619,GSM8022620,GSM8022621,GSM8022622,GSM8022623,GSM8022624,GSM8022625,GSM8022626,GSM8022627,GSM8022628,GSM8022629,GSM8022630,GSM8022631,GSM8022632,GSM8022633,GSM8022634,GSM8022635,GSM8022636,GSM8022637,GSM8022638,GSM8022639,GSM8022640,GSM8022641,GSM8022642,GSM8022643,GSM8022644,GSM8022645,GSM8022646,GSM8022647,GSM8022648,GSM8022649,GSM8022650,GSM8022651,GSM8022652,GSM8022653,GSM8022654,GSM8022655,GSM8022656,GSM8022657,GSM8022658,GSM8022659,GSM8022660,GSM8022661,GSM8022662,GSM8022663,GSM8022664,GSM8022665
+Bladder_Cancer,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

p3/preprocess/Bladder_Cancer/code/GSE138118.py

@@ -0,0 +1,179 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Bladder_Cancer"
+cohort = "GSE138118"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Bladder_Cancer"
+in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE138118"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Bladder_Cancer/GSE138118.csv"
+out_gene_data_file = "./output/preprocess/3/Bladder_Cancer/gene_data/GSE138118.csv"
+out_clinical_data_file = "./output/preprocess/3/Bladder_Cancer/clinical_data/GSE138118.csv"
+json_path = "./output/preprocess/3/Bladder_Cancer/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on the series title and summary, this is gene expression data from blood
+
+# 2.1 Data Availability
+trait_row = 0  # "stage at sample" contains cancer status
+age_row = 1    # Age information is available
+gender_row = None  # Gender information is not available in the characteristics
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    if pd.isna(x):
+        return None
+    value = x.split(': ')[-1].strip()
+    # Convert to binary: Healthy=0, Any cancer stage=1
+    if value == 'Healthy':
+        return 0
+    elif value in ['G1', 'G2', 'G3', 'G1 pTa', 'G2 pTa']:
+        return 1
+    return None
+
+def convert_age(x):
+    if pd.isna(x):
+        return None
+    value = x.split(': ')[-1].strip()
+    try:
+        # Convert to continuous numeric value
+        return float(value)
+    except:
+        return None
+
+def convert_gender(x):
+    return None  # Not used since gender data is not available
+
+# 3. Save Initial Filtering Results
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=trait_row is not None
+)
+
+# 4. Extract Clinical Features
+clinical_df = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age
+)
+
+# Preview the processed clinical data
+preview_result = preview_df(clinical_df)
+print("Preview of processed clinical data:")
+print(preview_result)
+
+# Save clinical data
+os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# Observe the gene identifiers
+# The identifiers are numeric strings starting with '16650', which appears to be probe IDs
+# These are not standard human gene symbols and will need to be mapped
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation columns and example values:")
+print(preview_df(gene_annotation))
+# Get gene mapping data from annotation
+# For this dataset:
+# ID column contains probe IDs matching gene expression data
+# gene_assignment column contains gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')
+
+# Apply gene mapping to convert probe expression to gene expression
+# The apply_gene_mapping function handles many-to-many mappings
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Preview data shape and first few rows
+print("Shape of gene expression data:", gene_data.shape) 
+print("\nFirst few rows:")
+print(gene_data.head())
+# 1. Normalize gene symbols and save normalized gene data
+gene_data.index = gene_data.index.str.replace('-mRNA', '')
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# Load previously saved clinical data
+clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features and remove them if needed
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note="NanoString nCounter RNA profiling data for bladder cancer recurrence study"
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Bladder_Cancer/code/GSE145261.py

@@ -0,0 +1,201 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Bladder_Cancer"
+cohort = "GSE145261"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Bladder_Cancer"
+in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE145261"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Bladder_Cancer/GSE145261.csv"
+out_gene_data_file = "./output/preprocess/3/Bladder_Cancer/gene_data/GSE145261.csv"
+out_clinical_data_file = "./output/preprocess/3/Bladder_Cancer/clinical_data/GSE145261.csv"
+json_path = "./output/preprocess/3/Bladder_Cancer/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on context, this dataset studies carcinoma with molecular analysis
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Row identification
+trait_row = 3  # "tissue type" contains SCC vs UC info
+age_row = 0    # "subject age" contains age info
+gender_row = 1 # "subject gender" contains gender info
+
+# 2.2 Conversion functions
+def convert_trait(x):
+    # Binary: SCC (1) vs UC (0) 
+    if not isinstance(x, str):
+        return None
+    x = x.lower()
+    if 'scc' in x or 'small cell' in x:
+        return 1
+    elif 'uc' in x or 'urothelial' in x:
+        return 0
+    return None
+
+def convert_age(x):
+    # Continuous: extract age in years
+    if not isinstance(x, str):
+        return None
+    try:
+        age = int(''.join(filter(str.isdigit, x)))
+        if 0 <= age <= 120:  # Basic age validation
+            return age
+        return None
+    except:
+        return None
+
+def convert_gender(x):
+    # Binary: female (0) vs male (1)
+    if not isinstance(x, str):
+        return None
+    x = x.lower()
+    if 'female' in x:
+        return 0
+    elif 'male' in x:
+        return 1
+    return None
+
+# 3. Save metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=trait_row is not None
+)
+
+# 4. Extract clinical features
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the extracted features
+    preview_data = preview_df(selected_clinical_df)
+    print("Preview of extracted clinical features:", preview_data)
+    
+    # Save to CSV
+    selected_clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# These are Illumina probes (starting with ILMN_), not gene symbols
+# We'll need to map them to human gene symbols for analysis
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file using default prefixes
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation shape:", gene_annotation.shape)
+print("\nGene annotation columns and first few values:")
+print(preview_df(gene_annotation))
+
+# Also inspect the raw SOFT file annotation section to verify parsing
+import gzip
+with gzip.open(soft_file, 'rt') as f:
+    found_table = False
+    lines = []
+    for line in f:
+        if '!platform_table_begin' in line.lower():
+            found_table = True
+            lines.append(next(f))  # Get header line
+            for _ in range(3):  # Get first 3 data lines
+                lines.append(next(f))
+            break
+            
+if found_table:
+    print("\nRaw annotation format in SOFT file:")
+    for line in lines:
+        print(line.strip())
+# Extract gene mapping from annotation
+# 'ID' column contains probe IDs (ILMN_*) matching the gene expression data
+# 'Symbol' column contains gene symbols we want to map to
+gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# Apply gene mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(gene_data, gene_mapping)
+
+# Print info about the conversion
+print("Shape of mapped gene expression data:", gene_data.shape) 
+print("\nFirst few rows of mapped gene expression data:")
+print(gene_data.head())
+# 1. Normalize gene symbols and save normalized gene data
+gene_data.index = gene_data.index.str.replace('-mRNA', '')
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features and remove them if needed
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note="NanoString nCounter RNA profiling data for bladder cancer recurrence study"
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)