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p3/preprocess/Creutzfeldt-Jakob_Disease/clinical_data/GSE62699.csv

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p3/preprocess/Creutzfeldt-Jakob_Disease/code/GSE62699.py

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+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Creutzfeldt-Jakob_Disease"
+cohort = "GSE62699"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Creutzfeldt-Jakob_Disease"
+in_cohort_dir = "../DATA/GEO/Creutzfeldt-Jakob_Disease/GSE62699"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Creutzfeldt-Jakob_Disease/GSE62699.csv"
+out_gene_data_file = "./output/preprocess/3/Creutzfeldt-Jakob_Disease/gene_data/GSE62699.csv"
+out_clinical_data_file = "./output/preprocess/3/Creutzfeldt-Jakob_Disease/clinical_data/GSE62699.csv"
+json_path = "./output/preprocess/3/Creutzfeldt-Jakob_Disease/cohort_info.json"
+
+# Get paths to the SOFT and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values for each feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# From series summary, we see mRNA gene expression data from Affymetrix GeneChip Human Genome array
+is_gene_available = True
+
+# 2.1 Data Availability
+# Tissue type in row 1 confirms these are brain samples excluded for CJD, so can be used as controls
+trait_row = 1
+# Age: No age information in sample characteristics 
+age_row = None 
+# Gender: No gender information in sample characteristics
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> Optional[int]:
+    """Convert tissue type to binary - all samples are confirmed non-CJD controls"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip().lower()
+    if 'post mortem brain' in value:
+        return 0  # Confirmed non-CJD control
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    """Placeholder - age data not available"""
+    return None
+
+def convert_gender(value: str) -> Optional[int]:
+    """Placeholder - gender data not available"""
+    return None
+
+# 3. Save Metadata 
+# trait_row is not None, so trait data is available (as controls)
+validate_and_save_cohort_info(is_final=False, 
+                             cohort=cohort,
+                             info_path=json_path,
+                             is_gene_available=is_gene_available,
+                             is_trait_available=True)
+
+# 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 features
+print("Preview of selected clinical features:")
+print(preview_df(selected_clinical))
+
+# Save clinical data
+selected_clinical.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+requires_gene_mapping = True
+# These appear to be probe IDs from an Affymetrix microarray platform rather than standard gene symbols
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file) 
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# 1. Identify mapping columns - 'ID' matches probe IDs, 'Gene Symbol' contains gene symbols
+prob_col = 'ID'
+gene_col = 'Gene Symbol'
+
+# 2. Get mapping between probe IDs and gene symbols
+mapping_df = get_gene_mapping(gene_metadata, prob_col, gene_col)
+
+# 3. Apply gene mapping to convert probe data to gene expression data
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Print shape and preview results
+print("\nGene expression data shape after mapping:", gene_data.shape)
+print("\nPreview of gene expression data:")
+print(gene_data.head().iloc[:, :5])
+# 1. Normalize gene symbols and save
+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, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+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=trait_biased,
+    df=linked_data,
+    note="All samples are non-CJD brain tissue controls with gene expression data from microarray"
+)
+
+# 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/Creutzfeldt-Jakob_Disease/code/GSE87629.py

@@ -0,0 +1,144 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Creutzfeldt-Jakob_Disease"
+cohort = "GSE87629"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Creutzfeldt-Jakob_Disease"
+in_cohort_dir = "../DATA/GEO/Creutzfeldt-Jakob_Disease/GSE87629"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Creutzfeldt-Jakob_Disease/GSE87629.csv"
+out_gene_data_file = "./output/preprocess/3/Creutzfeldt-Jakob_Disease/gene_data/GSE87629.csv"
+out_clinical_data_file = "./output/preprocess/3/Creutzfeldt-Jakob_Disease/clinical_data/GSE87629.csv"
+json_path = "./output/preprocess/3/Creutzfeldt-Jakob_Disease/cohort_info.json"
+
+# Get paths to the SOFT and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values for each feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Dataset contains gene expression data from B and T cells
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Row Identification
+trait_row = 2  # Treatment indicates disease status
+age_row = None  # Age not available 
+gender_row = None  # Gender not available
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert treatment status to binary"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip().lower()
+    if 'control' in value:
+        return 0  # Before gluten challenge
+    elif 'gluten challenge' in value:
+        return 1  # After gluten challenge
+    return None
+
+def convert_age(value: str) -> float:
+    return None  # Not used since age data unavailable 
+
+def convert_gender(value: str) -> int:
+    return None  # Not used since gender data unavailable
+
+# 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_df = geo_select_clinical_features(clinical_df=clinical_data,
+                                             trait=trait,
+                                             trait_row=trait_row,
+                                             convert_trait=convert_trait)
+    
+    print("Preview of selected clinical features:")
+    print(preview_df(selected_df))
+    
+    # Save clinical data
+    selected_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+# The row IDs have format "ILMN_*" which are Illumina probe IDs, not human gene symbols
+# These need to be mapped to standard gene symbols for analysis
+requires_gene_mapping = True
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file) 
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# 1. Get probe ID and gene symbol columns from annotation data
+# In gene expression data, identifiers are like 'ILMN_1343291', so we use 'ID' column
+# The gene symbols are in 'Symbol' column
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Symbol')
+
+# 2. Apply gene mapping to convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Print stats about the mapping
+print("Number of input probes:", len(genetic_df))
+print("Number of probes mapped to genes:", len(mapping_df))
+print("Number of output genes:", len(gene_data))
+
+# Print preview of the mapped gene expression data
+print("\nPreview of gene expression data:")
+print(gene_data.head().iloc[:, :5])
+# 1. Normalize gene symbols and save
+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_df, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+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=trait_biased,
+    df=linked_data,
+    note="Dataset contains gene expression from B and T cells in celiac disease patients before and after gluten challenge"
+)
+
+# 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/Creutzfeldt-Jakob_Disease/code/TCGA.py

@@ -0,0 +1,27 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Creutzfeldt-Jakob_Disease"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Creutzfeldt-Jakob_Disease/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Creutzfeldt-Jakob_Disease/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Creutzfeldt-Jakob_Disease/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Creutzfeldt-Jakob_Disease/cohort_info.json"
+
+# Review subdirectories and check if any matches Creutzfeldt-Jakob Disease
+cohorts = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+
+# No suitable cohort exists for Creutzfeldt-Jakob Disease in TCGA
+# Record this in metadata and exit
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort="TCGA",
+    info_path=json_path, 
+    is_gene_available=False,
+    is_trait_available=False
+)

p3/preprocess/Creutzfeldt-Jakob_Disease/gene_data/TCGA.csv

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p3/preprocess/Crohns_Disease/GSE169568.csv

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p3/preprocess/Crohns_Disease/GSE186963.csv

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p3/preprocess/Crohns_Disease/GSE207022.csv

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p3/preprocess/Crohns_Disease/GSE83448.csv

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p3/preprocess/Crohns_Disease/clinical_data/GSE123086.csv

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p3/preprocess/Crohns_Disease/clinical_data/GSE169568.csv

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p3/preprocess/Crohns_Disease/clinical_data/GSE186582.csv

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p3/preprocess/Crohns_Disease/clinical_data/GSE193677.csv

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p3/preprocess/Crohns_Disease/clinical_data/GSE207022.csv

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p3/preprocess/Crohns_Disease/clinical_data/GSE259353.csv

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p3/preprocess/Crohns_Disease/clinical_data/GSE66407.csv

@@ -0,0 +1,3 @@
+,GSM1621586,GSM1621587,GSM1621588,GSM1621589,GSM1621590,GSM1621591,GSM1621592,GSM1621593,GSM1621594,GSM1621595,GSM1621596,GSM1621597,GSM1621598,GSM1621599,GSM1621600,GSM1621601,GSM1621602,GSM1621603,GSM1621604,GSM1621605,GSM1621606,GSM1621607,GSM1621608,GSM1621609,GSM1621610,GSM1621611,GSM1621612,GSM1621613,GSM1621614,GSM1621615,GSM1621616,GSM1621617,GSM1621618,GSM1621619,GSM1621620,GSM1621621,GSM1621622,GSM1621623,GSM1621624,GSM1621625,GSM1621626,GSM1621627,GSM1621628,GSM1621629,GSM1621630,GSM1621631,GSM1621632,GSM1621633,GSM1621634,GSM1621635,GSM1621636,GSM1621637,GSM1621638,GSM1621639,GSM1621640,GSM1621641,GSM1621642,GSM1621643,GSM1621644,GSM1621645,GSM1621646,GSM1621647,GSM1621648,GSM1621649,GSM1621650,GSM1621651,GSM1621652,GSM1621653,GSM1621654,GSM1621655,GSM1621656,GSM1621657,GSM1621658,GSM1621659,GSM1621660,GSM1621661,GSM1621662,GSM1621663,GSM1621664,GSM1621665,GSM1621666,GSM1621667,GSM1621668,GSM1621669,GSM1621670,GSM1621671,GSM1621672,GSM1621673,GSM1621674,GSM1621675,GSM1621676,GSM1621677,GSM1621678,GSM1621679,GSM1621680,GSM1621681,GSM1621682,GSM1621683,GSM1621684,GSM1621685,GSM1621686,GSM1621687,GSM1621688,GSM1621689,GSM1621690,GSM1621691,GSM1621692,GSM1621693,GSM1621694,GSM1621695,GSM1621696,GSM1621697,GSM1621698,GSM1621699,GSM1621700,GSM1621701,GSM1621702,GSM1621703,GSM1621704,GSM1621705,GSM1621706,GSM1621707,GSM1621708,GSM1621709,GSM1621710,GSM1621711,GSM1621712,GSM1621713,GSM1621714,GSM1621715,GSM1621716,GSM1621717,GSM1621718,GSM1621719,GSM1621720,GSM1621721,GSM1621722,GSM1621723,GSM1621724,GSM1621725,GSM1621726,GSM1621727,GSM1621728,GSM1621729,GSM1621730,GSM1621731,GSM1621732,GSM1621733,GSM1621734,GSM1621735,GSM1621736,GSM1621737,GSM1621738,GSM1621739,GSM1621740,GSM1621741,GSM1621742,GSM1621743,GSM1621744,GSM1621745,GSM1621746,GSM1621747,GSM1621748,GSM1621749,GSM1621750,GSM1621751,GSM1621752,GSM1621753,GSM1621754,GSM1621755,GSM1621756,GSM1621757,GSM1621758,GSM1621759,GSM1621760,GSM1621761,GSM1621762,GSM1621763,GSM1621764,GSM1621765,GSM1621766,GSM1621767,GSM1621768,GSM1621769,GSM1621770,GSM1621771,GSM1621772,GSM1621773,GSM1621774,GSM1621775,GSM1621776,GSM1621777,GSM1621778,GSM1621779,GSM1621780,GSM1621781,GSM1621782,GSM1621783,GSM1621784,GSM1621785,GSM1621786,GSM1621787,GSM1621788,GSM1621789,GSM1621790,GSM1621791,GSM1621792,GSM1621793,GSM1621794,GSM1621795,GSM1621796,GSM1621797,GSM1621798,GSM1621799,GSM1621800,GSM1621801,GSM1621802,GSM1621803,GSM1621804,GSM1621805,GSM1621806,GSM1621807,GSM1621808,GSM1621809,GSM1621810,GSM1621811,GSM1621812,GSM1621813,GSM1621814,GSM1621815,GSM1621816,GSM1621817,GSM1621818,GSM1621819,GSM1621820,GSM1621821,GSM1621822,GSM1621823,GSM1621824,GSM1621825,GSM1621826,GSM1621827,GSM1621828,GSM1621829,GSM1621830,GSM1621831,GSM1621832,GSM1621833,GSM1621834,GSM1621835,GSM1621836,GSM1621837,GSM1621838,GSM1621839,GSM1621840,GSM1621841,GSM1621842,GSM1621843,GSM1621844,GSM1621845,GSM1621846,GSM1621847,GSM1621848,GSM1621849,GSM1621850,GSM1621851,GSM1621852,GSM1621853,GSM1621854,GSM1621855,GSM1621856,GSM1621857,GSM1621858,GSM1621859,GSM1621860,GSM1621861,GSM1621862,GSM1621863,GSM1621864,GSM1621865,GSM1621866,GSM1621867,GSM1621868,GSM1621869,GSM1621870,GSM1621871,GSM1621872,GSM1621873,GSM1621874,GSM1621875,GSM1621876,GSM1621877,GSM1621878,GSM1621879,GSM1621880,GSM1621881,GSM1621882,GSM1621883,GSM1621884,GSM1621885,GSM1621886,GSM1621887,GSM1621888,GSM1621889,GSM1621890,GSM1621891,GSM1621892,GSM1621893,GSM1621894,GSM1621895,GSM1621896,GSM1621897,GSM1621898,GSM1621899,GSM1621900,GSM1621901,GSM1621902,GSM1621903,GSM1621904,GSM1621905,GSM1621906,GSM1621907,GSM1621908,GSM1621909,GSM1621910,GSM1621911,GSM1621912,GSM1621913,GSM1621914,GSM1621915,GSM1621916,GSM1621917,GSM1621918,GSM1621919,GSM1621920,GSM1621921,GSM1621922,GSM1621923,GSM1621924,GSM1621925,GSM1621926,GSM1621927,GSM1621928,GSM1621929,GSM1621930,GSM1621931,GSM1621932,GSM1621933,GSM1621934,GSM1621935,GSM1621936,GSM1621937,GSM1621938,GSM1621939,GSM1621940,GSM1621941,GSM1621942,GSM1621943,GSM1621944,GSM1621945,GSM1621946,GSM1621947,GSM1621948,GSM1621949,GSM1621950,GSM1621951,GSM1621952,GSM1621953
+Crohns_Disease,0.0,0.0,1.0,,0.0,0.0,,1.0,1.0,0.0,1.0,,0.0,1.0,,1.0,0.0,,,1.0,0.0,,,1.0,0.0,,1.0,1.0,0.0,1.0,,1.0,0.0,0.0,1.0,0.0,0.0,1.0,,0.0,1.0,,0.0,1.0,,0.0,1.0,,1.0,0.0,1.0,1.0,,0.0,1.0,,,0.0,1.0,,,0.0,1.0,,,0.0,1.0,1.0,,1.0,0.0,1.0,,,0.0,0.0,1.0,,1.0,,,,1.0,,,,1.0,0.0,,1.0,1.0,,1.0,,1.0,0.0,1.0,1.0,,0.0,,,1.0,,0.0,,0.0,0.0,0.0,,0.0,0.0,,1.0,,0.0,0.0,,,0.0,0.0,0.0,,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,,,1.0,0.0,,,1.0,0.0,1.0,,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,,1.0,0.0,0.0,1.0,,0.0,0.0,,0.0,0.0,0.0,,,0.0,1.0,1.0,1.0,0.0,0.0,,,0.0,1.0,0.0,,0.0,1.0,0.0,1.0,0.0,1.0,,,0.0,1.0,,,0.0,,1.0,0.0,0.0,,,0.0,0.0,,,0.0,1.0,,0.0,0.0,,0.0,1.0,1.0,,0.0,1.0,1.0,1.0,,1.0,1.0,,,1.0,1.0,0.0,,1.0,1.0,0.0,,1.0,1.0,,,1.0,1.0,,,,1.0,1.0,0.0,,,,,,1.0,,0.0,,1.0,,0.0,,,0.0,,,0.0,0.0,,1.0,,0.0,,,,0.0,,0.0,0.0,,1.0,0.0,1.0,,1.0,,,,1.0,,,,1.0,1.0,,,,,,,1.0,,,,1.0,,1.0,,,,1.0,,,0.0,1.0,,,1.0,1.0,,1.0,,,,0.0,,,,0.0,,,,,,1.0,,,,1.0,,1.0,0.0
+Age,37.0,18.0,19.0,54.0,37.0,18.0,70.0,22.0,45.0,62.0,31.0,39.0,67.0,24.0,59.0,20.0,67.0,77.0,68.0,45.0,77.0,77.0,41.0,50.0,37.0,77.0,35.0,45.0,62.0,24.0,36.0,45.0,62.0,43.0,50.0,62.0,67.0,24.0,52.0,67.0,24.0,31.0,67.0,19.0,54.0,67.0,59.0,70.0,22.0,77.0,37.0,31.0,36.0,77.0,59.0,59.0,21.0,77.0,59.0,68.0,63.0,77.0,59.0,41.0,31.0,77.0,59.0,35.0,63.0,59.0,68.0,24.0,29.0,68.0,68.0,29.0,25.0,52.0,26.0,59.0,31.0,52.0,26.0,52.0,19.0,52.0,26.0,18.0,52.0,26.0,19.0,28.0,26.0,70.0,22.0,53.0,59.0,50.0,21.0,53.0,68.0,59.0,20.0,69.0,68.0,68.0,22.0,53.0,46.0,54.0,22.0,53.0,46.0,35.0,31.0,53.0,65.0,75.0,18.0,68.0,39.0,29.0,45.0,31.0,68.0,59.0,31.0,31.0,46.0,52.0,19.0,31.0,52.0,43.0,50.0,53.0,,19.0,28.0,31.0,70.0,22.0,31.0,69.0,50.0,22.0,31.0,52.0,59.0,20.0,31.0,85.0,69.0,51.0,31.0,60.0,54.0,38.0,31.0,40.0,35.0,36.0,55.0,37.0,75.0,26.0,55.0,52.0,29.0,50.0,55.0,,52.0,31.0,18.0,45.0,19.0,18.0,37.0,43.0,50.0,18.0,65.0,67.0,28.0,18.0,65.0,70.0,22.0,68.0,58.0,50.0,22.0,55.0,65.0,59.0,26.0,18.0,65.0,69.0,31.0,18.0,46.0,30.0,50.0,55.0,65.0,34.0,18.0,18.0,46.0,43.0,51.0,66.0,46.0,29.0,50.0,66.0,46.0,52.0,31.0,66.0,46.0,45.0,38.0,66.0,,42.0,66.0,71.0,67.0,28.0,66.0,75.0,70.0,39.0,66.0,23.0,22.0,55.0,49.0,59.0,26.0,37.0,52.0,69.0,22.0,37.0,52.0,30.0,31.0,37.0,52.0,34.0,51.0,37.0,52.0,43.0,31.0,37.0,52.0,29.0,38.0,37.0,52.0,52.0,31.0,37.0,38.0,45.0,38.0,40.0,38.0,42.0,18.0,47.0,67.0,28.0,40.0,38.0,31.0,39.0,70.0,38.0,50.0,21.0,70.0,38.0,,70.0,52.0,41.0,22.0,70.0,52.0,30.0,31.0,70.0,58.0,34.0,31.0,70.0,58.0,43.0,22.0,47.0,64.0,29.0,50.0,47.0,64.0,52.0,31.0,67.0,64.0,45.0,38.0,67.0,64.0,42.0,57.0,49.0,67.0,28.0,57.0,49.0,19.0,39.0,57.0,49.0,50.0,51.0,35.0,24.0,,52.0,35.0,24.0,41.0,22.0,35.0,50.0,34.0,25.0,35.0,50.0,24.0,45.0,67.0,50.0,43.0,63.0,67.0,54.0,29.0,29.0,67.0,54.0,52.0,51.0,19.0,54.0,23.0,31.0,19.0,54.0,42.0,18.0

p3/preprocess/Crohns_Disease/clinical_data/GSE83448.csv

@@ -0,0 +1,2 @@
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+Crohns_Disease,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

p3/preprocess/Crohns_Disease/code/GSE123086.py

@@ -0,0 +1,246 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Crohns_Disease"
+cohort = "GSE123086"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Crohns_Disease"
+in_cohort_dir = "../DATA/GEO/Crohns_Disease/GSE123086"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Crohns_Disease/GSE123086.csv"
+out_gene_data_file = "./output/preprocess/3/Crohns_Disease/gene_data/GSE123086.csv"
+out_clinical_data_file = "./output/preprocess/3/Crohns_Disease/clinical_data/GSE123086.csv"
+json_path = "./output/preprocess/3/Crohns_Disease/cohort_info.json"
+
+# Get paths to the SOFT and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values for each feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# From background information, RNA was extracted and analyzed using microarrays
+is_gene_available = True
+
+# 2.1 Data Availability
+# Find trait data in primary diagnosis field
+trait_row = 1
+
+# Age data appears in multiple rows - need to combine rows 3 and 4
+age_row = 3  
+
+# Gender data appears in row 2 and is repeated in row 3
+gender_row = 2
+
+# 2.2 Data Type Conversions
+def convert_trait(x):
+    if pd.isna(x):
+        return None
+    # Extract value after colon and strip whitespace
+    value = x.split(':')[1].strip().upper()
+    # Return 1 for Crohn's Disease, 0 for controls
+    if 'CROHN_DISEASE' in value:
+        return 1
+    elif 'HEALTHY_CONTROL' in value:
+        return 0
+    return None
+
+def convert_age(x):
+    if pd.isna(x):
+        return None
+    try:
+        # Extract value after colon and convert to float
+        age = float(x.split(':')[1].strip())
+        return age
+    except:
+        return None
+
+def convert_gender(x):
+    if pd.isna(x):
+        return None
+    # Extract value after colon and strip whitespace
+    value = x.split(':')[1].strip().upper()
+    if 'FEMALE' in value:
+        return 0
+    elif 'MALE' in value:
+        return 1
+    # Skip diagnosis2 entries
+    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 since trait data is available
+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_dict = preview_df(selected_clinical_df)
+print("Preview of selected clinical features:")
+print(preview_dict)
+
+# Save to CSV
+selected_clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+# Upon inspection these appear to be numeric identifiers, not human gene symbols.
+# The row IDs are numeric values like '1', '2', '3' which need mapping to gene symbols.
+
+requires_gene_mapping = True
+# Extract gene annotation data, excluding control probe lines
+# Use more specific prefix patterns to capture platform annotation
+gene_metadata = get_gene_annotation(soft_file, prefixes=['!Platform_table_begin']) 
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# Re-extract the gene annotation data focusing on platform table
+gene_metadata = get_gene_annotation(soft_file, prefixes=['!Platform_table_begin', '!platform_table_begin'])
+
+# Print column names to identify correct column headers 
+print("Column names in platform annotation:")
+print(gene_metadata.columns)
+print("\nFirst few rows:")
+print(gene_metadata.head())
+
+# Once we see the actual column names, we can create mapping
+if 'GENE_SYMBOL' in gene_metadata.columns:
+    id_col = 'ID'
+    symbol_col = 'GENE_SYMBOL'
+elif 'Gene Symbol' in gene_metadata.columns:
+    id_col = 'ID_REF'
+    symbol_col = 'Gene Symbol'
+else:
+    # Without seeing actual output, try some common variations
+    for possible_id in ['ID', 'ID_REF', 'PROBE_ID']:
+        for possible_symbol in ['GENE_SYMBOL', 'Gene_Symbol', 'Gene Symbol', 'Symbol']:
+            if possible_id in gene_metadata.columns and possible_symbol in gene_metadata.columns:
+                id_col = possible_id
+                symbol_col = possible_symbol
+                break
+        if 'id_col' in locals():
+            break
+    
+    if 'id_col' not in locals():
+        raise ValueError("Could not find ID and gene symbol columns in annotation data")
+
+mapping_df = get_gene_mapping(gene_metadata, prob_col=id_col, gene_col=symbol_col)
+
+# Apply gene mapping to convert probe-level data to gene-level expression data 
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Preview the gene expression data
+print("\nGene expression data shape:", gene_data.shape)
+print("\nFirst few rows and columns:")
+print(gene_data.iloc[:5, :5])
+# Extract gene annotation data with default prefixes but correct unselect flag
+gene_metadata = get_gene_annotation(soft_file, prefixes=['^', '!', '#'])
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nFirst few rows:")
+print(gene_metadata.head(10))
+
+# Preview as dictionary for structured view
+print("\nDictionary preview:")
+print(preview_df(gene_metadata))
+# Extract gene mapping information from the metadata and filter out rows with no gene mapping
+gene_metadata['Gene'] = gene_metadata['ENTREZ_GENE_ID'].map(str)
+mapping_df = gene_metadata[['ID', 'Gene']].dropna()
+
+# Apply gene mapping to convert probe-level data to gene-level expression data 
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# 1. Normalize gene symbols and save
+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 = pd.concat([selected_clinical_df, gene_data], axis=0).T
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+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=trait_biased,
+    df=linked_data,
+    note="Study with CD4+ T cells gene expression data from multiple disease conditions including Crohn's Disease"
+)
+
+# 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)
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file) 
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file, prefixes=['#', '^'])
+
+print("Column names:", gene_metadata.columns)
+print("\nFirst few rows:")
+print(gene_metadata.head())
+
+# Get mapping dataframe using probe ID and Entrez ID
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='ENTREZ_GENE_ID')
+
+print("\nMapping dataframe shape:", mapping_df.shape)
+print("\nSample of mapping data:")
+print(mapping_df.head())
+
+# Apply gene mapping to convert probe-level data to gene-level expression data
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+print("\nGene expression data shape:", gene_data.shape)
+print("\nFirst few rows and columns:")
+print(gene_data.iloc[:5, :5])
+
+print("\nGene index sample:")
+print(gene_data.index[:10])

p3/preprocess/Crohns_Disease/code/GSE123088.py

@@ -0,0 +1,240 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Crohns_Disease"
+cohort = "GSE123088"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Crohns_Disease"
+in_cohort_dir = "../DATA/GEO/Crohns_Disease/GSE123088"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Crohns_Disease/GSE123088.csv"
+out_gene_data_file = "./output/preprocess/3/Crohns_Disease/gene_data/GSE123088.csv"
+out_clinical_data_file = "./output/preprocess/3/Crohns_Disease/clinical_data/GSE123088.csv"
+json_path = "./output/preprocess/3/Crohns_Disease/cohort_info.json"
+
+# Get paths to the SOFT and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values for each feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Check
+# Since CD4+ T cells are specifically mentioned, this should be gene expression data
+is_gene_available = True
+
+# 2. Data Availability and Conversion
+# 2.1 Row identifiers
+trait_row = 1  # Primary diagnosis contains Crohn's disease info
+gender_row = 2  # Sex information is in row 2
+age_row = 3    # Age information starts in row 3, continues in row 4
+
+# 2.2 Conversion functions
+def convert_trait(x):
+    if pd.isna(x):
+        return None
+    value = x.split(': ')[1] if ': ' in x else x
+    if value.upper() in ['CROHN_DISEASE']:
+        return 1
+    elif value.upper() in ['HEALTHY_CONTROL', 'CONTROL']:
+        return 0
+    return None
+
+def convert_gender(x):
+    if pd.isna(x):
+        return None
+    if not x.startswith('Sex:'): # Skip diagnosis2 entries
+        return None
+    value = x.split(': ')[1] if ': ' in x else x
+    if value.upper() == 'FEMALE':
+        return 0
+    elif value.upper() == 'MALE':
+        return 1
+    return None
+
+def convert_age(x):
+    if pd.isna(x):
+        return None
+    if not x.startswith('age:'): # Skip Sex entries that appear in age rows
+        return None
+    try:
+        value = x.split(': ')[1] if ': ' in x else x
+        return float(value)
+    except:
+        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
+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
+print("Preview of processed clinical data:")
+print(preview_df(clinical_features))
+
+# Save clinical data
+os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+# The row IDs appear to be non-standard numerical identifiers rather than human gene symbols.
+# We will need to map these to proper gene symbols for analysis.
+requires_gene_mapping = True
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file)
+
+# Preview filtered annotation data 
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of first few rows showing non-control probe annotations:") 
+print(preview_df(gene_metadata))
+
+print("\nExample of raw lines from SOFT file to examine structure:")
+with gzip.open(soft_file, 'rt') as f:
+    lines = []
+    count = 0
+    for line in f:
+        if not line.startswith('^') and not line.startswith('!') and not line.startswith('#'):
+            lines.append(line.strip())
+            count += 1
+        if count >= 5:
+            break
+    print('\n'.join(lines))
+# Load NCBI gene mapping
+with open("./metadata/gene_info.json", "r") as f:
+    gene_info = json.load(f)
+
+# Create mapping using Entrez IDs 
+mapping_df = gene_metadata[['ID', 'ENTREZ_GENE_ID']].copy()
+mapping_df['Gene'] = mapping_df['ENTREZ_GENE_ID'].map(lambda x: gene_info.get(str(x), {}).get('Symbol'))
+mapping_df = mapping_df[['ID', 'Gene']].dropna()
+
+# Convert probe-level measurements to gene expression
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Preview the mapped gene expression data
+print("Gene expression data shape after mapping:", gene_data.shape)
+print("\nFirst few rows and columns of mapped gene expression data:")
+print(gene_data.head().iloc[:, :5])
+
+# Save gene expression data
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+# Create mapping between numeric IDs and Entrez IDs
+mapping_df = get_gene_mapping(gene_metadata, 'ID', 'ENTREZ_GENE_ID')
+
+# The extract_human_gene_symbols function in apply_gene_mapping expects strings
+mapping_df['Gene'] = mapping_df['Gene'].astype(str)
+
+# Convert probe-level measurements to gene expression
+gene_data = apply_gene_mapping(genetic_df, mapping_df) 
+
+# Preview the mapped gene expression data
+print("Gene expression data shape after mapping:", gene_data.shape)
+print("\nFirst few rows and columns of mapped gene expression data:")
+print(gene_data.head().iloc[:, :5])
+
+# Save gene expression data
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+# Fix gene mapping to get valid expression data first
+# Inspect gene metadata structure again
+print("Gene metadata preview:")
+print(gene_metadata.head())
+
+# Create mapping dataframe with numeric IDs directly to gene symbols 
+# using NCBI gene info stored in ENTREZ_GENE_ID
+mapping_df = gene_metadata[['ID', 'ENTREZ_GENE_ID']].copy()
+
+# Convert ENTREZ_GENE_ID to gene symbols from NCBI database
+with open("./metadata/gene_info.json", "r") as f:
+    gene_info = json.load(f)
+    
+mapping_df['Gene'] = mapping_df['ENTREZ_GENE_ID'].apply(lambda x: gene_info.get(str(x), {}).get('Symbol', str(x)))
+
+# Convert ENTREZ_GENE_ID to gene symbols from NCBI database
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+print("\nGene data shape after mapping:", gene_data.shape)
+print("First few rows:")
+print(gene_data.head())
+
+# Only proceed if we have valid gene data
+if len(gene_data) > 0:
+    # 1. Normalize gene symbols and save
+    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(clinical_features, gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check for biased features
+    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final validation and metadata saving
+    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=trait_biased,
+        df=linked_data,
+        note="Gene expression profiles from CD4+ T cells of Crohn's disease 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)
+else:
+    print("Gene mapping failed to produce valid expression data")
+    # Save metadata indicating failure
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=True,
+        is_biased=None,
+        df=None,
+        note="Failed to map gene identifiers to valid symbols"
+    )

p3/preprocess/Crohns_Disease/code/GSE169568.py

@@ -0,0 +1,313 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Crohns_Disease"
+cohort = "GSE169568"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Crohns_Disease"
+in_cohort_dir = "../DATA/GEO/Crohns_Disease/GSE169568"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Crohns_Disease/GSE169568.csv"
+out_gene_data_file = "./output/preprocess/3/Crohns_Disease/gene_data/GSE169568.csv"
+out_clinical_data_file = "./output/preprocess/3/Crohns_Disease/clinical_data/GSE169568.csv"
+json_path = "./output/preprocess/3/Crohns_Disease/cohort_info.json"
+
+# Get paths to the SOFT and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values for each feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# The background info mentions BeadChip microarray data and gene expression array (Illumina HT-12), so gene data is available
+is_gene_available = True
+
+# 2.1 Data Availability
+# Identifying rows for each variable
+trait_row = 2  # "diagnosis" field contains disease status
+age_row = 1    # "age" field contains age values 
+gender_row = 0  # "Sex" field contains gender info
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> Optional[int]:
+    """Convert diagnosis to binary (1 for Crohn's disease, 0 for controls)"""
+    if not value or ':' not in value:
+        return None
+    diagnosis = value.split(': ')[1].strip().lower()
+    if "crohn" in diagnosis:
+        return 1
+    elif "control" in diagnosis or "colitis" in diagnosis:
+        return 0
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    """Convert age string to float"""
+    if not value or ':' not in value:
+        return None
+    try:
+        return float(value.split(': ')[1])
+    except:
+        return None
+
+def convert_gender(value: str) -> Optional[int]:
+    """Convert gender to binary (0 for female, 1 for male)"""
+    if not value or ':' not in value:
+        return None
+    gender = value.split(': ')[1].lower()
+    if gender == 'female':
+        return 0
+    elif gender == 'male':
+        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 = 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 selected clinical features:")
+    print(preview_df(selected_clinical))
+    
+    # Save to CSV
+    selected_clinical.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+requires_gene_mapping = True
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file) 
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# Extract probe ID to gene symbol mapping from annotation data
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Symbol')
+
+# Apply gene mapping to convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Print information about the mapping results
+print("Shape of original probe-level data:", genetic_df.shape)
+print("Shape of gene-level data:", gene_data.shape)
+print("\nFirst few gene symbols:")
+print(gene_data.index[:10])
+print("\nPreview of gene expression data:")
+print(gene_data.head().iloc[:, :5])
+# 1. Normalize gene symbols and save
+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
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+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=trait_biased,
+    df=linked_data,
+    note="Study of infliximab treatment response in Crohn's disease patients with gene expression data from blood samples"
+)
+
+# 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)
+# Get paths to the SOFT and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values for each feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Yes - this is BeadChip microarray data on HumanHT-12 v4 array
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+trait_row = 2    # "diagnosis" field contains disease status
+age_row = 1      # "age" field available  
+gender_row = 0   # "Sex" field available
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x: str) -> Optional[int]:
+    if not isinstance(x, str):
+        return None
+    value = x.split(': ')[-1].strip().lower()
+    if "crohn's disease" in value:
+        return 1
+    elif "healthy control" in value or "symptomatic control" in value:
+        return 0
+    return None
+
+def convert_age(x: str) -> Optional[float]:
+    if not isinstance(x, str):
+        return None
+    try:
+        return float(x.split(': ')[-1])
+    except:
+        return None
+
+def convert_gender(x: str) -> Optional[int]:
+    if not isinstance(x, str):
+        return None
+    value = x.split(': ')[-1].lower()
+    if value == 'female':
+        return 0
+    elif value == 'male':
+        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
+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 extracted features
+print("Preview of clinical features:")
+print(preview_df(clinical_features))
+
+# Save clinical features
+clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+# These are Illumina probe IDs (starting with ILMN_) which need to be mapped to gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file) 
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# Extract probe ID to gene symbol mapping from annotation data
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Symbol')
+
+# Apply gene mapping to convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Print information about the mapping results
+print("Shape of original probe-level data:", genetic_df.shape)
+print("Shape of gene-level data:", gene_data.shape)
+print("\nFirst few gene symbols:")
+print(gene_data.index[:10])
+print("\nPreview of gene expression data:")
+print(gene_data.head().iloc[:, :5])
+# 1. Normalize gene symbols and save
+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)
+
+# Re-extract clinical features
+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
+)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+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=trait_biased,
+    df=linked_data,
+    note="Gene expression data from blood samples of treatment-naive Crohn's disease 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/Crohns_Disease/code/GSE186582.py

@@ -0,0 +1,158 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Crohns_Disease"
+cohort = "GSE186582"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Crohns_Disease"
+in_cohort_dir = "../DATA/GEO/Crohns_Disease/GSE186582"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Crohns_Disease/GSE186582.csv"
+out_gene_data_file = "./output/preprocess/3/Crohns_Disease/gene_data/GSE186582.csv"
+out_clinical_data_file = "./output/preprocess/3/Crohns_Disease/clinical_data/GSE186582.csv"
+json_path = "./output/preprocess/3/Crohns_Disease/cohort_info.json"
+
+# Get paths to the SOFT and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values for each feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Based on the series summary mentioning "microarrays" and "gene expression", gene data should be available
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+# Trait (Crohn's disease status) can be inferred from rutgeertrec in row 5
+trait_row = 5
+# Age is not available in sample characteristics
+age_row = None
+# Gender is available in row 1
+gender_row = 1
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert trait value to binary:
+    Ctrl (control) -> 0
+    Rec/Rem (Crohn's disease) -> 1"""
+    if not value or ':' not in value:
+        return None
+    val = value.split(':')[1].strip()
+    if val == 'Ctrl':
+        return 0
+    elif val in ['Rec', 'Rem']:
+        return 1
+    return None
+
+def convert_gender(value: str) -> int:
+    """Convert gender to binary:
+    Female -> 0
+    Male -> 1"""
+    if not value or ':' not in value:
+        return None
+    val = value.split(':')[1].strip()
+    if val == 'Female':
+        return 0
+    elif val == 'Male':
+        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
+# Since trait_row is not None, extract clinical features
+selected_clinical_df = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Preview the extracted features
+print(preview_df(selected_clinical_df))
+
+# Save clinical data
+selected_clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+# Looking at the gene identifiers like '1053_at', '121_at', '1316_at', etc.
+# These are Affymetrix probe IDs which need to be mapped to official gene symbols
+
+requires_gene_mapping = True
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file) 
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# 1. Looking at the gene annotation data, 'ID' contains probe IDs and 'Gene Symbol' contains gene symbols
+# Get mapping from probe IDs to gene symbols
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')
+
+# 2. Apply gene mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Print shape and preview results
+print("Gene expression data shape after mapping:", gene_data.shape)
+print("\nPreview of mapped gene expression data:")
+print(gene_data.head().iloc[:, :5])
+# 1. Normalize gene symbols and save
+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
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+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=trait_biased,
+    df=linked_data,
+    note="Study of infliximab treatment response in Crohn's disease patients with gene expression data from blood samples"
+)
+
+# 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/Crohns_Disease/code/GSE186963.py

@@ -0,0 +1,152 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Crohns_Disease"
+cohort = "GSE186963"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Crohns_Disease"
+in_cohort_dir = "../DATA/GEO/Crohns_Disease/GSE186963"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Crohns_Disease/GSE186963.csv"
+out_gene_data_file = "./output/preprocess/3/Crohns_Disease/gene_data/GSE186963.csv"
+out_clinical_data_file = "./output/preprocess/3/Crohns_Disease/clinical_data/GSE186963.csv"
+json_path = "./output/preprocess/3/Crohns_Disease/cohort_info.json"
+
+# Get paths to the SOFT and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values for each feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene expression data availability
+is_gene_available = True  # Based on series title mentioning "gene expression"
+
+# 2. Variable availability and data type conversion
+# 2.1 Data rows
+trait_row = 3  # "response status" row maps to trait
+age_row = None  # Age not available 
+gender_row = None  # Gender not available
+
+# 2.2 Data type conversion functions
+def convert_trait(x):
+    """Convert response status to binary: 0 for Non-responder, 1 for Responder"""
+    if not x or ':' not in x:
+        return None
+    value = x.split(': ')[1].strip()
+    if value == 'Responder':
+        return 1
+    elif value == 'Non-responder':
+        return 0
+    return None
+
+def convert_age(x):
+    return None  # Not used but defined for completeness
+
+def convert_gender(x):
+    return None  # Not used but defined for completeness
+
+# 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 = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait
+    )
+    
+    # Preview the processed data
+    print("Preview of processed 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_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+# These IDs appear to be transcript cluster IDs from Affymetrix Human Gene 2.1 ST Array
+# They need to be mapped to standard human gene symbols for consistent analysis
+requires_gene_mapping = True
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file) 
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# 1. Identify relevant columns for mapping
+prob_col = 'ID'  
+
+# Extract RefSeq gene names from the annotations
+# Get first valid gene symbol from each annotation
+gene_metadata['Gene'] = gene_metadata['SPOT_ID.1'].apply(lambda x: 
+    next(iter([s for s in extract_human_gene_symbols(x) if s]), None))
+
+# 2. Get mapping dataframe
+mapping_df = get_gene_mapping(gene_metadata, prob_col, 'Gene')
+
+# 3. Convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Preview the result 
+print("Shape of gene expression data:", gene_data.shape)
+print("\nPreview of first few rows and columns:")
+print(gene_data.head().iloc[:, :5])
+# 1. Normalize gene symbols and save
+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, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+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=trait_biased,
+    df=linked_data,
+    note="Study of infliximab treatment response in Crohn's disease patients with gene expression data from blood samples"
+)
+
+# 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/Crohns_Disease/code/GSE193677.py

@@ -0,0 +1,137 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Crohns_Disease"
+cohort = "GSE193677"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Crohns_Disease"
+in_cohort_dir = "../DATA/GEO/Crohns_Disease/GSE193677"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Crohns_Disease/GSE193677.csv"
+out_gene_data_file = "./output/preprocess/3/Crohns_Disease/gene_data/GSE193677.csv"
+out_clinical_data_file = "./output/preprocess/3/Crohns_Disease/clinical_data/GSE193677.csv"
+json_path = "./output/preprocess/3/Crohns_Disease/cohort_info.json"
+
+# Get paths to the SOFT and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values for each feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Based on the background info, this dataset contains RNA-seq data from biopsies
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+# Trait (CD status) in row 4 with disease status
+trait_row = 4
+# Age in row 0
+age_row = 0  
+# Gender in row 1
+gender_row = 1
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    # Extract value after colon and strip whitespace
+    if ':' in str(x):
+        value = x.split(':')[1].strip()
+        # CD is 1, UC and Control are 0
+        if value == 'CD':
+            return 1
+        elif value in ['UC', 'Control']:
+            return 0
+    return None
+
+def convert_age(x):
+    # Extract value after colon and convert to float
+    if ':' in str(x):
+        try:
+            return float(x.split(':')[1].strip())
+        except:
+            return None
+    return None
+
+def convert_gender(x):
+    # Extract value after colon and convert to binary
+    if ':' in str(x):
+        value = x.split(':')[1].strip()
+        if value == 'Female':
+            return 0
+        elif value == 'Male':
+            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:
+    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 processed data
+    preview = preview_df(selected_clinical_df)
+    
+    # Save to CSV
+    selected_clinical_df.to_csv(out_clinical_data_file)
+# Debug: Look for possible table markers
+with gzip.open(matrix_file, 'rt') as file:
+    print("Searching for table markers...")
+    for i, line in enumerate(file):
+        if '!series_matrix_table' in line.lower():
+            print(f"Found potential marker at line {i}:")
+            print(line.strip())
+        if i > 1000:  # Limit search to first 1000 lines
+            break
+print("\n")
+
+# Get all file lines to inspect data format
+with gzip.open(matrix_file, 'rt') as file:
+    lines = file.readlines()[:50]  # Get first 50 lines to see data structure
+    print("File content preview:")
+    for line in lines:
+        print(line.strip())
+
+# Extract gene expression data using revised marker
+genetic_df = pd.read_csv(matrix_file, compression='gzip', 
+                        skiprows=lambda x: x < 80,  # Skip header rows
+                        sep='\t', 
+                        comment='!')
+
+# Set gene IDs as index
+if 'ID_REF' in genetic_df.columns:
+    genetic_df = genetic_df.rename(columns={'ID_REF': 'ID'})
+genetic_df = genetic_df.set_index('ID')
+
+print("\nDataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+
+
+

p3/preprocess/Crohns_Disease/code/GSE207022.py

@@ -0,0 +1,148 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Crohns_Disease"
+cohort = "GSE207022"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Crohns_Disease"
+in_cohort_dir = "../DATA/GEO/Crohns_Disease/GSE207022"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Crohns_Disease/GSE207022.csv"
+out_gene_data_file = "./output/preprocess/3/Crohns_Disease/gene_data/GSE207022.csv"
+out_clinical_data_file = "./output/preprocess/3/Crohns_Disease/clinical_data/GSE207022.csv"
+json_path = "./output/preprocess/3/Crohns_Disease/cohort_info.json"
+
+# Get paths to the SOFT and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values for each feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Based on background information, this is a gene expression profiling study using microarrays
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+# Trait (CD status) can be found in row 3 under "diagnosis"
+trait_row = 3
+# Age and gender are not available in the characteristics
+age_row = None  
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert CD diagnosis to binary: 1 for CD, 0 for control"""
+    if not value or ':' not in value:
+        return None
+    diagnosis = value.split(':')[1].strip().lower()
+    if "crohn's disease" in diagnosis:
+        return 1
+    elif "healthy control" in diagnosis:
+        return 0
+    return None
+
+convert_age = None
+convert_gender = None
+
+# 3. Save initial 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 and save
+if trait_row is not None:
+    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 processed clinical data
+    print("Preview of processed clinical data:")
+    print(preview_df(clinical_df))
+    
+    # Save to CSV
+    clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+# These are Affymetrix probe IDs from the HG-U133_Plus_2.0 array
+# They need to be mapped to human gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file) 
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# 1. Gene identifier column is 'ID', gene symbol column is 'Gene Symbol'
+# 2. Extract mapping between probe IDs and gene symbols
+mapping_df = get_gene_mapping(gene_metadata, 'ID', 'Gene Symbol')
+
+# 3. Apply the mapping to convert probe data to gene expression data
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Preview the processed gene data shape and first few rows
+print("Gene expression data shape:", gene_data.shape)
+print("\nPreview of gene expression data:")
+print(gene_data.head().iloc[:, :5])
+# 1. Normalize gene symbols and save
+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(clinical_df, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+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=trait_biased,
+    df=linked_data,
+    note="Study of Crohn's disease vs healthy controls with gene expression data from rectum biopsies"
+)
+
+# 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/Crohns_Disease/code/GSE259353.py

@@ -0,0 +1,142 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Crohns_Disease"
+cohort = "GSE259353"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Crohns_Disease"
+in_cohort_dir = "../DATA/GEO/Crohns_Disease/GSE259353"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Crohns_Disease/GSE259353.csv"
+out_gene_data_file = "./output/preprocess/3/Crohns_Disease/gene_data/GSE259353.csv"
+out_clinical_data_file = "./output/preprocess/3/Crohns_Disease/clinical_data/GSE259353.csv"
+json_path = "./output/preprocess/3/Crohns_Disease/cohort_info.json"
+
+# Get paths to the SOFT and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values for each feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Yes - This is Nanostring gene expression data for fibrosis-related genes
+is_gene_available = True
+
+# 2.1 Data Availability
+# Trait: The "group" field indicates disease subtype/penetrance (row 0)
+trait_row = 0
+# Gender data available in row 1
+gender_row = 1 
+# Age data available in row 2
+age_row = 2
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert penetrating vs stricturing to binary"""
+    if pd.isna(value) or not isinstance(value, str):
+        return None
+    value = value.split(": ")[-1].strip().upper()
+    # B2 = Penetrating, B3 variants = Stricturing
+    if value == "B2":
+        return 1  # Penetrating
+    elif value in ["B3O", "B3S"]: 
+        return 0  # Stricturing
+    return None
+
+def convert_age(value: str) -> float:
+    """Convert age to continuous numeric value"""
+    if pd.isna(value) or not isinstance(value, str):
+        return None
+    try:
+        return float(value.split(": ")[-1])
+    except:
+        return None
+
+def convert_gender(value: str) -> int:
+    """Convert gender to binary (0=Female, 1=Male)"""
+    if pd.isna(value) or not isinstance(value, str):
+        return None
+    value = value.split(": ")[-1].strip().upper()
+    if value == "FEMALE":
+        return 0
+    elif value == "MALE":
+        return 1
+    return None
+
+# 3. Save initial 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
+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 and save clinical data
+print("Clinical data preview:")
+print(preview_df(clinical_df))
+clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+requires_gene_mapping = False  # The identifiers are already official human gene symbols
+# 1. Normalize gene symbols and save
+genetic_df = normalize_gene_symbols_in_index(genetic_df) 
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+genetic_df.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_df, genetic_df)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+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=trait_biased,
+    df=linked_data,
+    note="Study of penetrating vs stricturing Crohn's disease with gene expression data from ileal surgical specimens"
+)
+
+# 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/Crohns_Disease/code/GSE66407.py

@@ -0,0 +1,152 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Crohns_Disease"
+cohort = "GSE66407"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Crohns_Disease"
+in_cohort_dir = "../DATA/GEO/Crohns_Disease/GSE66407"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Crohns_Disease/GSE66407.csv"
+out_gene_data_file = "./output/preprocess/3/Crohns_Disease/gene_data/GSE66407.csv"
+out_clinical_data_file = "./output/preprocess/3/Crohns_Disease/clinical_data/GSE66407.csv"
+json_path = "./output/preprocess/3/Crohns_Disease/cohort_info.json"
+
+# Get paths to the SOFT and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values for each feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# The background info mentions "transcriptome analysis", which indicates gene expression data
+is_gene_available = True
+
+# 2.1 Data Availability and 2.2 Data Type Conversion
+# Trait (diagnosis) is in row 3, binary (CD vs Control)
+trait_row = 3
+def convert_trait(x):
+    if pd.isna(x):
+        return None
+    val = x.split(': ')[1].strip()
+    if val == 'CD':
+        return 1
+    elif val == 'Control':
+        return 0
+    return None
+
+# Age is in row 2, continuous
+age_row = 2
+def convert_age(x):
+    if pd.isna(x):
+        return None
+    try:
+        return int(x.split(': ')[1])
+    except:
+        return None
+
+# Gender data not available in sample characteristics
+gender_row = None
+def convert_gender(x):
+    # Function defined but not used since gender data unavailable
+    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
+# Since trait_row is not None, extract clinical features
+selected_clinical_df = geo_select_clinical_features(clinical_data, 
+                                                  trait, 
+                                                  trait_row,
+                                                  convert_trait,
+                                                  age_row,
+                                                  convert_age,
+                                                  gender_row,
+                                                  convert_gender)
+
+# Preview the extracted clinical features
+preview_df(selected_clinical_df)
+
+# Save clinical data
+selected_clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+# These identifiers are ENSEMBL gene IDs with '_at' suffix
+# They need to be mapped to human gene symbols for standardization
+requires_gene_mapping = True
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file) 
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# Extract gene symbols from Description column using helper function
+gene_metadata['Gene'] = gene_metadata['Description'].apply(extract_human_gene_symbols).str[0]
+
+# Get mapping between probe IDs and gene symbols 
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene')
+
+# Convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Print shape and preview of resulting gene expression data
+print("Gene expression data shape:", gene_data.shape)
+print("\nPreview of gene expression data:")
+print(gene_data.head().iloc[:, :5])
+# 1. Normalize gene symbols and save
+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
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+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=trait_biased,
+    df=linked_data,
+    note="Gene expression data from gut biopsies of CD patients, UC patients 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/Crohns_Disease/code/GSE83448.py

@@ -0,0 +1,152 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Crohns_Disease"
+cohort = "GSE83448"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Crohns_Disease"
+in_cohort_dir = "../DATA/GEO/Crohns_Disease/GSE83448"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Crohns_Disease/GSE83448.csv"
+out_gene_data_file = "./output/preprocess/3/Crohns_Disease/gene_data/GSE83448.csv"
+out_clinical_data_file = "./output/preprocess/3/Crohns_Disease/clinical_data/GSE83448.csv"
+json_path = "./output/preprocess/3/Crohns_Disease/cohort_info.json"
+
+# Get paths to the SOFT and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values for each feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene expression data availability - Yes, based on "GE Healthcare/Amersham Biosciences CodeLink Human Whole Genome Bioarray"
+is_gene_available = True
+
+# 2.1 Variable availability
+trait_row = 1  # inflammation status indicates CD vs control
+age_row = None  # Age not available
+gender_row = None # Gender not available
+
+# 2.2 Data type conversion functions
+def convert_trait(value: str) -> Optional[int]:
+    """Convert inflammation status to binary CD indicator (0=Control, 1=CD)"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip().lower()
+    if 'control' in value:
+        return 0
+    elif 'inflamed' in value or 'non-inflamed' in value:
+        return 1
+    return None
+
+convert_age = None
+convert_gender = None
+
+# 3. Save initial cohort 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
+)
+
+# 4. Extract clinical features
+if trait_row is not None:
+    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
+    print("Clinical data preview:")
+    print(preview_df(clinical_df))
+    
+    # Save to CSV
+    clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+# Based on the gene identifiers like GE469557, GE469567, etc., these look like custom probe IDs.
+# These are not human gene symbols and will need to be mapped to standard gene symbols.
+requires_gene_mapping = True
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file)
+
+# Extract potential gene symbols from description field where available
+gene_metadata['Gene'] = gene_metadata['DESCRIPTION'].apply(extract_human_gene_symbols)
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# Get gene annotation with symbols extracted from description
+gene_metadata = get_gene_annotation(soft_file)
+gene_metadata['Gene'] = gene_metadata['DESCRIPTION'].apply(extract_human_gene_symbols)
+
+# Create mapping DataFrame and perform mapping
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene') 
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Print shape and preview data
+print("Gene expression data shape after mapping:", gene_data.shape)
+print("\nPreview of gene expression data:")
+print(gene_data.head().iloc[:, :5])
+
+# Save gene expression data
+gene_data.to_csv(out_gene_data_file)
+# 1. Since gene mapping failed (empty DataFrame), skip gene symbol normalization
+# and use the original gene expression data with probe IDs
+gene_data = genetic_df
+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(clinical_df, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+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=trait_biased,
+    df=linked_data,
+    note="Contains gene expression data from intestinal biopsies of CD patients and controls. Using probe IDs as gene identifiers due to unreliable mapping."
+)
+
+# 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/Crohns_Disease/code/TCGA.py

@@ -0,0 +1,25 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Crohns_Disease"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Crohns_Disease/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Crohns_Disease/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Crohns_Disease/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Crohns_Disease/cohort_info.json"
+
+# Review subdirectories and check if any matches Crohn's Disease phenotype
+# TCGA is a cancer database and does not have IBD cohorts
+# Record this in metadata and exit 
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=False,
+    is_trait_available=False
+)