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@@ -1782,3 +1782,25 @@ p3/preprocess/Hypertension/gene_data/GSE128381.csv filter=lfs diff=lfs merge=lfs
 p3/preprocess/Hypertension/gene_data/GSE77627.csv filter=lfs diff=lfs merge=lfs -text
 p3/preprocess/Hypertension/gene_data/GSE117261.csv filter=lfs diff=lfs merge=lfs -text
 p3/preprocess/Hypertension/gene_data/GSE161533.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Hypertension/gene_data/GSE256539.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Lower_Grade_Glioma/gene_data/GSE35158.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Pancreatic_Cancer/GSE130563.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Pancreatic_Cancer/GSE131027.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Lower_Grade_Glioma/gene_data/GSE107850.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Pancreatic_Cancer/gene_data/GSE130563.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Pancreatic_Cancer/gene_data/GSE131027.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Sjögrens_Syndrome/GSE40611.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Sjögrens_Syndrome/GSE135809.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Pancreatic_Cancer/gene_data/GSE183795.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Sjögrens_Syndrome/GSE93683.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Sjögrens_Syndrome/GSE84844.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Sjögrens_Syndrome/GSE51092.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Pancreatic_Cancer/GSE183795.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Sjögrens_Syndrome/gene_data/GSE135809.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Sjögrens_Syndrome/gene_data/GSE40611.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Pancreatic_Cancer/gene_data/TCGA.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Sjögrens_Syndrome/gene_data/GSE84844.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Sjögrens_Syndrome/GSE66795.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Sjögrens_Syndrome/gene_data/GSE93683.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Sjögrens_Syndrome/gene_data/GSE51092.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Sjögrens_Syndrome/gene_data/GSE66795.csv filter=lfs diff=lfs merge=lfs -text

p3/preprocess/Hypertension/gene_data/GSE256539.csv

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p3/preprocess/Hypertrophic_Cardiomyopathy/clinical_data/GSE36961.csv

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p3/preprocess/Hypertrophic_Cardiomyopathy/code/GSE36961.py

@@ -0,0 +1,144 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hypertrophic_Cardiomyopathy"
+cohort = "GSE36961"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hypertrophic_Cardiomyopathy"
+in_cohort_dir = "../DATA/GEO/Hypertrophic_Cardiomyopathy/GSE36961"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Hypertrophic_Cardiomyopathy/GSE36961.csv"
+out_gene_data_file = "./output/preprocess/3/Hypertrophic_Cardiomyopathy/gene_data/GSE36961.csv"
+out_clinical_data_file = "./output/preprocess/3/Hypertrophic_Cardiomyopathy/clinical_data/GSE36961.csv"
+json_path = "./output/preprocess/3/Hypertrophic_Cardiomyopathy/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Get unique values for each clinical feature 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background information
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Series title and summary indicate this is transcriptome profiling data
+is_gene_available = True
+
+# 2.1 Data Availability
+# Trait is in row 3, gender in row 0, age in row 1
+trait_row = 3  # disease state / sample type shows HCM vs control
+gender_row = 0  # Sex field
+age_row = 1   # age (yrs) field
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert trait value to binary (1=case, 0=control)"""
+    if pd.isna(value):
+        return None
+    value = value.split(": ")[-1].lower()
+    if "hypertrophic cardiomyopathy" in value or "hcm" in value or "case" in value:
+        return 1
+    elif "control" in value:
+        return 0
+    return None
+
+def convert_age(value: str) -> float:
+    """Convert age value to continuous numeric"""
+    if pd.isna(value):
+        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):
+        return None
+    value = value.split(": ")[-1].lower()
+    if value == "female":
+        return 0
+    elif value == "male":
+        return 1
+    return None
+
+# 3. Save Metadata 
+# trait_row is not None, so trait data is available
+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. 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 clinical data
+preview_result = preview_df(selected_clinical)
+
+# Save clinical data
+selected_clinical.to_csv(out_clinical_data_file)
+# Extract gene expression data from the matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs
+print("First 20 row IDs:")
+print(genetic_data.index[:20].tolist())
+requires_gene_mapping = False
+# 1. Normalize gene symbols
+genetic_data = normalize_gene_symbols_in_index(genetic_data)
+genetic_data.to_csv(out_gene_data_file)
+
+# Get clinical features 
+clinical_features = geo_select_clinical_features(
+    clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, genetic_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Judge whether features are biased and remove biased demographic features
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+note = "Dataset contains gene expression data comparing cardiac tissue from patients with hypertrophic cardiomyopathy (HCM) versus control donor cardiac tissues."
+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=note
+)
+
+# 6. Save the linked data only if it's usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Hypertrophic_Cardiomyopathy/code/TCGA.py

@@ -0,0 +1,28 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hypertrophic_Cardiomyopathy"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Hypertrophic_Cardiomyopathy/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Hypertrophic_Cardiomyopathy/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Hypertrophic_Cardiomyopathy/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Hypertrophic_Cardiomyopathy/cohort_info.json"
+
+# Get subdirectories from TCGA root directory 
+tcga_subdirs = os.listdir(tcga_root_dir)
+tcga_subdirs = [d for d in tcga_subdirs if not d.startswith('.')]
+
+# No suitable cohort exists for HDL deficiency in TCGA cancer datasets
+# Record this and end processing
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort="TCGA",
+    info_path=json_path, 
+    is_gene_available=True,
+    is_trait_available=False
+)

p3/preprocess/Hypertrophic_Cardiomyopathy/cohort_info.json

@@ -0,0 +1 @@
+{"GSE36961": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": true, "sample_size": 142, "note": "Dataset contains gene expression data comparing cardiac tissue from patients with hypertrophic cardiomyopathy (HCM) versus control donor cardiac tissues."}, "TCGA": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}

p3/preprocess/Hypothyroidism/clinical_data/GSE151158.csv

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p3/preprocess/Hypothyroidism/clinical_data/GSE224330.csv

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p3/preprocess/Hypothyroidism/clinical_data/GSE32445.csv

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+Age,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0,9.0
+Gender,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

p3/preprocess/Hypothyroidism/clinical_data/GSE75678.csv

@@ -0,0 +1,4 @@
+,GSM1963528,GSM1963529,GSM1963530,GSM1963531,GSM1963532,GSM1963533,GSM1963534,GSM1963535,GSM1963536,GSM1963537,GSM1963538,GSM1963539,GSM1963540,GSM1963541,GSM1963542,GSM1963543,GSM1963544,GSM1963545,GSM1963546,GSM1963547,GSM1963548,GSM1963549,GSM1963550,GSM1963551,GSM1963552,GSM1963553,GSM1963554,GSM1963555,GSM1963556,GSM1963557,GSM1963558,GSM1963559,GSM1963560,GSM1963561,GSM1963562,GSM1963563,GSM1963564,GSM1963565,GSM1963566,GSM1963567,GSM1963568,GSM1963569,GSM1963570,GSM1963571,GSM1963572,GSM1963573,GSM1963574,GSM1963575,GSM1963576,GSM1963577,GSM1963578,GSM1963579,GSM1963580,GSM1963581
+Hypothyroidism,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+Age,45.0,41.0,59.0,57.0,42.0,49.0,59.0,54.0,54.0,31.0,70.0,44.0,50.0,42.0,56.0,51.0,58.0,55.0,71.0,42.0,41.0,40.0,57.0,62.0,87.0,36.0,50.0,45.0,43.0,42.0,43.0,44.0,43.0,48.0,45.0,51.0,56.0,57.0,41.0,48.0,66.0,53.0,36.0,51.0,57.0,45.0,55.0,35.0,44.0,68.0,46.0,58.0,45.0,54.0
+Gender,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0

p3/preprocess/Hypothyroidism/clinical_data/GSE75685.csv

@@ -0,0 +1,4 @@
+,GSM1963127,GSM1963128,GSM1963129,GSM1963130,GSM1963131,GSM1963132,GSM1963133,GSM1963134,GSM1963135,GSM1963136,GSM1963137,GSM1963138,GSM1963139,GSM1963140,GSM1963141,GSM1963142,GSM1963143,GSM1963144,GSM1963145,GSM1963146,GSM1963147,GSM1963148,GSM1963149,GSM1963150,GSM1963151,GSM1963152,GSM1963153,GSM1963154,GSM1963155,GSM1963156,GSM1963157,GSM1963158,GSM1963159,GSM1963160,GSM1963161,GSM1963162,GSM1963163,GSM1963164,GSM1963165,GSM1963166,GSM1963167,GSM1963168,GSM1963169,GSM1963170,GSM1963171,GSM1963172,GSM1963173,GSM1963174,GSM1963175,GSM1963176,GSM1963177,GSM1963178,GSM1963179,GSM1963180
+Hypothyroidism,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
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+Gender,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0

p3/preprocess/Hypothyroidism/clinical_data/TCGA.csv

@@ -0,0 +1,581 @@
+sampleID,Hypothyroidism,Age,Gender
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p3/preprocess/Hypothyroidism/code/GSE151158.py

@@ -0,0 +1,146 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hypothyroidism"
+cohort = "GSE151158"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hypothyroidism"
+in_cohort_dir = "../DATA/GEO/Hypothyroidism/GSE151158"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Hypothyroidism/GSE151158.csv"
+out_gene_data_file = "./output/preprocess/3/Hypothyroidism/gene_data/GSE151158.csv"
+out_clinical_data_file = "./output/preprocess/3/Hypothyroidism/clinical_data/GSE151158.csv"
+json_path = "./output/preprocess/3/Hypothyroidism/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Get unique values for each clinical feature 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background information
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Background shows this is gene expression study of 594 genes
+
+# 2.1 Data Availability
+trait_row = 12  # hypothyroidism data found in row 12
+age_row = 1     # age data found in row 1
+gender_row = 2  # gender data found in row 2 as "Sex"
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    if pd.isna(x):
+        return None
+    value = x.split(": ")[1] if ": " in x else x
+    if value.upper() == 'Y':
+        return 1
+    elif value.upper() == 'N': 
+        return 0
+    return None
+
+def convert_age(x):
+    if pd.isna(x):
+        return None
+    try:
+        age = int(x.split(": ")[1])
+        return age
+    except:
+        return None
+
+def convert_gender(x):
+    if pd.isna(x):
+        return None
+    value = x.split(": ")[1] if ": " in x else x
+    if value.upper() == 'F':
+        return 0
+    elif value.upper() == '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_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(clinical_features_df)
+    print(preview)
+    
+    # Save to CSV
+    clinical_features_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from the matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs
+print("First 20 row IDs:")
+print(genetic_data.index[:20].tolist())
+# These IDs are standard HUGO gene symbols - e.g. ABCB1, ABCF1, ABL1 are well-known gene symbols
+# No mapping needed as they are already in the correct format
+requires_gene_mapping = False
+# 1. Normalize gene symbols
+genetic_data = normalize_gene_symbols_in_index(genetic_data)
+genetic_data.to_csv(out_gene_data_file)
+
+# Get clinical features
+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
+)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, genetic_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Judge whether features are biased and remove biased demographic features
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+note = "Dataset contains gene expression data studying hypothyroidism in the context of NAFLD progression, with clinical annotations."
+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=note
+)
+
+# 6. Save the linked data only if it's usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Hypothyroidism/code/GSE224330.py

@@ -0,0 +1,180 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hypothyroidism"
+cohort = "GSE224330"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hypothyroidism"
+in_cohort_dir = "../DATA/GEO/Hypothyroidism/GSE224330"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Hypothyroidism/GSE224330.csv"
+out_gene_data_file = "./output/preprocess/3/Hypothyroidism/gene_data/GSE224330.csv"
+out_clinical_data_file = "./output/preprocess/3/Hypothyroidism/clinical_data/GSE224330.csv"
+json_path = "./output/preprocess/3/Hypothyroidism/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Get unique values for each clinical feature 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background information
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+print(json.dumps(unique_values_dict, indent=2))
+# Gene expression data availability
+# Yes, this dataset contains gene expression data as indicated by series title and summary
+is_gene_available = True
+
+# Data availability and type conversion
+# Trait data (Hypothyroidism) can be extracted from comorbidity field
+trait_row = 3
+def convert_trait(value):
+    if pd.isna(value):
+        return None
+    value = value.split(': ')[1].lower()
+    if 'hypothyroidism' in value:
+        return 1
+    elif 'none' in value:
+        return 0
+    return None
+
+# Age data is in row 1
+age_row = 1
+def convert_age(value):
+    if pd.isna(value):
+        return None
+    try:
+        return int(value.split(': ')[1].rstrip('y'))
+    except:
+        return None
+
+# Gender data is in row 2  
+gender_row = 2
+def convert_gender(value):
+    if pd.isna(value):
+        return None
+    value = value.split(': ')[1].lower()
+    if 'female' in value:
+        return 0
+    elif 'male' in value:
+        return 1
+    return None
+
+# 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
+)
+
+# Extract clinical features since trait_row is available
+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
+preview_df(clinical_features)
+
+# Save clinical data
+clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from the matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs
+print("First 20 row IDs:")
+print(genetic_data.index[:20].tolist())
+# These identifiers appear to be Agilent probe IDs, not human gene symbols
+# The format A_19_P00xxxxxx is characteristic of Agilent microarray probes
+# We will need to map these to proper gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data from SOFT file
+gene_metadata = get_gene_annotation(soft_file_path)
+
+# Display information about the annotation data
+print("Column names:")
+print(gene_metadata.columns.tolist())
+
+# Look at general data statistics
+print("\nData shape:", gene_metadata.shape)
+
+# Display non-NaN value counts for key gene identifier columns
+print("\nNumber of non-NaN values in key columns:")
+for col in ['GENE_SYMBOL', 'GENE_NAME']:
+    print(f"{col}: {gene_metadata[col].notna().sum()}")
+
+# Preview rows with actual gene information
+print("\nPreview of rows with gene information:")
+gene_rows = gene_metadata[gene_metadata['GENE_SYMBOL'].notna()].head()
+print(json.dumps(preview_df(gene_rows), indent=2))
+# Get mapping between probes and gene symbols
+mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# Convert probe-level data to gene-level data using the mapping
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Save gene expression data
+gene_data.to_csv(out_gene_data_file)
+
+# Print statistics about the mapping
+print(f"Original probe number: {len(genetic_data)}")
+print(f"Number of probes with gene mapping: {len(mapping_data)}")
+print(f"Final number of genes: {len(gene_data)}")
+# 1. Normalize gene symbols
+genetic_data = normalize_gene_symbols_in_index(gene_data)
+genetic_data.to_csv(out_gene_data_file)
+
+# Get clinical features
+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
+)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, genetic_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Judge whether features are biased and remove biased demographic features
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+note = "Dataset contains gene expression data from breast cancer patients, with clinical annotations including hypothyroidism status."
+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=note
+)
+
+# 6. Save the linked data only if it's usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Hypothyroidism/code/GSE32445.py

@@ -0,0 +1,143 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hypothyroidism"
+cohort = "GSE32445"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hypothyroidism"
+in_cohort_dir = "../DATA/GEO/Hypothyroidism/GSE32445"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Hypothyroidism/GSE32445.csv"
+out_gene_data_file = "./output/preprocess/3/Hypothyroidism/gene_data/GSE32445.csv"
+out_clinical_data_file = "./output/preprocess/3/Hypothyroidism/clinical_data/GSE32445.csv"
+json_path = "./output/preprocess/3/Hypothyroidism/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Get unique values for each clinical feature 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background information
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability 
+# The series title and description suggest this is a study involving gene regulation,
+# so it's likely to have gene expression data
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Data Availability
+# Trait: Not directly available in characteristics - cannot be inferred from strain alone
+trait_row = None
+
+# Age: Available in row 2
+age_row = 2
+
+# Gender: Available in row 1
+gender_row = 1
+
+# 2.2 Data Type Conversion
+# Trait converter not needed since trait data not available
+def convert_trait(x):
+    return None
+
+# Age converter - continuous
+def convert_age(x):
+    try:
+        # Extract value after colon and remove 'months'/'years'
+        value = x.split(':')[1].strip()
+        value = value.lower().replace('months', '').replace('years', '').strip()
+        return float(value)
+    except:
+        return None
+
+# Gender converter - binary (female=0, male=1)
+def convert_gender(x):
+    try:
+        value = x.split(':')[1].strip().lower()
+        if 'female' in value:
+            return 0
+        elif 'male' in value:
+            return 1
+        return None
+    except:
+        return None
+
+# 3. Save Metadata
+# Initial filtering - trait data not available so dataset will be filtered out
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=False
+)
+
+# 4. Clinical Feature Extraction
+# Skip since trait_row is None
+# Extract gene expression data from the matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs
+print("First 20 row IDs:")
+print(genetic_data.index[:20].tolist())
+# The row IDs are Illumina probe IDs (starting with 'ILMN_') rather than human gene symbols
+# These need to be mapped to gene symbols for analysis
+requires_gene_mapping = True
+# Extract gene annotation data from SOFT file
+gene_metadata = get_gene_annotation(soft_file_path)
+
+# Display information about the annotation data
+print("Column names:")
+print(gene_metadata.columns.tolist())
+
+# Look at general data statistics
+print("\nData shape:", gene_metadata.shape)
+
+# Display non-NaN value counts for key gene identifier columns
+print("\nNumber of non-NaN values in key columns:")
+for col in ['Gene Symbol', 'Gene Title']:
+    print(f"{col}: {gene_metadata[col].notna().sum()}")
+
+# Preview rows with actual gene information
+print("\nPreview of rows with gene information:")
+gene_rows = gene_metadata[gene_metadata['Gene Symbol'].notna()].head()
+print(json.dumps(preview_df(gene_rows), indent=2))
+# Extract gene mapping information from gene annotation data
+# Split multiple gene symbols and expand them
+mapping_data = gene_metadata.loc[:, ['ID', 'Gene Symbol']]
+mapping_data = mapping_data.dropna()
+# Rename column to match expected name in apply_gene_mapping function
+mapping_data = mapping_data.rename(columns={'Gene Symbol': 'Gene'})
+
+# Apply the mapping to get gene-level expression data  
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Preview the results
+print("\nNumber of genes after mapping:", len(gene_data))
+print("\nFirst few gene symbols:", gene_data.index[:10].tolist())
+# Create an empty DataFrame to represent unusable data
+empty_df = pd.DataFrame()
+
+# Record that this dataset is unusable 
+note = "Dataset lacks trait information and gene mapping failed to produce any valid gene expression data."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort, 
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=True,  # Set to True to indicate the data is unusable
+    df=empty_df,     # Provide empty DataFrame instead of None
+    note=note
+)

p3/preprocess/Hypothyroidism/code/GSE75678.py

@@ -0,0 +1,173 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hypothyroidism"
+cohort = "GSE75678"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hypothyroidism"
+in_cohort_dir = "../DATA/GEO/Hypothyroidism/GSE75678"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Hypothyroidism/GSE75678.csv"
+out_gene_data_file = "./output/preprocess/3/Hypothyroidism/gene_data/GSE75678.csv"
+out_clinical_data_file = "./output/preprocess/3/Hypothyroidism/clinical_data/GSE75678.csv"
+json_path = "./output/preprocess/3/Hypothyroidism/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Get unique values for each clinical feature 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background information
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+is_gene_available = True   # Based on series title and summary indicating gene expression data
+
+# 2. Variable Availability and Data Type Conversion
+# Hypothyroidism data is in row 21 (personal pathological history)
+trait_row = 21
+age_row = 19  # Age at diagnosis
+gender_row = 1  # Gender data is in row 1
+
+def convert_trait(x):
+    if pd.isna(x):
+        return None
+    val = x.split(': ')[1] if ': ' in x else x
+    if 'Hypothyroidism' in val:
+        return 1
+    return 0
+
+def convert_age(x):
+    if pd.isna(x):
+        return None
+    val = x.split(': ')[1] if ': ' in x else x
+    try:
+        return float(val)
+    except:
+        return None
+
+def convert_gender(x):
+    if pd.isna(x):
+        return None
+    val = x.split(': ')[1] if ': ' in x else x
+    if val.lower() == 'female':
+        return 0
+    elif val.lower() == 'male':
+        return 1
+    return None
+
+# 3. Save Metadata
+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
+)
+
+# 4. Clinical Feature Extraction
+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 and save clinical data
+print(preview_df(selected_clinical))
+selected_clinical.to_csv(out_clinical_data_file)
+# Extract gene expression data from the matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs
+print("First 20 row IDs:")
+print(genetic_data.index[:20].tolist())
+# Looking at the row IDs, they appear to be simple numeric indices rather than gene symbols
+# This indicates we need to map these identifiers to actual gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data from SOFT file
+gene_metadata = get_gene_annotation(soft_file_path)
+
+# Display information about the annotation data
+print("Column names:")
+print(gene_metadata.columns.tolist())
+
+# Look at general data statistics
+print("\nData shape:", gene_metadata.shape)
+
+# Display non-NaN value counts for key gene identifier columns
+print("\nNumber of non-NaN values in key columns:")
+for col in ['GENE', 'GENE_SYMBOL', 'GENE_NAME']:
+    print(f"{col}: {gene_metadata[col].notna().sum()}")
+
+# Preview rows with actual gene information
+print("\nPreview of rows with gene information:")
+gene_rows = gene_metadata[gene_metadata['GENE_SYMBOL'].notna()].head()
+print(json.dumps(preview_df(gene_rows), indent=2))
+# Extract mapping between numeric IDs and gene symbols from annotation data
+mapping_df = get_gene_mapping(gene_metadata, 'ID', 'GENE_SYMBOL')
+
+# Convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Preview the gene data shape 
+print("Gene expression data shape:", gene_data.shape)
+
+# Preview first few gene symbols and samples
+print("\nFirst few gene symbols:", gene_data.index[:5].tolist())
+print("\nFirst few samples:", gene_data.columns[:5].tolist())
+# 1. Normalize gene symbols
+genetic_data = normalize_gene_symbols_in_index(gene_data)
+genetic_data.to_csv(out_gene_data_file)
+
+# Get clinical features
+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
+)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, genetic_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Judge whether features are biased and remove biased demographic features
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+note = "Dataset contains gene expression data from breast cancer patients, with clinical annotations including hypothyroidism status."
+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=note
+)
+
+# 6. Save the linked data only if it's usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Hypothyroidism/code/GSE75685.py

@@ -0,0 +1,160 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hypothyroidism"
+cohort = "GSE75685"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hypothyroidism"
+in_cohort_dir = "../DATA/GEO/Hypothyroidism/GSE75685"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Hypothyroidism/GSE75685.csv"
+out_gene_data_file = "./output/preprocess/3/Hypothyroidism/gene_data/GSE75685.csv"
+out_clinical_data_file = "./output/preprocess/3/Hypothyroidism/clinical_data/GSE75685.csv"
+json_path = "./output/preprocess/3/Hypothyroidism/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Get unique values for each clinical feature 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background information
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene expression data availability check
+# Study description suggests this is a breast cancer study with tumor samples
+# There is RNA concentration and quality data (RQI Experion)
+is_gene_available = True
+
+# 2.1 Data row identification
+trait_row = 21  # personal pathological history has 'Hypothyroidism' data
+age_row = 19  # 'age at diagnosis'
+gender_row = 1  # gender information
+
+# 2.2 Data type conversion functions
+def convert_trait(value):
+    if pd.isna(value):
+        return None
+    value = value.split(': ')[-1]
+    return 1 if value == 'Hypothyroidism' else 0
+
+def convert_age(value):
+    if pd.isna(value):
+        return None
+    try:
+        age = int(value.split(': ')[-1])
+        return age
+    except:
+        return None
+
+def convert_gender(value):
+    if pd.isna(value):
+        return None
+    value = value.split(': ')[-1].lower()
+    if 'female' in value:
+        return 0
+    elif 'male' in value:
+        return 1
+    return None
+
+# 3. Save metadata about dataset usability
+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 and save clinical features
+print("Preview of extracted clinical features:")
+print(preview_df(clinical_features))
+clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from the matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs
+print("First 20 row IDs:")
+print(genetic_data.index[:20].tolist())
+# The row IDs are numerical indices, not gene symbols or other identifiers
+# Therefore, gene mapping will be required to convert these to meaningful gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data from SOFT file
+gene_metadata = get_gene_annotation(soft_file_path)
+
+# Display information about the annotation data
+print("Column names:")
+print(gene_metadata.columns.tolist())
+print("\nPreview of first few rows:")
+print(json.dumps(preview_df(gene_metadata), indent=2))
+# Extract gene ID and gene symbol columns from annotation data
+mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# Convert probe-level measurements to gene-level expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Preview result
+print("\nPreview of first few genes and their expression values:")
+print(preview_df(gene_data))
+# 1. Normalize gene symbols
+genetic_data = normalize_gene_symbols_in_index(gene_data)
+genetic_data.to_csv(out_gene_data_file)
+
+# Get clinical features
+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
+)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, genetic_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Judge whether features are biased and remove biased demographic features
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+note = "Dataset contains gene expression data from breast cancer patients, with clinical annotations including hypothyroidism status."
+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=note
+)
+
+# 6. Save the linked data only if it's usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Hypothyroidism/code/TCGA.py

@@ -0,0 +1,91 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hypothyroidism"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Hypothyroidism/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Hypothyroidism/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Hypothyroidism/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Hypothyroidism/cohort_info.json"
+
+# Get subdirectories from TCGA root directory
+tcga_subdirs = os.listdir(tcga_root_dir)
+tcga_subdirs = [d for d in tcga_subdirs if not d.startswith('.')]
+
+# Select thyroid cancer cohort as most relevant for hypothyroidism
+selected_dir = "TCGA_Thyroid_Cancer_(THCA)"
+cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+
+# Get clinical and genetic data file paths
+clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+# Load the data files
+clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t') 
+genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t')
+
+# Print clinical data columns for inspection
+print("Clinical data columns:")
+print(clinical_df.columns.tolist())
+# Part 1: Define candidate columns
+candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
+candidate_gender_cols = ['gender']
+
+# Part 2: Preview existing clinical data
+# Print age columns preview
+age_preview = {}
+for col in candidate_age_cols:
+    age_preview[col] = clinical_df[col].head().tolist()
+print("Age columns preview:", age_preview)
+
+# Print gender columns preview
+gender_preview = {}
+for col in candidate_gender_cols:
+    gender_preview[col] = clinical_df[col].head().tolist()
+print("Gender columns preview:", gender_preview)
+# Selecting age column
+age_col = "age_at_initial_pathologic_diagnosis"  # Contains direct age values, easier to interpret than days_to_birth
+
+# Selecting gender column
+gender_col = "gender"  # Contains standard gender values
+
+# Print chosen columns
+print(f"Selected age column: {age_col}")
+print(f"Selected gender column: {gender_col}")
+# Extract and standardize clinical features
+selected_clinical_df = tcga_select_clinical_features(clinical_df, trait, age_col, gender_col)
+selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Normalize gene symbols and save
+normalized_genetic_df = normalize_gene_symbols_in_index(genetic_df)
+normalized_genetic_df.to_csv(out_gene_data_file)
+
+# Link clinical and genetic data
+linked_data = pd.concat([selected_clinical_df, normalized_genetic_df.T], axis=1)
+
+# Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# Judge whether features are biased and remove biased demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# Final validation and save cohort info
+note = "Used thyroid cancer (THCA) data as thyroid disorders are closely related to hypothyroidism"
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note=note
+)
+
+# Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Hypothyroidism/cohort_info.json

@@ -0,0 +1 @@
+{"GSE75685": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": true, "has_gender": false, "sample_size": 54, "note": "Dataset contains gene expression data from breast cancer patients, with clinical annotations including hypothyroidism status."}, "GSE75678": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": true, "has_gender": false, "sample_size": 54, "note": "Dataset contains gene expression data from breast cancer patients, with clinical annotations including hypothyroidism status."}, "GSE32445": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "Dataset lacks trait information and gene mapping failed to produce any valid gene expression data."}, "GSE224330": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": true, "has_gender": true, "sample_size": 15, "note": "Dataset contains gene expression data from breast cancer patients, with clinical annotations including hypothyroidism status."}, "GSE151158": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 61, "note": "Dataset contains gene expression data studying hypothyroidism in the context of NAFLD progression, with clinical annotations."}, "TCGA": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 572, "note": "Used thyroid cancer (THCA) data as thyroid disorders are closely related to hypothyroidism"}}

p3/preprocess/Hypothyroidism/gene_data/GSE32445.csv

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p3/preprocess/Insomnia/clinical_data/GSE208668.csv

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p3/preprocess/Insomnia/cohort_info.json

@@ -0,0 +1 @@
+{"GSE208668": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 42, "note": "Dataset contains genome-wide transcriptional profiling of PBMCs from older adults with and without insomnia disorder."}, "TCGA": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}

p3/preprocess/Lower_Grade_Glioma/gene_data/GSE107850.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8cfc4c9142d4576bda39c238fd7300590dcdb3a81d4dc746610a06c4c8c0fcae
+size 47672138

p3/preprocess/Lower_Grade_Glioma/gene_data/GSE35158.csv

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+size 12648522

p3/preprocess/Pancreatic_Cancer/GSE130563.csv

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+size 10970181

p3/preprocess/Pancreatic_Cancer/GSE131027.csv

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+size 24380321

p3/preprocess/Pancreatic_Cancer/GSE183795.csv

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+size 66229403

p3/preprocess/Pancreatic_Cancer/clinical_data/GSE130563.csv

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p3/preprocess/Pancreatic_Cancer/clinical_data/GSE183795.csv

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+Pancreatic_Cancer,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+Age,83.0,64.0,59.0,64.0,72.0,72.0,89.0,59.0,64.0,82.0,75.0,61.0,59.0,68.0,49.0,71.0,68.0,58.0,76.0,67.0,52.0,57.0,72.0,59.0,53.0,95.0,53.0,55.0,43.0,71.0,48.0,43.0,55.0,63.0
+Gender,1.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0

p3/preprocess/Pancreatic_Cancer/code/GSE120127.py

@@ -0,0 +1,221 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Pancreatic_Cancer"
+cohort = "GSE120127"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
+in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE120127"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Pancreatic_Cancer/GSE120127.csv"
+out_gene_data_file = "./output/preprocess/3/Pancreatic_Cancer/gene_data/GSE120127.csv"
+out_clinical_data_file = "./output/preprocess/3/Pancreatic_Cancer/clinical_data/GSE120127.csv"
+json_path = "./output/preprocess/3/Pancreatic_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
+# Based on series title and genotype info, this appears to be gene expression data from pancreatic cancer cell lines
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion 
+# 2.1 Data Availability
+# Trait can be inferred from genotype (feature 2) - KrasG12D vs KO
+trait_row = 2
+
+# Gender can be found in feature 0
+gender_row = 0
+
+# Age not available for cell lines
+age_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    """Convert genotype to binary trait"""
+    if not value or not isinstance(value, str):
+        return None
+    value = value.split(': ')[-1].strip()
+    # Bap1 KO vs WT
+    if 'KO' in value:
+        return 1
+    elif 'WT' in value:
+        return 0
+    return None
+
+def convert_gender(value):
+    """Convert gender to binary"""
+    if not value or not isinstance(value, str):
+        return None
+    value = value.split(': ')[-1].strip().upper()
+    if value == 'F':
+        return 0
+    elif value == 'M':
+        return 1
+    return None
+
+convert_age = 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:
+    # Extract features using the library function
+    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
+    preview = preview_df(clinical_features)
+    print("Preview of clinical features:")
+    print(preview)
+    
+    # Save to CSV
+    clinical_features.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)
+# From the row identifiers and examining the number format, these appear to be Agilent probe IDs
+# These will need to be mapped to standard human gene symbols for analysis
+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))
+# Extract gene annotation data using a different prefix pattern for correct platform
+gene_annotation = filter_content_by_prefix(soft_file, prefixes_a=['^FEATURES'], unselect=True, source_type='file', 
+                                         return_df_a=True)[0]
+
+# Get mapping between probe IDs and gene symbols
+gene_mapping = gene_annotation.loc[:, ['ID', 'Gene Symbol']]
+gene_mapping = gene_mapping.rename(columns={'Gene Symbol': 'Gene'}).astype({'ID': 'str'})
+
+# Convert probe-level measurements to gene expression values
+gene_data = apply_gene_mapping(gene_data, gene_mapping)
+
+# Save the gene expression data
+gene_data.to_csv(out_gene_data_file)
+# 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 mapping between probe IDs and gene symbols using library function
+gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# Convert probe-level measurements to gene expression values using library function
+gene_data = apply_gene_mapping(gene_data, gene_mapping)
+
+# Save the gene expression data
+gene_data.to_csv(out_gene_data_file)
+# 1. Normalize gene symbols and save normalized gene data
+# Remove "-mRNA" suffix from gene symbols before normalization
+gene_data.index = gene_data.index.str.replace('-mRNA', '')
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data and trait
+# First get selected clinical features using the extraction function from previous step
+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
+)
+
+# Debug data structures before linking
+print("\nPre-linking data shapes:")
+print("Clinical data shape:", selected_clinical.shape)
+print("Gene data shape:", gene_data.shape)
+print("\nClinical data preview:")
+print(selected_clinical.head())
+
+# Transpose gene data to match clinical data orientation
+gene_data_t = gene_data.T
+linked_data = pd.concat([selected_clinical.T, gene_data_t], axis=1)
+
+# 3. Handle missing values systematically
+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 data quality and save metadata
+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="Gene expression data from pancreatic cancer study. All samples are cancer cases (no controls)."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Pancreatic_Cancer/code/GSE124069.py

@@ -0,0 +1,189 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Pancreatic_Cancer"
+cohort = "GSE124069"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
+in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE124069"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Pancreatic_Cancer/GSE124069.csv"
+out_gene_data_file = "./output/preprocess/3/Pancreatic_Cancer/gene_data/GSE124069.csv"
+out_clinical_data_file = "./output/preprocess/3/Pancreatic_Cancer/clinical_data/GSE124069.csv"
+json_path = "./output/preprocess/3/Pancreatic_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
+# Yes, this dataset contains gene expression data from microarrays
+is_gene_available = True
+
+# 2.1 Data Availability
+# Trait (disease state) is available in row 0
+trait_row = 0
+
+# Age and gender data not available in sample characteristics
+age_row = None
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert pancreatic cancer status to binary"""
+    if not isinstance(value, str):
+        return None
+    value = value.lower().split(': ')[-1].strip()
+    if 'pancreatic cancer' in value:
+        return 1
+    return None
+
+def convert_age(value: str) -> float:
+    """Convert age to float - not used since age not available"""
+    return None
+
+def convert_gender(value: str) -> int:
+    """Convert gender to binary - not used since gender not available"""
+    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:
+    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
+    preview = preview_df(clinical_features)
+    print("Preview of clinical features:")
+    print(preview)
+    
+    # Save to CSV
+    clinical_features.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)
+# Based on the identifiers starting with numbers and having '_at' suffix (e.g. '1007_s_at', '1053_at'),
+# these are Affymetrix probe IDs which need to be mapped to official gene symbols
+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))
+# Extract ID and Gene Symbol columns to create mapping table
+gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# Apply gene mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=gene_mapping)
+
+# Preview
+print("Shape of mapped gene data:", gene_data.shape)
+print("\nFirst few rows of mapped gene data:")
+print(gene_data.head())
+# 1. Normalize gene symbols and save normalized gene data
+# Remove "-mRNA" suffix from gene symbols before normalization
+gene_data.index = gene_data.index.str.replace('-mRNA', '')
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data and trait
+# First get selected clinical features using the extraction function from previous step
+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
+)
+
+# Debug data structures before linking
+print("\nPre-linking data shapes:")
+print("Clinical data shape:", selected_clinical.shape)
+print("Gene data shape:", gene_data.shape)
+print("\nClinical data preview:")
+print(selected_clinical.head())
+
+# Transpose gene data to match clinical data orientation
+gene_data_t = gene_data.T
+linked_data = pd.concat([selected_clinical.T, gene_data_t], axis=1)
+
+# 3. Handle missing values systematically
+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 data quality and save metadata
+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="Gene expression data from pancreatic cancer study. All samples are cancer cases (no controls)."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Pancreatic_Cancer/code/GSE125158.py

@@ -0,0 +1,207 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Pancreatic_Cancer"
+cohort = "GSE125158"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
+in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE125158"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Pancreatic_Cancer/GSE125158.csv"
+out_gene_data_file = "./output/preprocess/3/Pancreatic_Cancer/gene_data/GSE125158.csv"
+out_clinical_data_file = "./output/preprocess/3/Pancreatic_Cancer/clinical_data/GSE125158.csv"
+json_path = "./output/preprocess/3/Pancreatic_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
+# Yes, this dataset contains mRNA data from whole blood cells, as indicated in the series title
+is_gene_available = True
+
+# 2. Variable Identification and Conversion Functions
+# 2.1 Data Availability
+trait_row = 0  # diagnosis
+age_row = 3  # age
+gender_row = 2  # gender
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    if not isinstance(x, str):
+        return None
+    x = x.lower()
+    if 'diagnosis:' not in x:
+        return None
+    val = x.split('diagnosis:')[1].strip()
+    if 'pancreatic cancer' in val:
+        return 1
+    elif 'healthy' in val:
+        return 0
+    return None
+
+def convert_age(x):
+    if not isinstance(x, str):
+        return None
+    if 'age:' not in x:
+        return None
+    try:
+        return float(x.split('age:')[1].strip())
+    except:
+        return None
+
+def convert_gender(x):
+    if not isinstance(x, str):
+        return None
+    x = x.lower()
+    if 'gender:' not in x:
+        return None
+    val = x.split('gender:')[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
+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 data:")
+    print(preview)
+    
+    # Save clinical features
+    clinical_features.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)
+# Looking at the ID patterns (prefixes like A_23_P, eQC, etc.), 
+# these appear to be Agilent probe IDs rather than human gene symbols
+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))
+# 'ID' in annotation matches the probe IDs in expression data
+# 'GENE_SYMBOL' contains the corresponding gene symbols
+mapping_data = get_gene_mapping(gene_annotation, 'ID', 'GENE_SYMBOL')
+
+# Apply gene mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Print shape and preview results
+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
+# Remove "-mRNA" suffix from gene symbols before normalization
+gene_data.index = gene_data.index.str.replace('-mRNA', '')
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data and trait
+# First get selected clinical features using the extraction function from previous step
+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
+)
+
+# Debug data structures before linking
+print("\nPre-linking data shapes:")
+print("Clinical data shape:", selected_clinical.shape)
+print("Gene data shape:", gene_data.shape)
+print("\nClinical data preview:")
+print(selected_clinical.head())
+
+# Transpose gene data to match clinical data orientation
+gene_data_t = gene_data.T
+linked_data = pd.concat([selected_clinical.T, gene_data_t], axis=1)
+
+# 3. Handle missing values systematically
+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 data quality and save metadata
+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="Gene expression data from pancreatic cancer study. All samples are cancer cases (no controls)."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Pancreatic_Cancer/code/GSE130563.py

@@ -0,0 +1,259 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Pancreatic_Cancer"
+cohort = "GSE130563"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
+in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE130563"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Pancreatic_Cancer/GSE130563.csv"
+out_gene_data_file = "./output/preprocess/3/Pancreatic_Cancer/gene_data/GSE130563.csv"
+out_clinical_data_file = "./output/preprocess/3/Pancreatic_Cancer/clinical_data/GSE130563.csv"
+json_path = "./output/preprocess/3/Pancreatic_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
+# Yes - this is a microarray study analyzing transcriptional profiling data
+is_gene_available = True
+
+# 2. Variable Availability and Data Types
+
+# 2.1 Data Availability
+trait_row = 0  # Diagnosis info in row 0
+age_row = 4   # Age info in row 4
+gender_row = 1  # Sex info in row 1
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert diagnosis info to binary: 1 for PDAC, 0 for non-cancer controls"""
+    if value is None or 'diagnosis:' not in value:
+        return None
+    diagnosis = value.split('diagnosis:')[1].strip().lower()
+    if 'pancreatic ductal adenocarcinoma' in diagnosis:
+        return 1
+    elif 'chronic pancreatitis' in diagnosis:  # Excluded from analysis per background info
+        return None
+    else:  # All other diagnoses are non-cancer controls
+        return 0
+
+def convert_age(value: str) -> float:
+    """Convert age to continuous value"""
+    if value is None or 'age:' not in value:
+        return None
+    try:
+        return float(value.split('age:')[1].strip())
+    except:
+        return None
+
+def convert_gender(value: str) -> int:
+    """Convert sex to binary: 0 for female, 1 for male"""
+    if value is None or 'Sex:' not in value:
+        return None
+    sex = value.split('Sex:')[1].strip().upper()
+    if sex == 'F':
+        return 0
+    elif sex == '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_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_df))
+    
+    # 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)
+# The gene identifiers end with '_at', which is a characteristic format of Affymetrix 
+# microarray probe IDs rather than standard human gene symbols
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Let's inspect more of the raw SOFT file to find the annotation data
+import gzip
+start_line = "!platform_table_begin"
+end_line = "!platform_table_end"
+found_data = False
+print("Sample of annotation data from SOFT file:")
+with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
+    for line in f:
+        if start_line in line:
+            found_data = True
+            # Skip the header line
+            next(f)
+            # Print first few lines of actual data
+            for _ in range(5):
+                print(next(f).strip())
+            break
+
+# Extract gene annotation data - exclude metadata prefixes and keep data between platform table markers
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview annotation data
+print("\nGene annotation columns and example values:")
+print(preview_df(gene_annotation))
+
+# Display column names to help identify relevant fields
+print("\nAvailable columns:")
+print(gene_annotation.columns.tolist())
+# Since we can't access the proper gene symbol mapping file, 
+# let's look for gene annotation information in the SOFT file
+import gzip
+
+# Search for gene symbols in the SOFT file
+found_symbols = False
+gene_symbols = []
+
+with gzip.open(soft_file, 'rt') as f:
+    for line in f:
+        # Look for platform table begin marker
+        if "!Platform_table_begin" in line:
+            headers = next(f).strip().split('\t')
+            # Find columns that might contain gene symbol information
+            symbol_cols = [i for i, h in enumerate(headers) 
+                         if 'symbol' in h.lower() or 'gene' in h.lower()]
+            if symbol_cols:
+                found_symbols = True
+                # Extract gene symbols from identified columns
+                for line in f:
+                    if "!Platform_table_end" in line:
+                        break
+                    values = line.strip().split('\t')
+                    for col in symbol_cols:
+                        if col < len(values):
+                            gene_symbols.append(values[col])
+            break
+
+if found_symbols and len(gene_symbols) > 0:
+    # Create mapping using found gene symbols
+    unique_probes = gene_annotation['ID'].unique()
+    mapping_df = pd.DataFrame({
+        'ID': unique_probes,
+        'Gene': gene_symbols[:len(unique_probes)]
+    })
+else:
+    # If no gene symbols found, create temporary mapping using probe IDs
+    # This allows pipeline to continue but indicates mapping needs to be updated
+    mapping_df = pd.DataFrame({
+        'ID': gene_annotation['ID'],
+        'Gene': gene_annotation['ID']
+    })
+    print("WARNING: No gene symbols found. Using probe IDs as temporary mapping.")
+
+# Convert probe-level measurements to gene-level measurements
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+print("Shape of gene expression data after mapping:", gene_data.shape)
+print("\nPreview of mapped gene expression data:")
+print(gene_data.head())
+# 1. Skip normalization and use probe-level data since gene mapping failed
+gene_data = get_genetic_data(matrix_file)
+print("WARNING: Using probe IDs instead of gene symbols due to failed mapping")
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data and trait
+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
+)
+
+# Debug pre-linking
+print("\nPre-linking data shapes:")
+print("Clinical data shape:", selected_clinical.shape)
+print("Gene data shape:", gene_data.shape)
+print("\nClinical data preview:")
+print(selected_clinical.head())
+
+# Link the data
+gene_data_t = gene_data.T
+linked_data = pd.concat([selected_clinical.T, gene_data_t], axis=1)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate data quality and save metadata
+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="Gene expression data from pancreatic cancer study. Using probe IDs instead of gene symbols."
+)
+
+# 6. Save if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Pancreatic_Cancer/code/GSE131027.py

@@ -0,0 +1,190 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Pancreatic_Cancer"
+cohort = "GSE131027"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
+in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE131027"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Pancreatic_Cancer/GSE131027.csv"
+out_gene_data_file = "./output/preprocess/3/Pancreatic_Cancer/gene_data/GSE131027.csv"
+out_clinical_data_file = "./output/preprocess/3/Pancreatic_Cancer/clinical_data/GSE131027.csv"
+json_path = "./output/preprocess/3/Pancreatic_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
+# Based on the series title and design, this appears to be focused on germline mutations and variants
+# rather than gene expression data
+is_gene_available = False
+
+# 2.1 Data Row Identifiers
+# For trait, we can use cancer types from Feature 1
+trait_row = 1
+
+# Age and gender are not recorded in the characteristics
+age_row = None  
+gender_row = None
+
+# 2.2 Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert cancer type to binary indicating if it's pancreatic cancer"""
+    if not value or ':' not in value:
+        return None
+    cancer_type = value.split(':')[1].strip().lower()
+    return 1 if 'pancreatic cancer' in cancer_type else 0
+
+def convert_age(value: str) -> float:
+    """Placeholder function since age data is not available"""
+    return None
+
+def convert_gender(value: str) -> int:
+    """Placeholder function since gender data is not available"""
+    return None
+
+# 3. Save Initial 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. 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 data
+    print("Preview of selected clinical features:")
+    print(preview_df(selected_clinical_df))
+    
+    # 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)
+# Based on the identifiers like "1007_s_at", "1053_at", these are Affymetrix probe IDs
+# rather than standard human gene symbols. They need to be mapped to gene symbols.
+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 mapping data between probe IDs and gene symbols
+mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# Apply gene mapping to convert probe data to gene expression 
+mapped_gene_data = apply_gene_mapping(gene_data, mapping_data)
+gene_data = mapped_gene_data
+
+# Preview results
+print("Shape of mapped gene expression data:", gene_data.shape)
+print("\nFirst few rows of mapped data:")
+print(gene_data.head())
+print("\nFirst 20 gene symbols:")
+print(gene_data.index[:20])
+# 1. Normalize gene symbols and save normalized gene data
+# Remove "-mRNA" suffix from gene symbols before normalization
+gene_data.index = gene_data.index.str.replace('-mRNA', '')
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data and trait
+# First get selected clinical features using the extraction function from previous step
+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
+)
+
+# Debug data structures before linking
+print("\nPre-linking data shapes:")
+print("Clinical data shape:", selected_clinical.shape)
+print("Gene data shape:", gene_data.shape)
+print("\nClinical data preview:")
+print(selected_clinical.head())
+
+# Transpose gene data to match clinical data orientation
+gene_data_t = gene_data.T
+linked_data = pd.concat([selected_clinical.T, gene_data_t], axis=1)
+
+# 3. Handle missing values systematically
+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 data quality and save metadata
+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="Gene expression data from pancreatic cancer study. All samples are cancer cases (no controls)."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Pancreatic_Cancer/code/GSE157494.py

@@ -0,0 +1,217 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Pancreatic_Cancer"
+cohort = "GSE157494"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
+in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE157494"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Pancreatic_Cancer/GSE157494.csv"
+out_gene_data_file = "./output/preprocess/3/Pancreatic_Cancer/gene_data/GSE157494.csv"
+out_clinical_data_file = "./output/preprocess/3/Pancreatic_Cancer/clinical_data/GSE157494.csv"
+json_path = "./output/preprocess/3/Pancreatic_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 = filter_content_by_prefix(matrix_file, 
+                                                        prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
+                                                        prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1'], 
+                                                        unselect=False,
+                                                        source_type='file',
+                                                        return_df_a=False,
+                                                        return_df_b=True,
+                                                        transpose=True)
+
+# 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")
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = filter_content_by_prefix(
+    matrix_file,
+    prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
+    prefixes_b=['!Sample_characteristics_ch'],  
+    unselect=False,
+    source_type='file',
+    return_df_a=False,
+    return_df_b=True
+)
+
+# 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
+# Yes - the series summary mentions gene expression profiling with Affymetrix Gene Chip
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# Sample Characteristics output is empty, indicating no clinical data available
+trait_row = None  
+age_row = None
+gender_row = None
+
+def convert_trait(x):
+    return None
+
+def convert_age(x): 
+    return None
+
+def convert_gender(x):
+    return None
+
+# 3. Save metadata
+# Initial filtering - save info that this dataset has gene data but no clinical data
+validate_and_save_cohort_info(is_final=False,
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=False)
+
+# 4. Clinical Feature Extraction
+# Skip since trait_row is None (no clinical data available)
+# 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)
+# Looking at the IDs (e.g. 1007_s_at, 1053_at), these are Affymetrix probe IDs 
+# from HG-U133_Plus_2 array platform, not gene symbols.
+# They need to be mapped to human gene symbols for standardized analysis
+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))
+# Looking at gene_data index ['1007_s_at', '1053_at', '117_at'...] and 
+# gene_annotation dictionary preview, 'ID' column contains probe IDs matching gene_data index,
+# and 'Gene Symbol' column contains the gene symbols we need
+
+# Create mapping between probe IDs and gene symbols
+gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# Convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(gene_data, gene_mapping)
+
+# Normalize gene symbols to standard format using synonym dictionary
+gene_data = normalize_gene_symbols_in_index(gene_data)
+# First get the gene expression data again
+gene_data = get_genetic_data(matrix_file)
+gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+gene_data = apply_gene_mapping(gene_data, gene_mapping)
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Save normalized gene data
+gene_data.to_csv(out_gene_data_file)
+
+# Update cohort info - dataset unusable due to lack of clinical data
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False, 
+    is_biased=None,
+    df=None,
+    note="Gene expression data available but no clinical annotations/controls present."
+)
+# 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 file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get gene annotation first
+gene_annotation = get_gene_annotation(soft_file)
+
+# Get gene expression data
+gene_data = get_genetic_data(matrix_file)
+
+# Create mapping between probe IDs and gene symbols
+gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# Convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(gene_data, gene_mapping)
+
+# Print dimensions of result for verification 
+print(f"\nShape of gene expression data after mapping: {gene_data.shape}")
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get gene annotation and expression data 
+gene_annotation = get_gene_annotation(soft_file)
+gene_data = get_genetic_data(matrix_file)
+
+# Create mapping and apply it
+gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol') 
+gene_data = apply_gene_mapping(gene_data, gene_mapping)
+
+# Normalize gene symbols and save gene data
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# Update cohort info - dataset unusable due to lack of clinical data
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=None, 
+    df=None,
+    note="Gene expression data available but no clinical annotations/controls present."
+)

p3/preprocess/Pancreatic_Cancer/code/GSE183795.py

@@ -0,0 +1,200 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Pancreatic_Cancer"
+cohort = "GSE183795"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
+in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE183795"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Pancreatic_Cancer/GSE183795.csv"
+out_gene_data_file = "./output/preprocess/3/Pancreatic_Cancer/gene_data/GSE183795.csv"
+out_clinical_data_file = "./output/preprocess/3/Pancreatic_Cancer/clinical_data/GSE183795.csv"
+json_path = "./output/preprocess/3/Pancreatic_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
+# Based on the background info, this is a microarray gene expression dataset
+is_gene_available = True 
+
+# 2.1 Data Availability
+# For trait: Feature 0 contains tissue type info (tumor vs non-tumor)
+trait_row = 0
+
+# Age is not available in the data
+age_row = None 
+
+# Gender is not available in the data
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert tissue type to binary: 1 for tumor, 0 for non-tumor/normal"""
+    if not value or 'tissue:' not in value:
+        return None
+    value = value.split('tissue:')[1].strip().lower()
+    if 'tumor' in value and 'non' not in value:
+        return 1
+    elif 'non-tumor' in value or 'normal' in value:
+        return 0
+    return None
+
+def convert_age(value: str) -> float:
+    """Convert age to float"""
+    return None
+
+def convert_gender(value: str) -> int:
+    """Convert gender to binary"""
+    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. Extract clinical features
+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)
+    
+    print("Preview of extracted clinical features:")
+    print(preview_df(clinical_features))
+    
+    # Save clinical data
+    clinical_features.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)
+# The identifiers appear to be probe IDs, not gene symbols
+# The format is numerical IDs (e.g. 7896748, 7896754) which are probe identifiers 
+# from the microarray platform
+# These need to be mapped to gene symbols
+
+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))
+# 1. Observe the IDs used in gene expression data and gene annotation data 
+# In gene expression data, we see probe IDs like '7896748', '7896754', etc.
+# In gene annotation, these probe IDs are stored in the 'ID' column
+# Gene symbols are stored in 'gene_assignment' column
+
+# 2. Extract probe-to-gene mapping 
+mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')
+
+# 3. Convert probe-level data to gene-level data using mapping
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Save the processed gene data
+gene_data.to_csv(out_gene_data_file)
+
+# Preview the gene_data to verify the conversion
+print("Gene expression data shape after mapping:", gene_data.shape)
+print("\nFirst few genes and their expression values:")
+print(preview_df(gene_data))
+# 1. Normalize gene symbols and save normalized gene data
+# Remove "-mRNA" suffix from gene symbols before normalization
+gene_data.index = gene_data.index.str.replace('-mRNA', '')
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data and trait
+# First get selected clinical features using the extraction function from previous step
+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
+)
+
+# Debug data structures before linking
+print("\nPre-linking data shapes:")
+print("Clinical data shape:", selected_clinical.shape)
+print("Gene data shape:", gene_data.shape)
+print("\nClinical data preview:")
+print(selected_clinical.head())
+
+# Transpose gene data to match clinical data orientation
+gene_data_t = gene_data.T
+linked_data = pd.concat([selected_clinical.T, gene_data_t], axis=1)
+
+# 3. Handle missing values systematically
+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 data quality and save metadata
+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="Gene expression data from pancreatic cancer study. All samples are cancer cases (no controls)."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Pancreatic_Cancer/code/GSE222788.py

@@ -0,0 +1,166 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Pancreatic_Cancer"
+cohort = "GSE222788"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
+in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE222788"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Pancreatic_Cancer/GSE222788.csv"
+out_gene_data_file = "./output/preprocess/3/Pancreatic_Cancer/gene_data/GSE222788.csv"
+out_clinical_data_file = "./output/preprocess/3/Pancreatic_Cancer/clinical_data/GSE222788.csv"
+json_path = "./output/preprocess/3/Pancreatic_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
+# From background info, this is a NanoString gene profiling study with 730 genes panel
+is_gene_available = True
+
+# 2.1 Data Availability & Row Keys
+# From sample characteristics, only treatment group info is available (row 0)
+# Can infer trait (cancer vs control) from treatment - patients are all cancer cases
+trait_row = 0
+age_row = None  # Age not recorded
+gender_row = None  # Gender not recorded
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    # Extract value after colon and strip whitespace
+    if ':' in value:
+        value = value.split(':')[1].strip()
+    # All samples are cancer cases - convert to binary 1
+    return 1
+
+def convert_age(value):
+    return None  # Not used since age data not available
+
+def convert_gender(value):
+    return None  # Not used since gender data not available
+
+# 3. Save metadata about data availability
+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. Extract clinical features if trait data available
+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 data
+    preview = preview_df(selected_clinical)
+    print("Preview of selected clinical features:")
+    print(preview)
+    
+    # Save to CSV
+    selected_clinical.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)
+# Looking at gene identifiers like "A2M-mRNA", "ABCB1-mRNA", etc.
+# These are already human gene symbols with "-mRNA" suffix
+# No mapping needed, just need to remove the "-mRNA" suffix
+requires_gene_mapping = False
+# 1. Normalize gene symbols and save normalized gene data
+# Remove "-mRNA" suffix from gene symbols before normalization
+gene_data.index = gene_data.index.str.replace('-mRNA', '')
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data and trait
+# First get selected clinical features using the extraction function from previous step
+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
+)
+
+# Debug data structures before linking
+print("\nPre-linking data shapes:")
+print("Clinical data shape:", selected_clinical.shape)
+print("Gene data shape:", gene_data.shape)
+print("\nClinical data preview:")
+print(selected_clinical.head())
+
+# Transpose gene data to match clinical data orientation
+gene_data_t = gene_data.T
+linked_data = pd.concat([selected_clinical.T, gene_data_t], axis=1)
+
+# 3. Handle missing values systematically
+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 data quality and save metadata
+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="Gene expression data from pancreatic cancer study. All samples are cancer cases (no controls)."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Pancreatic_Cancer/code/GSE223409.py

@@ -0,0 +1,173 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Pancreatic_Cancer"
+cohort = "GSE223409"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
+in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE223409"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Pancreatic_Cancer/GSE223409.csv"
+out_gene_data_file = "./output/preprocess/3/Pancreatic_Cancer/gene_data/GSE223409.csv"
+out_clinical_data_file = "./output/preprocess/3/Pancreatic_Cancer/clinical_data/GSE223409.csv"
+json_path = "./output/preprocess/3/Pancreatic_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
+# Based on the background info, this appears to be an EVs study with specific gene treatment
+# and likely contains gene expression data
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# Looking at sample characteristics:
+# - Can infer trait data from treatment groups in row 1
+# - No age data 
+# - No gender data
+
+# Row indices for variables
+trait_row = 1  # Can infer from treatment groups
+age_row = None  # Age not available
+gender_row = None  # Gender not available
+
+def convert_trait(value: str) -> int:
+    """Convert treatment value to binary trait."""
+    if pd.isna(value):
+        return None
+    value = value.split(': ')[-1].lower()
+    # Consider control/PBS as non-cancer (0) and treated as cancer (1)
+    if 'control' in value or 'pbs' in value:
+        return 0
+    return 1
+
+# Age and gender conversion functions not needed since data unavailable
+convert_age = None
+convert_gender = 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:
+    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
+    )
+    print("Preview of extracted clinical features:")
+    print(preview_df(clinical_features))
+    clinical_features.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)
+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 mapping between probe IDs and gene symbols from annotation data
+mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# Apply mapping to convert probe data to gene expression
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Preview the result 
+print("Shape of mapped gene expression data:", gene_data.shape)
+print("\nFirst few rows of mapped data:")
+print(gene_data.head())
+# 1. Normalize gene symbols and save normalized gene data
+gene_data = normalize_gene_symbols_in_index(gene_data) 
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data and trait
+# First get selected clinical features using the extraction function from previous step
+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
+)
+
+linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)
+
+# 3. Handle missing values systematically
+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 data quality and save metadata
+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="Gene expression data from extracellular vesicles in pancreatic cancer study"
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Pancreatic_Cancer/code/GSE236951.py

@@ -0,0 +1,165 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Pancreatic_Cancer"
+cohort = "GSE236951"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
+in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE236951"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Pancreatic_Cancer/GSE236951.csv"
+out_gene_data_file = "./output/preprocess/3/Pancreatic_Cancer/gene_data/GSE236951.csv"
+out_clinical_data_file = "./output/preprocess/3/Pancreatic_Cancer/clinical_data/GSE236951.csv"
+json_path = "./output/preprocess/3/Pancreatic_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
+# The series summary indicates nanostring gene expression analysis of ~700 immune related genes
+is_gene_available = True
+
+# 2.1 Data Availability 
+# Disease status in row 0, gender in row 2, age in row 3
+trait_row = 0
+gender_row = 2 
+age_row = 3
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x: str) -> Optional[int]:
+    if not isinstance(x, str):
+        return None
+    x = x.lower().split(': ')[-1]
+    if 'pancreatic' in x:
+        return 1
+    elif 'colon' in x or 'benign' in x:
+        return 0
+    return None
+
+def convert_gender(x: str) -> Optional[int]:
+    if not isinstance(x, str):
+        return None
+    x = x.lower().split(': ')[-1]
+    if 'female' in x:
+        return 0
+    elif 'male' in x:
+        return 1
+    return None
+
+def convert_age(x: str) -> Optional[float]:
+    if not isinstance(x, str):
+        return None
+    try:
+        return float(x.split(': ')[-1].split()[0])
+    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. 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
+)
+
+print("Preview of extracted clinical features:")
+print(preview_df(clinical_features))
+
+clinical_features.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)
+# The gene identifiers are already human gene symbols (like A2M, ABCB1, ABL1, etc.)
+# No mapping is needed
+requires_gene_mapping = False
+# 1. Normalize gene symbols and save normalized gene data
+gene_data = normalize_gene_symbols_in_index(gene_data) 
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data and trait
+# First get selected clinical features using the extraction function from previous step
+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
+)
+
+linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)
+
+# 3. Handle missing values systematically
+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 data quality and save metadata
+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="Gene expression data comparing cervical carcinoma vs normal tissue samples"
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)