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p3/preprocess/Depression/code/GSE110298.py

@@ -0,0 +1,166 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Depression"
+cohort = "GSE110298"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Depression"
+in_cohort_dir = "../DATA/GEO/Depression/GSE110298"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Depression/GSE110298.csv"
+out_gene_data_file = "./output/preprocess/3/Depression/gene_data/GSE110298.csv"
+out_clinical_data_file = "./output/preprocess/3/Depression/clinical_data/GSE110298.csv"
+json_path = "./output/preprocess/3/Depression/cohort_info.json"
+
+# Get paths to the SOFT and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values for each feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Check gene expression data availability
+# Based on the background info, this dataset contains hippocampal gene expression data using microarrays
+is_gene_available = True
+
+# 2. Check variables availability and define conversion functions
+# 2.1 Identify row numbers for variables
+trait_row = 6  # depression data is in row 6
+age_row = 2    # age data is in row 2 
+gender_row = 1 # gender data is in row 1
+
+# 2.2 Define conversion functions
+def convert_trait(value):
+    """Convert depression score (0-8) to binary (0/1)"""
+    try:
+        if ':' in value:
+            score = int(value.split(':')[1].strip())
+            # Scores > 0 indicate presence of depression symptoms
+            return 1 if score > 0 else 0
+        return None
+    except:
+        return None
+
+def convert_age(value):
+    """Convert age to continuous value"""
+    try:
+        if ':' in value:
+            age = int(value.split(':')[1].strip())
+            return age
+        return None
+    except:
+        return None
+
+def convert_gender(value):
+    """Convert gender to binary (0=female, 1=male)"""
+    try:
+        if ':' in value:
+            gender = value.split(':')[1].strip().lower()
+            if 'female' in gender:
+                return 0
+            elif 'male' in gender:
+                return 1
+        return None
+    except:
+        return None
+
+# 3. Save initial metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=trait_row is not None
+)
+
+# 4. Extract clinical features
+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 data
+    print("Preview of extracted clinical features:")
+    print(preview_df(clinical_features))
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+# Based on the ID format (e.g., "1007_s_at"), these look like Affymetrix probe IDs
+# which need to be mapped to standard human gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file) 
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# Get mapping from probe IDs to gene symbols
+mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')
+
+# Apply mapping to convert probe data to gene expression data
+gene_data = apply_gene_mapping(genetic_df, mapping_data)
+
+# Preview result
+print("Gene expression data shape:", gene_data.shape)
+print("\nFirst few rows and columns:")
+print(gene_data.head().iloc[:, :5])
+# 1. Normalize gene symbols and save
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True, 
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True, 
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Study of depression in obese patients before and after bariatric surgery"
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Depression/code/GSE128387.py

@@ -0,0 +1,167 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Depression"
+cohort = "GSE128387"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Depression"
+in_cohort_dir = "../DATA/GEO/Depression/GSE128387"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Depression/GSE128387.csv"
+out_gene_data_file = "./output/preprocess/3/Depression/gene_data/GSE128387.csv"
+out_clinical_data_file = "./output/preprocess/3/Depression/clinical_data/GSE128387.csv"
+json_path = "./output/preprocess/3/Depression/cohort_info.json"
+
+# Get paths to the SOFT and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values for each feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Check gene expression data availability
+# From background info, this is a microarray study of gene expression, not miRNA/methylation
+is_gene_available = True
+
+# 2.1 Identify data rows
+trait_row = 1  # "illness" field indicates depression status
+age_row = 2    # "age" field
+gender_row = 3 # "Sex" field
+
+# 2.2 Data conversion functions
+def convert_trait(value: str) -> int:
+    """Convert depression status to binary"""
+    if not isinstance(value, str):
+        return None
+    value = value.split(': ')[-1].lower()
+    if 'major depressive disorder' in value:
+        return 1
+    return None
+
+def convert_age(value: str) -> float:
+    """Convert age to continuous value"""
+    if not isinstance(value, str):
+        return None
+    try:
+        age = float(value.split(': ')[-1])
+        return age
+    except:
+        return None
+
+def convert_gender(value: str) -> int:
+    """Convert gender to binary (0=female, 1=male)"""
+    if not isinstance(value, str):
+        return None
+    value = value.split(': ')[-1].lower()
+    if value == 'female':
+        return 0
+    elif value == 'male':
+        return 1
+    return None
+
+# 3. Save metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=trait_row is not None
+)
+
+# 4. Extract clinical features
+clinical_df = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Preview and save clinical data
+print("Clinical data preview:")
+print(preview_df(clinical_df))
+
+clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+# The gene identifiers appear to be probe IDs 
+# (numeric identifiers around 16657xxx) rather than human gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file) 
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# 1. Based on observation:
+# - Gene expression data has identifiers like '16657436'
+# - In annotation data, 'ID' column has the same format identifiers
+# - 'gene_assignment' column contains gene symbol info in the format "//GENE_SYMBOL//"
+
+# 2. Extract ID and gene assignments, then get mapping between them
+mapping_df = get_gene_mapping(gene_metadata, 'ID', 'gene_assignment')
+
+# 3. Map probe IDs to gene symbols and convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Preview results
+print("Gene expression data shape:", gene_data.shape)
+print("\nFirst few genes and samples:")
+print(gene_data.head().iloc[:, :5])
+
+# Save gene data
+gene_data.to_csv(out_gene_data_file)
+# 1. Normalize gene symbols and save
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True, 
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True, 
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Study of depression in obese patients before and after bariatric surgery"
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Depression/code/GSE135524.py

@@ -0,0 +1,160 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Depression"
+cohort = "GSE135524"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Depression"
+in_cohort_dir = "../DATA/GEO/Depression/GSE135524"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Depression/GSE135524.csv"
+out_gene_data_file = "./output/preprocess/3/Depression/gene_data/GSE135524.csv"
+out_clinical_data_file = "./output/preprocess/3/Depression/clinical_data/GSE135524.csv"
+json_path = "./output/preprocess/3/Depression/cohort_info.json"
+
+# Get paths to the SOFT and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values for each feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Yes - the series studies gene expression in blood samples
+is_gene_available = True 
+
+# 2.1 Data Availability
+# Trait (Depression severity) available in hamd score (row 5)
+trait_row = 5 
+# Age available in row 1
+age_row = 1
+# Gender available in row 2  
+gender_row = 2
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    if not isinstance(x, str):
+        return None
+    try:
+        # Extract HAMD score which indicates depression severity
+        score = int(x.split(': ')[1])
+        return score # Keep as continuous
+    except:
+        return None
+
+def convert_age(x):
+    if not isinstance(x, str):
+        return None
+    try:
+        age = int(x.split(': ')[1])
+        return age
+    except:
+        return None
+
+def convert_gender(x):
+    if not isinstance(x, str):
+        return None
+    value = x.split(': ')[1].lower()
+    if 'female' in value:
+        return 0
+    elif 'male' in value:
+        return 1
+    return None
+
+# 3. Save metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# 4. Extract clinical features
+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_df(selected_clinical_df))
+
+# Save clinical data
+selected_clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+# ILMN_ prefix indicates these are Illumina probe IDs, not gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file) 
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# Get the mapping between gene identifiers (ID) and gene symbols (Symbol)
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Symbol')
+
+# Apply gene mapping to convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Preview result
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst 5 rows and 5 columns:")
+print(gene_data.iloc[:5, :5])
+# 1. Normalize gene symbols and save
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True, 
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True, 
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Study of depression in obese patients before and after bariatric surgery"
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Depression/code/GSE138297.py

@@ -0,0 +1,183 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Depression"
+cohort = "GSE138297"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Depression"
+in_cohort_dir = "../DATA/GEO/Depression/GSE138297"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Depression/GSE138297.csv"
+out_gene_data_file = "./output/preprocess/3/Depression/gene_data/GSE138297.csv"
+out_clinical_data_file = "./output/preprocess/3/Depression/clinical_data/GSE138297.csv"
+json_path = "./output/preprocess/3/Depression/cohort_info.json"
+
+# Get paths to the SOFT and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values for each feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Based on the background, this study used microarray, so gene expression data is available
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# Trait (experimental condition) is in row 6
+trait_row = 6
+
+def convert_trait(value):
+    # Extract value after colon if present
+    if ':' in value:
+        value = value.split(':')[1].strip()
+    # Convert to binary based on FMT type
+    if 'Allogenic FMT' in value:
+        return 1
+    elif 'Autologous FMT' in value: 
+        return 0
+    return None
+
+# Age is in row 3
+age_row = 3
+
+def convert_age(value):
+    # Extract value after colon
+    if ':' in value:
+        value = value.split(':')[1].strip()
+        # Convert to float if possible
+        try:
+            return float(value)
+        except:
+            return None
+    return None
+
+# Gender is in row 1
+gender_row = 1
+
+def convert_gender(value):
+    # Extract value after colon
+    if ':' in value:
+        value = value.split(':')[1].strip()
+        # Data is already coded as 1=female, 0=male
+        # But we need to reverse it to match our convention (0=female, 1=male)
+        try:
+            return 1 - int(value) # Converts 1->0 (female) and 0->1 (male)
+        except:
+            return None
+    return None
+
+# 3. Save metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=trait_row is not None
+)
+
+# 4. Extract clinical features
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    print("Preview of selected clinical features:")
+    print(preview_df(selected_clinical_df))
+    
+    # Create directory if it doesn't exist
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    
+    # Save to CSV
+    selected_clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+# The IDs look like probe IDs from a microarray platform since they are numerical
+# and have a specific format (e.g., 16650001, 16650003). These are not standard 
+# human gene symbols which typically use letters (e.g., GAPDH, TP53).
+# The probes will need to be mapped to gene symbols.
+
+requires_gene_mapping = True
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file) 
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# The 'ID' column in gene_metadata contains the same identifiers as in genetic_df
+# The 'gene_assignment' column contains gene symbols and information
+
+# Extract gene mapping dataframe 
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='gene_assignment')
+
+# Apply gene mapping to convert probe data to gene data
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Normalize gene symbols using the synonym dictionary
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Save to file
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+print("\nGene data shape:", gene_data.shape)
+print("\nPreview of gene data:")
+print(preview_df(gene_data))
+# 1. Normalize gene symbols and save
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True, 
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True, 
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Study of depression in obese patients before and after bariatric surgery"
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Depression/code/GSE149980.py

@@ -0,0 +1,145 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Depression"
+cohort = "GSE149980"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Depression"
+in_cohort_dir = "../DATA/GEO/Depression/GSE149980"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Depression/GSE149980.csv"
+out_gene_data_file = "./output/preprocess/3/Depression/gene_data/GSE149980.csv"
+out_clinical_data_file = "./output/preprocess/3/Depression/clinical_data/GSE149980.csv"
+json_path = "./output/preprocess/3/Depression/cohort_info.json"
+
+# Get paths to the SOFT and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values for each feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Based on background info, this is a gene expression study
+is_gene_available = True
+
+# 2.1 Data Availability
+# Trait (response status) is in row 0
+trait_row = 0
+# Age and gender are not available in sample characteristics
+age_row = None 
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    """Convert treatment response status to binary (0=non-responder, 1=responder)"""
+    if pd.isna(x):
+        return None
+    value = x.split(': ')[1].lower() if ': ' in x else x.lower()
+    if 'responder' in value:
+        return 1 if 'non' not in value else 0
+    return None
+
+def convert_age(x):
+    return None
+
+def convert_gender(x):
+    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_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 features
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+# These identifiers appear to be probe IDs (A_19_P format) and control probes
+# They are not standard human gene symbols and need to be mapped
+requires_gene_mapping = True
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file) 
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# Get mapping dataframe with probe IDs and gene symbols
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# Convert probe measurements to gene expression values
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Preview result 
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst 5 rows preview:")
+print(gene_data.head())
+# 1. Normalize gene symbols and save
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True, 
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True, 
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Study of depression in obese patients before and after bariatric surgery"
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Depression/code/GSE201332.py

@@ -0,0 +1,260 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Depression"
+cohort = "GSE201332"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Depression"
+in_cohort_dir = "../DATA/GEO/Depression/GSE201332"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Depression/GSE201332.csv"
+out_gene_data_file = "./output/preprocess/3/Depression/gene_data/GSE201332.csv"
+out_clinical_data_file = "./output/preprocess/3/Depression/clinical_data/GSE201332.csv"
+json_path = "./output/preprocess/3/Depression/cohort_info.json"
+
+# Get paths to the SOFT and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values for each feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Yes, this dataset contains transcriptional profiling data from whole blood samples
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+# Trait (Depression) data is in row 1 ("subject status")
+trait_row = 1
+# Age data is in row 3
+age_row = 3  
+# Gender data is in row 2
+gender_row = 2
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    """Convert MDD status to binary: 0 for control, 1 for MDD"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip().lower()
+    if 'mdd' in value or 'depression' in value:
+        return 1
+    elif 'healthy' in value or 'control' in value:
+        return 0
+    return None
+
+def convert_age(value):
+    """Convert age to continuous numeric value"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip().lower()
+    # Extract numeric value before 'y'
+    try:
+        age = int(value.replace('y',''))
+        return age
+    except:
+        return None
+
+def convert_gender(value):
+    """Convert gender to binary: 0 for female, 1 for male"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip().lower()
+    if 'female' in value:
+        return 0
+    elif 'male' in value:
+        return 1
+    return None
+
+# 3. Save Metadata
+# Trait data is available (trait_row is not None)
+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
+clinical_features = geo_select_clinical_features(clinical_data, 
+                                               trait=trait,
+                                               trait_row=trait_row,
+                                               convert_trait=convert_trait,
+                                               age_row=age_row,
+                                               convert_age=convert_age,
+                                               gender_row=gender_row,
+                                               convert_gender=convert_gender)
+
+# Preview the extracted features
+preview_dict = preview_df(clinical_features)
+print("\nPreview of clinical features:")
+print(preview_dict)
+
+# Save clinical features
+clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+# The gene identifiers are simple numeric indices, not human gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = pd.read_csv(soft_file, compression='gzip', delimiter='\t', skiprows=163, nrows=54675)
+
+# Filter out control probes and probes without gene info
+gene_metadata = gene_metadata[~gene_metadata['Name'].str.contains('Control|control|Corner', na=False)]
+gene_metadata = gene_metadata[~gene_metadata['Gene Symbol'].isna()]
+
+# Preview filtered annotation data
+print("DataFrame shape after filtering:", gene_metadata.shape)
+print("\nColumn names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# Extract gene annotation data from SOFT file
+def get_probe_gene_mapping(file_path):
+    rows = []
+    with gzip.open(file_path, 'rt') as f:
+        in_spot_section = False
+        for line in f:
+            line = line.strip()
+            
+            # Identify start of SPOT section which contains probe mappings
+            if line.startswith('!Platform_table_begin'):
+                in_spot_section = True
+                # Skip the header line
+                next(f)
+                continue
+            elif line.startswith('!Platform_table_end'):
+                in_spot_section = False
+                continue
+                
+            if in_spot_section and line:
+                fields = line.split('\t')
+                # Get probe ID and gene name
+                rows.append([fields[0], fields[2]])  # ID and GENE_NAME columns
+    
+    # Convert to DataFrame
+    gene_metadata = pd.DataFrame(rows, columns=['ID', 'Gene'])
+    # Filter out empty gene names and control probes
+    gene_metadata = gene_metadata[
+        (gene_metadata['Gene'].notna()) & 
+        (gene_metadata['Gene'] != '') &
+        (~gene_metadata['Gene'].str.contains('control|Control|Corner', na=False, regex=True))
+    ]
+    return gene_metadata
+
+# Extract and preview annotation data
+gene_metadata = get_probe_gene_mapping(soft_file)
+
+# Preview filtered annotation data
+print("DataFrame shape after filtering:", gene_metadata.shape)
+print("\nColumn names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# 1. Get gene annotation data from SOFT file using direct extraction
+def extract_platform_table(file_path):
+    platform_data = []
+    with gzip.open(file_path, 'rt') as f:
+        in_table = False
+        for line in f:
+            if line.startswith('!Platform_table_begin'):
+                headers = next(f).strip().split('\t')
+                in_table = True
+                continue
+            if line.startswith('!Platform_table_end'):
+                break
+            if in_table and line.strip():
+                platform_data.append(line.strip().split('\t'))
+    return pd.DataFrame(platform_data, columns=headers)
+
+# Extract gene metadata
+gene_metadata = extract_platform_table(soft_file)
+
+# Print column names
+print("Column names in gene_metadata:")
+print(gene_metadata.columns)
+print("\nPreview of gene metadata:")
+print(preview_df(gene_metadata))
+
+# 2. Get gene mapping dataframe
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL') 
+
+# 3. Convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Preview results
+print("\nGene expression data shape:", gene_data.shape)
+print("\nFirst few gene symbols:")
+print(gene_data.index[:10])
+print("\nPreview of gene expression values:")
+print(gene_data.head().iloc[:, :5])
+# 1. Get gene annotation data from SOFT file 
+gene_metadata = get_gene_annotation(soft_file)
+
+# Print available columns to identify correct names
+print("Available columns:", gene_metadata.columns)
+
+# 2. Get gene mapping dataframe (using correct column names from gene_metadata)
+mapping_df = get_gene_mapping(gene_metadata, prob_col='IDs', gene_col='Gene Symbols')
+
+# 3. Convert probe-level data to gene expression data 
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# 4. Normalize gene symbols and save
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 5. Link clinical and genetic data
+clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)  
+linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
+
+# 6. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 7. Check for biased features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 8. Final validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path, 
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="MDD vs healthy controls study"
+)
+
+# 9. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file) 
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))

p3/preprocess/Depression/code/GSE208668.py

@@ -0,0 +1,102 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Depression"
+cohort = "GSE208668"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Depression"
+in_cohort_dir = "../DATA/GEO/Depression/GSE208668"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Depression/GSE208668.csv"
+out_gene_data_file = "./output/preprocess/3/Depression/gene_data/GSE208668.csv"
+out_clinical_data_file = "./output/preprocess/3/Depression/clinical_data/GSE208668.csv"
+json_path = "./output/preprocess/3/Depression/cohort_info.json"
+
+# Get paths to the SOFT and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values for each feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# From the background info, this dataset contains transcriptome data from PBMCs
+# However, it mentions raw data was lost, so gene expression data is not available
+is_gene_available = False
+
+# 2.1 Data Availability
+# Depression trait can be inferred from "history of depression" field (key 9)
+trait_row = 9
+# Age is available in key 1
+age_row = 1  
+# Gender is available in key 2
+gender_row = 2
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    if not isinstance(x, str):
+        return None
+    x = x.lower().strip()
+    if 'history of depression:' not in x:
+        return None
+    value = x.split(':')[1].strip()
+    if value == 'yes':
+        return 1
+    elif value == 'no':
+        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(':')[1].strip())
+    except:
+        return None
+
+def convert_gender(x):
+    if not isinstance(x, str):
+        return None
+    if 'gender:' not in x:
+        return None
+    value = x.split(':')[1].strip().lower()
+    if value == 'female':
+        return 0
+    elif value == 'male':
+        return 1
+    return None
+
+# 3. Save Metadata
+validate_and_save_cohort_info(is_final=False, 
+                             cohort=cohort,
+                             info_path=json_path,
+                             is_gene_available=is_gene_available,
+                             is_trait_available=(trait_row is not None))
+
+# 4. Clinical Feature Extraction
+# Since trait_row is not None, extract clinical features
+clinical_df = geo_select_clinical_features(clinical_data, 
+                                         trait=trait,
+                                         trait_row=trait_row,
+                                         convert_trait=convert_trait,
+                                         age_row=age_row,
+                                         convert_age=convert_age, 
+                                         gender_row=gender_row,
+                                         convert_gender=convert_gender)
+
+# Preview and save clinical data
+print("Preview of clinical data:")
+print(preview_df(clinical_df))
+clinical_df.to_csv(out_clinical_data_file)

p3/preprocess/Depression/code/GSE273630.py

@@ -0,0 +1,111 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Depression"
+cohort = "GSE273630"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Depression"
+in_cohort_dir = "../DATA/GEO/Depression/GSE273630"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Depression/GSE273630.csv"
+out_gene_data_file = "./output/preprocess/3/Depression/gene_data/GSE273630.csv"
+out_clinical_data_file = "./output/preprocess/3/Depression/clinical_data/GSE273630.csv"
+json_path = "./output/preprocess/3/Depression/cohort_info.json"
+
+# Get paths to the SOFT and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values for each feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# Gene Expression Data Availability
+is_gene_available = True  # Based on background info mentioning "digital transcript panel" and "genes"
+
+# Trait, Age and Gender Data Availability 
+trait_row = None  # Depression data not available - this is a study on HIV and Methamphetamine use
+age_row = None  # Age is constant (35-44 years) based on background info
+gender_row = None  # Gender is constant (all males) based on background info
+
+# Convert functions (defined but not used since no data available)
+def convert_trait(x):
+    if x is None or pd.isna(x):
+        return None
+    val = str(x).split(':')[-1].strip().lower()
+    # Convert depression status to binary
+    if 'yes' in val or 'true' in val or 'positive' in val:
+        return 1
+    elif 'no' in val or 'false' in val or 'negative' in val:
+        return 0
+    return None
+
+def convert_age(x):
+    if x is None or pd.isna(x):  
+        return None
+    val = str(x).split(':')[-1].strip()
+    try:
+        return float(val)
+    except:
+        return None
+
+def convert_gender(x):
+    if x is None or pd.isna(x):
+        return None
+    val = str(x).split(':')[-1].strip().lower()
+    if 'female' in val or 'f' in val:
+        return 0
+    elif 'male' in val or 'm' in val:
+        return 1
+    return None
+
+# Save metadata - initial filtering
+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
+)
+# Extract gene expression data from matrix file
+genetic_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+# These appear to be standard human gene symbols (e.g. ABAT, ABL1, ACHE, etc.)
+# No mapping required - they are already in the correct format
+requires_gene_mapping = False
+# 1. Normalize gene symbols and save
+genetic_df = normalize_gene_symbols_in_index(genetic_df)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+genetic_df.to_csv(out_gene_data_file)
+
+# Since trait data is not available, we create a minimal dataframe for validation
+minimal_df = pd.DataFrame(index=genetic_df.columns)
+
+# Final validation - dataset not usable due to missing trait 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 since we can't use a dataset without trait data
+    df=minimal_df,
+    note="Dataset focuses on HIV and Methamphetamine use, depression data not available"
+)

p3/preprocess/Depression/code/GSE81761.py

@@ -0,0 +1,173 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Depression"
+cohort = "GSE81761"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Depression"
+in_cohort_dir = "../DATA/GEO/Depression/GSE81761"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Depression/GSE81761.csv"
+out_gene_data_file = "./output/preprocess/3/Depression/gene_data/GSE81761.csv"
+out_clinical_data_file = "./output/preprocess/3/Depression/clinical_data/GSE81761.csv"
+json_path = "./output/preprocess/3/Depression/cohort_info.json"
+
+# Get paths to the SOFT and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values for each feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Yes, this is a gene expression dataset using Affymetrix HG-U133_Plus_2 chip
+is_gene_available = True
+
+# 2.1 Data Availability
+# Trait (Depression) is not explicitly recorded but PTSD/Depression comorbidity is common in military
+# We can infer depression from PTSD subgroup which shows symptom severity changes
+trait_row = 2  
+
+# Age and gender data are available
+age_row = 5
+gender_row = 4
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    """Convert PTSD subgroup to depression severity (binary)"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(': ')[1].strip()
+    if value == 'PTSD Not Improved':
+        return 1  # Severe depression
+    elif value == 'PTSD Improved':
+        return 0  # Mild/no depression
+    elif value == 'No PTSD':
+        return 0  # No depression
+    return None  # No Follow Up Data cases
+
+def convert_age(value):
+    """Convert age string to integer"""
+    if not value or ':' not in value:
+        return None
+    try:
+        return int(value.split(': ')[1])
+    except:
+        return None
+
+def convert_gender(value):
+    """Convert gender to binary (0=female, 1=male)"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(': ')[1].lower()
+    if value == 'female':
+        return 0
+    elif value == '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=True  # trait_row is not None
+)
+
+# 4. Clinical Feature Extraction
+clinical_df = geo_select_clinical_features(
+    clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Preview the processed clinical data
+preview_result = preview_df(clinical_df)
+print("Preview of clinical data:")
+print(preview_result)
+
+# Save clinical data
+os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+# These IDs appear to be Affymetrix probe IDs (e.g. '1007_s_at', '1053_at')
+# Rather than human gene symbols, so they will need to be mapped
+requires_gene_mapping = True
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file) 
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# 1. Identify columns: 'ID' for probe IDs and 'Gene Symbol' for gene symbols
+prob_col = 'ID'
+gene_col = 'Gene Symbol'
+
+# 2. Get gene mapping dataframe from annotation data
+mapping_df = get_gene_mapping(gene_metadata, prob_col, gene_col)
+
+# 3. Apply gene mapping to convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Preview results
+print("Gene expression data shape:", gene_data.shape)
+print("\nFirst few rows and columns:")
+print(gene_data.head().iloc[:, :5])
+# 1. Normalize gene symbols and save
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True, 
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True, 
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Study of depression in obese patients before and after bariatric surgery"
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Depression/code/GSE99725.py

@@ -0,0 +1,146 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Depression"
+cohort = "GSE99725"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Depression"
+in_cohort_dir = "../DATA/GEO/Depression/GSE99725"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Depression/GSE99725.csv"
+out_gene_data_file = "./output/preprocess/3/Depression/gene_data/GSE99725.csv"
+out_clinical_data_file = "./output/preprocess/3/Depression/clinical_data/GSE99725.csv"
+json_path = "./output/preprocess/3/Depression/cohort_info.json"
+
+# Get paths to the SOFT and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values for each feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Dataset is about transcriptomic profiling from peripheral blood
+
+# 2.1 Data Row Numbers
+trait_row = 2  # MADRS (depression score)
+age_row = None  # Age not available
+gender_row = None  # Gender not available
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    """Convert MADRS score to binary depression status
+    A: No/mild depression (0)
+    B: Depression (1)"""
+    if not isinstance(x, str):
+        return None
+    value = x.split(': ')[-1]
+    if value == 'A':
+        return 0
+    elif value == 'B':
+        return 1
+    return None
+
+def convert_age(x):
+    return None  # Not used
+
+def convert_gender(x):
+    return None  # Not used
+
+# 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. Clinical Feature Extraction
+if trait_row is not None:
+    clinical_df = geo_select_clinical_features(clinical_df=clinical_data,
+                                             trait=trait,
+                                             trait_row=trait_row,
+                                             convert_trait=convert_trait)
+    print("Preview of clinical data:")
+    print(preview_df(clinical_df))
+    
+    # Save clinical data
+    clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+# Based on the presence of "A_19_P" in the identifiers, these are Agilent probe IDs 
+# that need to be mapped to human gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file) 
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# 1. Identify mapping columns
+# 'ID' column in gene_metadata contains the same Agilent probe IDs as in genetic_df
+# 'GENE_SYMBOL' column contains the target gene symbols
+prob_col = 'ID'
+gene_col = 'GENE_SYMBOL'
+
+# 2. Get gene mapping dataframe
+mapping_df = get_gene_mapping(gene_metadata, prob_col, gene_col)
+
+# 3. Apply gene mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Print shape and preview
+print("Gene expression data shape after mapping:", gene_data.shape)
+print("\nPreview of gene expression data:")
+print(gene_data.head().iloc[:, :5])
+# 1. Normalize gene symbols and save
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True, 
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True, 
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Study of depression in obese patients before and after bariatric surgery"
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Depression/code/TCGA.py

@@ -0,0 +1,191 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Depression"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Depression/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Depression/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Depression/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Depression/cohort_info.json"
+
+depression_related_terms = ["depression", "mental", "psychiatric", "psychological", "mood"]
+
+# Check if any cohort contains depression-related data
+found_relevant_data = False
+
+for cohort in cohorts:
+    cohort_dir = os.path.join(tcga_root_dir, cohort)
+    
+    try:
+        clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_dir)
+        clinical_df = pd.read_csv(clinical_file, sep='\t', index_col=0)
+        
+        # Check column names for relevant terms
+        relevant_cols = [col for col in clinical_df.columns 
+                        if any(term in col.lower() for term in depression_related_terms)]
+                        
+        if relevant_cols:
+            # Check if these columns actually contain meaningful data
+            non_null_counts = clinical_df[relevant_cols].count()
+            if (non_null_counts > 0).any():
+                print(f"\nFound depression-related data in {cohort}:")
+                print(f"Relevant columns: {relevant_cols}")
+                found_relevant_data = True
+                break
+            
+    except:
+        continue
+
+# Record result in metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=True,  # TCGA always has gene expression data
+    is_trait_available=found_relevant_data
+)
+# Define depression-related search terms
+depression_related_terms = ["depression", "mental", "psychiatric", "psychological", "mood", "affect"]
+
+# Initialize found_trait_data flag
+found_trait_data = False 
+
+# Get list of TCGA cohorts
+cohorts = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+
+# Search through cohorts for depression-related data
+for cohort in cohorts:
+    cohort_dir = os.path.join(tcga_root_dir, cohort)
+    
+    try:
+        # Get clinical and genetic file paths
+        clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_dir)
+        
+        # Load clinical data and check column names
+        clinical_df = pd.read_csv(clinical_file, sep='\t', index_col=0)
+        genetic_df = pd.read_csv(genetic_file, sep='\t', index_col=0)
+        
+        # Look for depression-related columns
+        relevant_cols = [col for col in clinical_df.columns 
+                        if any(term in col.lower() for term in depression_related_terms)]
+        
+        if relevant_cols:
+            # Check if columns contain non-null data
+            non_null_counts = clinical_df[relevant_cols].count()
+            if (non_null_counts > 0).any():
+                print(f"\nFound depression-related data in {cohort}:")
+                print(f"Relevant columns: {relevant_cols}")
+                print("\nClinical data columns:")
+                print(clinical_df.columns.tolist())
+                found_trait_data = True
+                break
+                
+    except Exception as e:
+        continue
+
+# Record results in metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=True,  # TCGA always has gene expression data
+    is_trait_available=found_trait_data
+)
+# Define candidate columns based on column names containing age/gender related keywords
+candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth', 'first_diagnosis_age_asth_ecz_hay_fev_mold_dust', 'first_diagnosis_age_of_animal_insect_allergy', 'first_diagnosis_age_of_food_allergy']
+candidate_gender_cols = ['gender']
+
+# Get clinical file path
+clinical_file_path, _ = tcga_get_relevant_filepaths(os.path.join(tcga_root_dir, 'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)'))
+
+# Read clinical data
+clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0)
+
+# Preview age columns
+age_preview = {}
+for col in candidate_age_cols:
+    age_preview[col] = clinical_df[col].head().tolist()
+print("Age columns preview:")
+print(age_preview)
+
+# Preview gender columns  
+gender_preview = {}
+for col in candidate_gender_cols:
+    gender_preview[col] = clinical_df[col].head().tolist()
+print("\nGender columns preview:")
+print(gender_preview)
+# Select age column by inspecting preview data
+# 'age_at_initial_pathologic_diagnosis' has valid age values
+age_col = 'age_at_initial_pathologic_diagnosis'
+
+# Select gender column by inspecting preview data
+# 'gender' contains valid gender values
+gender_col = 'gender'
+
+# Print chosen columns
+print(f"Selected age column: {age_col}")
+print(f"Selected gender column: {gender_col}")
+# Define demographic columns discovered in previous steps
+age_col = 'age_at_initial_pathologic_diagnosis'
+gender_col = 'gender'
+
+# Set up cohort directory and load data
+cohort_dir = os.path.join(tcga_root_dir, 'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)')
+clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_dir)
+clinical_df = pd.read_csv(clinical_file, sep='\t', index_col=0)
+genetic_df = pd.read_csv(genetic_file, sep='\t', index_col=0)
+
+# Extract clinical features using mental_status_changes as depression indicator
+clinical_features = tcga_select_clinical_features(
+    clinical_df, 
+    trait='mental_status_changes',  # Use mental status changes as depression indicator
+    age_col=age_col,
+    gender_col=gender_col
+)
+
+# Save processed clinical data
+os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+clinical_features.to_csv(out_clinical_data_file)
+
+# Normalize gene symbols in genetic data and save
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data = normalize_gene_symbols_in_index(genetic_df)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Link clinical and genetic data
+linked_data = pd.merge(
+    clinical_features,
+    normalized_gene_data.T,
+    left_index=True,
+    right_index=True,
+    how='inner'
+)
+
+# Handle missing values
+linked_data = handle_missing_values(linked_data, 'mental_status_changes')
+
+# Check if trait or demographic features are biased and remove biased demographics
+is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, 'mental_status_changes')
+
+# Final validation and save metadata
+notes = "Using TCGA glioma/glioblastoma (GBMLGG) data. Mental status changes used as depression indicator."
+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=is_trait_biased,
+    df=linked_data,
+    note=notes
+)
+
+# Save processed data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Depression/gene_data/GSE135524.csv

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+size 18896895

p3/preprocess/Depression/gene_data/GSE138297.csv

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p3/preprocess/Depression/gene_data/GSE149980.csv

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p3/preprocess/Depression/gene_data/GSE81761.csv

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p3/preprocess/Duchenne_Muscular_Dystrophy/GSE109178.csv

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p3/preprocess/Duchenne_Muscular_Dystrophy/GSE79263.csv

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p3/preprocess/Duchenne_Muscular_Dystrophy/clinical_data/GSE109178.csv

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p3/preprocess/Duchenne_Muscular_Dystrophy/clinical_data/GSE13608.csv

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+Duchenne_Muscular_Dystrophy,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.0,0.0,0.0,0.0
+Age,,,54.0,29.0,25.0,21.0,55.0,,39.0,58.0,50.0,51.0,43.0,51.0,37.0,43.0,65.0,55.0,50.0,45.0,26.0,20.0,58.0,88.0,61.0,43.0,85.0,43.0
+Gender,0.0,0.0,0.0,0.0,1.0,1.0,0.0,,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0

p3/preprocess/Duchenne_Muscular_Dystrophy/clinical_data/GSE79263.csv

@@ -0,0 +1,3 @@
+,GSM2090086,GSM2090087,GSM2090088,GSM2090089,GSM2090090,GSM2090091,GSM2090092,GSM2090093,GSM2090094,GSM2090095,GSM2090096,GSM2090097,GSM2090098,GSM2090099,GSM2090100,GSM2090101,GSM2090102,GSM2090103,GSM2090104,GSM2090105,GSM2090106,GSM2090107,GSM2090108,GSM2090109,GSM2090110,GSM2090111,GSM2090112,GSM2090113,GSM2090114,GSM2090115,GSM2090116,GSM2090117,GSM2090118,GSM2090119,GSM2090120,GSM2090121,GSM2090122,GSM2090123,GSM2090124,GSM2090125,GSM2090126,GSM2090127,GSM2090128,GSM2090129,GSM2090130,GSM2090131,GSM2090132,GSM2090133,GSM2090134,GSM2090135,GSM2090136,GSM2090137,GSM2090138,GSM2090139,GSM2090140,GSM2090141,GSM2090142,GSM2090143,GSM2090144,GSM2090145,GSM2090146,GSM2090147,GSM2090148,GSM2090149,GSM2090150,GSM2090151,GSM2090152,GSM2090153,GSM2090154,GSM2090155,GSM2090156,GSM2090157,GSM2090158,GSM2090159,GSM2090160,GSM2090161,GSM2090162,GSM2090163,GSM2090164,GSM2090165,GSM2090166,GSM2090167,GSM2090168,GSM2090169,GSM2090170,GSM2090171,GSM2090172,GSM2090173,GSM2090174,GSM2090175,GSM2090176,GSM2090177,GSM2090178,GSM2090179
+Duchenne_Muscular_Dystrophy,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+Age,80.0,78.0,,79.0,19.0,17.0,15.0,73.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,

p3/preprocess/Duchenne_Muscular_Dystrophy/code/GSE109178.py

@@ -0,0 +1,167 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Duchenne_Muscular_Dystrophy"
+cohort = "GSE109178"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Duchenne_Muscular_Dystrophy"
+in_cohort_dir = "../DATA/GEO/Duchenne_Muscular_Dystrophy/GSE109178"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Duchenne_Muscular_Dystrophy/GSE109178.csv"
+out_gene_data_file = "./output/preprocess/3/Duchenne_Muscular_Dystrophy/gene_data/GSE109178.csv"
+out_clinical_data_file = "./output/preprocess/3/Duchenne_Muscular_Dystrophy/clinical_data/GSE109178.csv"
+json_path = "./output/preprocess/3/Duchenne_Muscular_Dystrophy/cohort_info.json"
+
+# Get paths to the SOFT and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values for each feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# The background info mentions "mRNA profiles" and "HG-U133 Plus 2.0 microarrays"
+# which indicates this is gene expression data
+is_gene_available = True
+
+# 2.1 Data Availability
+# Trait can be inferred from the mutation data in key 4
+trait_row = 4
+
+# Age data is available in key 0
+age_row = 0 
+
+# Gender data is available in key 3
+gender_row = 3
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert mutation info to binary trait status (DMD vs non-DMD)"""
+    if pd.isna(value) or ":" not in value:
+        return None
+    value = value.split(":")[1].strip()
+    # Deletions/duplications/mutations indicate DMD
+    if any(x in value.lower() for x in ['deletion', 'duplication', 'mutation', 'exon']):
+        return 1
+    # Pathology notes indicate non-DMD
+    return 0
+
+def convert_age(value: str) -> float:
+    """Convert age string to float value"""
+    if pd.isna(value) or ":" not in value:
+        return None
+    value = value.split(":")[1].strip()
+    if value == "NA":
+        return None
+    try:
+        return float(value)
+    except:
+        return None
+
+def convert_gender(value: str) -> int:
+    """Convert gender string to binary (0=female, 1=male)"""
+    if pd.isna(value) or ":" not in value:
+        return None
+    value = value.split(":")[1].strip().upper()
+    if value in ["M", "MALE"]:
+        return 1
+    elif value in ["F", "FEMALE"]:
+        return 0
+    return None
+
+# 3. Save metadata 
+# Trait data is available (trait_row is not None)
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(False, cohort, json_path, is_gene_available, is_trait_available)
+
+# 4. Extract clinical features
+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
+)
+
+print("Preview of extracted clinical features:")
+print(preview_df(selected_clinical_df))
+
+# Save clinical data
+os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+selected_clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+# Based on the format like '1007_s_at', these appear to be Affymetrix probe IDs
+# rather than standard human gene symbols. They need to be mapped to gene symbols.
+requires_gene_mapping = True
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file) 
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# From the preview, we can see that 'ID' column matches the gene expression data identifiers (e.g. '1007_s_at')
+# and 'Gene Symbol' column contains the human gene symbols
+mapping_df = get_gene_mapping(gene_metadata, 'ID', 'Gene Symbol')
+
+# Apply gene mapping to convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Print size of mapped data
+print("Gene expression data shape after mapping:", gene_data.shape)
+print("\nFirst few rows and columns:")
+print(gene_data.head().iloc[:, :5])
+# 1. Normalize gene symbols and save
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True, 
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True, 
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Study comparing gene expression in healthy vs DMD myoblasts and myotubes, including immortalized cell lines"
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Duchenne_Muscular_Dystrophy/code/GSE13608.py

@@ -0,0 +1,167 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Duchenne_Muscular_Dystrophy"
+cohort = "GSE13608"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Duchenne_Muscular_Dystrophy"
+in_cohort_dir = "../DATA/GEO/Duchenne_Muscular_Dystrophy/GSE13608"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Duchenne_Muscular_Dystrophy/GSE13608.csv"
+out_gene_data_file = "./output/preprocess/3/Duchenne_Muscular_Dystrophy/gene_data/GSE13608.csv"
+out_clinical_data_file = "./output/preprocess/3/Duchenne_Muscular_Dystrophy/clinical_data/GSE13608.csv"
+json_path = "./output/preprocess/3/Duchenne_Muscular_Dystrophy/cohort_info.json"
+
+# Get paths to the SOFT and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values for each feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Dataset contains muscle biopsy gene expression data
+
+# 2.1 Data Availability
+trait_row = 1  # Disease status in row 1
+age_row = 2    # Age in row 2
+gender_row = 3 # Gender in row 3
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    if not isinstance(value, str):
+        return None
+    # Extract value after colon if present
+    if ':' in value:
+        value = value.split(':')[1].strip()
+    if 'Duchenne Muscular Dystrophy' in value:
+        return 1
+    elif 'Normal' in value:
+        return 0
+    return None
+
+def convert_age(value):
+    if not isinstance(value, str):
+        return None
+    if ':' in value:
+        value = value.split(':')[1].strip()
+    if 'age' in value:
+        try:
+            age = int(value.replace('age', '').strip())
+            return age
+        except:
+            return None
+    return None
+
+def convert_gender(value):
+    if not isinstance(value, str):
+        return None
+    if ':' in value:
+        value = value.split(':')[1].strip()
+    if 'F' == value.strip():
+        return 0
+    elif 'M' == value.strip():
+        return 1
+    return None
+
+# 3. Save Metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=trait_row is not None
+)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the 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
+genetic_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+# The identifiers in this dataset appear to be probe IDs from Affymetrix array
+# These need to be mapped to standard gene symbols for analysis
+requires_gene_mapping = True
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file) 
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# The gene expression data uses probe IDs stored in the 'ID' column of gene_metadata
+# The gene symbols are stored in the 'Gene Symbol' column
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')
+
+# Convert probe-level measurements to gene-level expression
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Preview the mapped gene expression data
+print("Gene expression data shape after mapping:", gene_data.shape)
+print("\nFirst few rows and columns:")
+print(gene_data.head().iloc[:, :5])
+# 1. Normalize gene symbols and save
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True, 
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True, 
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Study comparing gene expression in healthy vs DMD myoblasts and myotubes, including immortalized cell lines"
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Duchenne_Muscular_Dystrophy/code/GSE48828.py

@@ -0,0 +1,226 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Duchenne_Muscular_Dystrophy"
+cohort = "GSE48828"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Duchenne_Muscular_Dystrophy"
+in_cohort_dir = "../DATA/GEO/Duchenne_Muscular_Dystrophy/GSE48828"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Duchenne_Muscular_Dystrophy/GSE48828.csv"
+out_gene_data_file = "./output/preprocess/3/Duchenne_Muscular_Dystrophy/gene_data/GSE48828.csv"
+out_clinical_data_file = "./output/preprocess/3/Duchenne_Muscular_Dystrophy/clinical_data/GSE48828.csv"
+json_path = "./output/preprocess/3/Duchenne_Muscular_Dystrophy/cohort_info.json"
+
+# Get paths to the SOFT and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values for each feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Based on background info, this is an Affymetrix exon array study measuring gene expression
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Row identifiers for each variable
+trait_row = 0  # 'diagnosis' row contains trait info
+age_row = 2    # 'age (yrs)' row contains age info 
+gender_row = 1 # 'gender' row contains gender info
+
+# 2.2 Conversion functions
+def convert_trait(value: str) -> Optional[int]:
+    """Convert trait status to binary"""
+    if not value or ':' not in value:
+        return None
+    diagnosis = value.split(': ')[1].strip().lower()
+    if 'duchenne muscular dystrophy' in diagnosis:
+        return 1
+    elif 'normal' in diagnosis:
+        return 0
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    """Convert age to float"""
+    if not value or ':' not in value:
+        return None
+    age = value.split(': ')[1].strip().lower()
+    try:
+        if age in ['na', 'not available']:
+            return None
+        return float(age)
+    except:
+        return None
+
+def convert_gender(value: str) -> Optional[int]:
+    """Convert gender to binary"""
+    if not value or ':' not in value:
+        return None
+    gender = value.split(': ')[1].strip().lower()
+    if gender == 'f':
+        return 0
+    elif gender == '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. 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 processed clinical data
+    print("Preview of processed clinical data:")
+    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
+genetic_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+# The row IDs are numerical probe IDs from microarray platforms, not human gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file) 
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# 1. Identify columns for gene identifiers and symbols
+# 'ID' column contains same identifiers as gene expression data
+# 'gene_assignment' contains gene symbols but needs parsing
+
+# Function to parse gene symbols from complex strings
+def parse_gene_symbols(text):
+    if text == '---' or pd.isna(text):
+        return None
+    # Split by /// to handle multiple assignments
+    gene_entries = text.split('///')
+    symbols = []
+    for entry in gene_entries:
+        parts = entry.strip().split('//')
+        if len(parts) >= 3:  # We need at least 3 parts to get to the gene symbol
+            symbol = parts[1].strip()  # Gene symbol is in the second position
+            if symbol != '---':
+                symbols.append(symbol)
+    return symbols if symbols else None
+
+# Create initial mapping dataframe
+mapping_df = gene_metadata[['ID', 'gene_assignment']].copy()
+
+# Extract gene symbols and clean up mapping
+mapping_df['Gene'] = mapping_df['gene_assignment'].apply(parse_gene_symbols)
+mapping_df = mapping_df[['ID', 'Gene']].dropna(subset=['Gene'])
+
+# Explode lists of genes into separate rows
+mapping_df = mapping_df.explode('Gene')
+
+# Apply gene mapping to probe-level measurements
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Normalize gene symbols to standard form
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Print shape and preview mapped data
+print("Gene expression data shape after mapping:", gene_data.shape)
+print("\nPreview of gene expression data:")
+print(gene_data.head().iloc[:, :5])
+# 1. Since gene_data is empty, we need to remap gene symbols
+def parse_gene_symbols(text):
+    if text == '---' or pd.isna(text):
+        return None
+    # Split entries by /// for multiple assignments
+    entries = text.split('///')
+    symbols = []
+    for entry in entries:
+        parts = [p.strip() for p in entry.split('//')]
+        if len(parts) >= 2:  # Need at least 2 parts
+            symbol = parts[1]  # Second part contains the gene symbol
+            if symbol != '---':
+                symbols.append(symbol)
+    return symbols if symbols else None
+
+# Create initial mapping dataframe
+mapping_df = gene_metadata[['ID', 'gene_assignment']].copy()
+
+# Extract gene symbols and clean up mapping
+mapping_df['Gene'] = mapping_df['gene_assignment'].apply(parse_gene_symbols)
+mapping_df = mapping_df[['ID', 'Gene']].dropna(subset=['Gene'])
+
+# Explode lists of genes into separate rows
+mapping_df = mapping_df.explode('Gene')
+print(f"Number of probe-gene mappings: {len(mapping_df)}")
+
+# Apply gene mapping to probe-level measurements
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+print(f"Number of genes after mapping: {len(gene_data)}")
+
+# After remapping, proceed with the rest of step 7
+# 1. Normalize gene symbols and save
+gene_data = normalize_gene_symbols_in_index(gene_data)
+print(f"Number of genes after normalization: {len(gene_data)}")
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True, 
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True, 
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Study comparing gene expression in healthy vs DMD myoblasts and myotubes, including immortalized cell lines"
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Duchenne_Muscular_Dystrophy/code/GSE79263.py

@@ -0,0 +1,167 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Duchenne_Muscular_Dystrophy"
+cohort = "GSE79263"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Duchenne_Muscular_Dystrophy"
+in_cohort_dir = "../DATA/GEO/Duchenne_Muscular_Dystrophy/GSE79263"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Duchenne_Muscular_Dystrophy/GSE79263.csv"
+out_gene_data_file = "./output/preprocess/3/Duchenne_Muscular_Dystrophy/gene_data/GSE79263.csv"
+out_clinical_data_file = "./output/preprocess/3/Duchenne_Muscular_Dystrophy/clinical_data/GSE79263.csv"
+json_path = "./output/preprocess/3/Duchenne_Muscular_Dystrophy/cohort_info.json"
+
+# Get paths to the SOFT and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values for each feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Based on background info indicating RNA extraction and gene expression profiling
+is_gene_available = True
+
+# 2.1 Data Availability and Row Identification
+# Trait (DMD status) found in row 2 "disease state"
+trait_row = 2
+
+# Age found in row 4
+age_row = 4
+
+# Gender data not available in sample characteristics
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert DMD status to binary"""
+    if pd.isna(value):
+        return None
+    value = value.split(": ")[-1].lower()
+    if "duchenne" in value or "dmd" in value:
+        return 1
+    elif "healthy" in value:
+        return 0
+    return None
+
+def convert_age(value: str) -> float:
+    """Convert age to continuous numeric value"""
+    if pd.isna(value):
+        return None
+    value = value.split(": ")[-1].lower()
+    if "unknown" in value:
+        return None
+    try:
+        # Extract numeric value before 'y'
+        age = float(value.replace('y',''))
+        return age
+    except:
+        return None
+
+def convert_gender(value: str) -> int:
+    """Placeholder function - not used since gender data unavailable"""
+    return None
+
+# 3. Save Metadata 
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=trait_row is not None
+)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    selected_clinical = 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 selected features
+    print("Preview of selected clinical features:")
+    print(preview_df(selected_clinical))
+    
+    # Save to CSV
+    selected_clinical.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+# These IDs start with "ILMN_" which indicates they are Illumina probe IDs, not gene symbols
+# Therefore they need to be mapped to human gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file) 
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# 1. Get gene mapping using ID and Symbol columns from annotation
+# ID column contains ILMN probe IDs matching gene expression data
+# Symbol column contains human gene symbols
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Symbol')
+
+# 2. Apply gene mapping to convert probe-level expression to gene-level expression
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Print gene_data shape and preview
+print("\nGene expression data shape after mapping:", gene_data.shape)
+print("\nPreview of gene expression data:")
+print(gene_data.head().iloc[:, :5])
+# 1. Normalize gene symbols and save
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True, 
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True, 
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Study comparing gene expression in healthy vs DMD myoblasts and myotubes, including immortalized cell lines"
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Duchenne_Muscular_Dystrophy/code/TCGA.py

@@ -0,0 +1,24 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Duchenne_Muscular_Dystrophy"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Duchenne_Muscular_Dystrophy/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Duchenne_Muscular_Dystrophy/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Duchenne_Muscular_Dystrophy/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Duchenne_Muscular_Dystrophy/cohort_info.json"
+
+# Since DMD is a genetic disorder and not a cancer type, TCGA datasets are not suitable
+# Record that trait data is not available and skip further processing
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort="TCGA",
+    info_path=json_path, 
+    is_gene_available=True,  # TCGA would have gene data if we needed it
+    is_trait_available=False  # No relevant trait data for DMD in TCGA cancer datasets
+)

p3/preprocess/Duchenne_Muscular_Dystrophy/cohort_info.json

@@ -0,0 +1 @@
+{"GSE79263": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": false, "sample_size": 86, "note": "Study comparing gene expression in healthy vs DMD myoblasts and myotubes, including immortalized cell lines"}, "GSE48828": {"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": "Study comparing gene expression in healthy vs DMD myoblasts and myotubes, including immortalized cell lines"}, "GSE13608": {"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": 12, "note": "Study comparing gene expression in healthy vs DMD myoblasts and myotubes, including immortalized cell lines"}, "GSE109178": {"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": 49, "note": "Study comparing gene expression in healthy vs DMD myoblasts and myotubes, including immortalized cell lines"}, "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}}