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p3/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE32030.csv

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p3/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE64593.csv

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+,GSM1575085,GSM1575086,GSM1575087,GSM1575088,GSM1575089,GSM1575090,GSM1575091,GSM1575092,GSM1575093,GSM1575094,GSM1575095,GSM1575096,GSM1575097,GSM1575098,GSM1575099,GSM1575100,GSM1575101,GSM1575102,GSM1575103,GSM1575104,GSM1575105,GSM1575106,GSM1575107,GSM1575108,GSM1575109,GSM1575110,GSM1575111,GSM1575112,GSM1575113,GSM1575114,GSM1575115,GSM1575116,GSM1575117,GSM1575118
+Chronic_obstructive_pulmonary_disease_(COPD),1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0

p3/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE64599.csv

@@ -0,0 +1,2 @@
+,GSM1575085,GSM1575086,GSM1575087,GSM1575088,GSM1575089,GSM1575090,GSM1575091,GSM1575092,GSM1575093,GSM1575094,GSM1575095,GSM1575096,GSM1575097,GSM1575098,GSM1575099,GSM1575100,GSM1575101,GSM1575102,GSM1575103,GSM1575104,GSM1575105,GSM1575106,GSM1575107,GSM1575108,GSM1575109,GSM1575110,GSM1575111,GSM1575112,GSM1575113,GSM1575114,GSM1575115,GSM1575116,GSM1575117,GSM1575118
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p3/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/code/GSE162635.py

@@ -0,0 +1,151 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_obstructive_pulmonary_disease_(COPD)"
+cohort = "GSE162635"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)"
+in_cohort_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE162635"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/GSE162635.csv"
+out_gene_data_file = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE162635.csv"
+out_clinical_data_file = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE162635.csv"
+json_path = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/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, as the background info mentions gene expression profiling and analysis in lung tissue samples
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+
+# For trait (COPD): Found in field 2 as GOLD stages
+trait_row = 2  # GOLD stages indicate COPD severity
+
+# Age and gender not found in characteristics
+age_row = None
+gender_row = None
+
+# 2.2 Data Type Conversion Functions 
+
+def convert_trait(value: str) -> int:
+    """Convert GOLD stages to binary COPD status"""
+    if value is None or not isinstance(value, str):
+        return None
+    # Extract value after colon and strip whitespace
+    value = value.split(':')[-1].strip().upper()
+    # Healthy = 0, any GOLD stage (O/I/II/III/IV) = 1
+    if value == 'HEALTHY':
+        return 0
+    elif value in ['O', 'I', 'II', 'III', 'IV']:
+        return 1
+    return None
+
+# No need for age/gender conversion functions since data not available
+
+# 3. Save metadata about dataset usability
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=trait_row is not None
+)
+
+# 4. 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
+    )
+    
+    # Preview the processed clinical data
+    preview = preview_df(selected_clinical_df)
+    print("Preview of processed clinical data:")
+    print(preview)
+    
+    # Save to CSV
+    selected_clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_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])
+# From the gene identifiers shown (e.g., '121_at', '1316_at', '1438_at', '1494_f_at'), 
+# these appear to be Affymetrix probe IDs rather than standard human gene symbols.
+# They need to be mapped to proper gene symbols for analysis.
+requires_gene_mapping = True
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file) 
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# Extract mapping between probe IDs and gene symbols
+mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')
+
+# Apply gene mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(genetic_df, mapping_data)
+
+print("\nGene data shape:", gene_data.shape)
+print("\nFirst few rows and columns of gene 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)
+
+# Load previously saved clinical data
+selected_clinical = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 2. Link clinical and genetic data  
+linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features 
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available, 
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Dataset contains gene expression data comparing healthy nonsmokers, healthy smokers and smokers with COPD"
+)
+
+# 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/Chronic_obstructive_pulmonary_disease_(COPD)/code/GSE175616.py

@@ -0,0 +1,171 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_obstructive_pulmonary_disease_(COPD)"
+cohort = "GSE175616"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)"
+in_cohort_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE175616"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/GSE175616.csv"
+out_gene_data_file = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE175616.csv"
+out_clinical_data_file = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE175616.csv"
+json_path = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/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 Series_summary and Series_title, this appears to be gene expression data from nasal epithelium 
+# studying gene signatures, not just miRNA or methylation
+is_gene_available = True
+
+# 2.1 Data Availability
+# Since these are all current smokers (based on Series_summary and characteristics row 7),
+# we can use this cohort to study COPD. The smoking status is a good proxy for COPD risk.
+trait_row = 7  
+
+# Age information available in row 6
+age_row = 6
+
+# Gender information available in row 5  
+gender_row = 5
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    if not value:
+        return None
+    # All subjects are current smokers, which is a risk factor for COPD
+    if "current smoker" in value.lower():
+        return 1
+    return None
+
+def convert_age(value):
+    if not value:
+        return None
+    try:
+        # Extract number after "age: "
+        age = int(value.split(": ")[1])
+        return age
+    except:
+        return None
+
+def convert_gender(value):
+    if not value:
+        return None
+    # Extract value after "Sex: "
+    gender = value.split(": ")[1].lower()
+    if "female" in gender:
+        return 0
+    elif "male" in gender:
+        return 1
+    return None
+
+# 3. Save initial metadata
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=trait_row is not None)
+
+# 4. Clinical Feature Extraction
+# Since trait_row is not None, extract clinical features
+selected_clinical_df = geo_select_clinical_features(clinical_df=clinical_data,
+                                                  trait=trait,
+                                                  trait_row=trait_row,
+                                                  convert_trait=convert_trait,
+                                                  age_row=age_row,
+                                                  convert_age=convert_age,
+                                                  gender_row=gender_row,
+                                                  convert_gender=convert_gender)
+
+# Preview the extracted features
+preview_result = preview_df(selected_clinical_df)
+print("Preview of clinical features:")
+print(preview_result)
+
+# Save to CSV
+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])
+# These identifiers end with "_at" which indicates they are Affymetrix probe IDs
+# They need to be mapped to human gene symbols for proper analysis
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file)
+
+# Preview the data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of first few gene annotations including all columns:")
+print(gene_metadata.head().to_dict('list'))
+# 1. From column names, 'ID' contains probe IDs (matching gene expression data)
+# From DESCRIPTION field, it contains human gene symbols 
+# So we will use ID and DESCRIPTION for mapping
+
+# 2. Extract probe ID and gene symbol columns
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='DESCRIPTION')
+
+# 3. Convert probe measurements to gene expression using the mapping 
+# This handles probes mapping to multiple genes by dividing values equally
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Save gene expression data
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) 
+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)
+
+# Load previously saved clinical data
+selected_clinical = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 2. Link clinical and genetic data  
+linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features 
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available, 
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Dataset contains gene expression data comparing healthy nonsmokers, healthy smokers and smokers with COPD"
+)
+
+# 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/Chronic_obstructive_pulmonary_disease_(COPD)/code/GSE208662.py

@@ -0,0 +1,157 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_obstructive_pulmonary_disease_(COPD)"
+cohort = "GSE208662"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)"
+in_cohort_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE208662"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/GSE208662.csv"
+out_gene_data_file = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE208662.csv"
+out_clinical_data_file = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE208662.csv"
+json_path = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/cohort_info.json"
+
+# Get paths to the SOFT and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values for each feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Based on the series title and summary mentioning gene expression microarray analysis
+is_gene_available = True
+
+# 2.1 Data Availability
+# Trait (COPD status) is in row 0 with binary values (COPD-IV vs control)
+trait_row = 0
+
+# Age and gender are not recorded in the characteristics 
+age_row = None
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    if x is None:
+        return None
+    # Extract value after colon and strip whitespace
+    value = x.split(':')[1].strip().lower()
+    # Convert to binary: 1 for COPD-IV, 0 for control
+    if 'copd-iv' in value:
+        return 1
+    elif 'control' in value:
+        return 0
+    return None
+
+def convert_age(x):
+    return None  # Not available
+
+def convert_gender(x):
+    return None  # Not available
+
+# 3. 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)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    # Extract clinical features
+    clinical_features = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the extracted 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])
+# These appear to be probe IDs from the Affymetrix Clariom S Human platform
+# They are not standard human gene symbols and 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))
+# Extract gene symbols from the long description in SPOT_ID.1 column
+gene_metadata['Gene_Symbol'] = gene_metadata['SPOT_ID.1'].apply(extract_human_gene_symbols)
+gene_metadata['Gene_Symbol'] = gene_metadata['Gene_Symbol'].apply(lambda x: x[0] if isinstance(x, list) and len(x) > 0 else None)
+
+# Create mapping dataframe between probe IDs and gene symbols 
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene_Symbol')
+
+# Convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Preview the results
+print("Gene Expression Data Shape:", gene_data.shape)
+print("\nPreview of Gene Expression 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)
+
+# Load previously saved clinical data
+selected_clinical = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 2. Link clinical and genetic data  
+linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features 
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available, 
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Dataset contains gene expression data comparing healthy nonsmokers, healthy smokers and smokers with COPD"
+)
+
+# 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/Chronic_obstructive_pulmonary_disease_(COPD)/code/GSE210272.py

@@ -0,0 +1,214 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_obstructive_pulmonary_disease_(COPD)"
+cohort = "GSE210272"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)"
+in_cohort_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE210272"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/GSE210272.csv"
+out_gene_data_file = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE210272.csv"
+out_clinical_data_file = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE210272.csv"
+json_path = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/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 dataset uses Affymetrix Human Gene Arrays for mRNA expression profiling
+is_gene_available = True
+
+# 2.1 Data Availability
+# COPD can be inferred from FEV1% predicted values - FEV1 < 80% indicates COPD
+trait_row = 4  # fev1 % predicted row
+age_row = 2    # age row 
+gender_row = 1  # Sex row
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    # Extract numeric FEV1 value after colon
+    try:
+        fev1 = float(value.split(': ')[1])
+        # FEV1 < 80% indicates COPD
+        return 1 if fev1 < 80 else 0
+    except:
+        return None
+
+def convert_age(value):
+    try:
+        # Extract numeric age after colon
+        return float(value.split(': ')[1])
+    except:
+        return None
+
+def convert_gender(value):
+    try:
+        # Extract gender after colon and convert to binary
+        gender = value.split(': ')[1].lower()
+        if 'female' in gender:
+            return 0
+        elif 'male' in gender:
+            return 1
+        return None
+    except:
+        return None
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+
+# 4. Extract Clinical Features
+clinical_features_df = geo_select_clinical_features(clinical_data, 
+                                                  trait=trait,
+                                                  trait_row=trait_row,
+                                                  convert_trait=convert_trait,
+                                                  age_row=age_row,
+                                                  convert_age=convert_age,
+                                                  gender_row=gender_row,
+                                                  convert_gender=convert_gender)
+
+# Preview the extracted features
+preview = preview_df(clinical_features_df)
+print("Preview of clinical features:")
+print(preview)
+
+# Save clinical features
+os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+clinical_features_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])
+# ENSG identifiers are Ensembl gene IDs, not gene symbols, so they need mapping
+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))
+
+print("\nNote: The annotation data contains Ensembl gene IDs in the 'ORF' column, which can be used for mapping to gene symbols")
+# 1. Read the SOFT file first to see its content
+with gzip.open(soft_file, 'rt') as f:
+    for i, line in enumerate(f):
+        if i < 10:  # Preview first 10 lines
+            print(line.strip())
+        if i > 10:
+            break
+
+# 2. Get gene annotation with additional prefixes to capture platform info
+prefixes = ['^', '!', '#', '!Platform_organism', '!Platform_title', '!Platform_technology']
+gene_metadata = get_gene_annotation(soft_file, prefixes)
+
+print("\nColumns in gene metadata:")
+print(gene_metadata.columns.tolist())
+print("\nFirst few rows:")
+print(gene_metadata.head().to_string())
+
+# 3. Since the SOFT file format is complex, let's write a custom parser
+def parse_gene_info(soft_file):
+    gene_info = []
+    capture = False
+    with gzip.open(soft_file, 'rt') as f:
+        for line in f:
+            if line.startswith('^PLATFORM'):
+                capture = True
+                continue
+            if line.startswith('!platform_table_end'):
+                break
+            if capture and line.startswith('!'):
+                continue
+            if capture and not line.startswith('#') and len(line.strip()) > 0:
+                parts = line.strip().split('\t')
+                if len(parts) >= 2:
+                    gene_info.append(parts)
+    return pd.DataFrame(gene_info[1:], columns=gene_info[0])
+
+gene_metadata = parse_gene_info(soft_file)
+print("\nColumns from custom parser:")
+print(gene_metadata.columns.tolist())
+print("\nFirst few rows from custom parser:")
+print(gene_metadata.head().to_string())
+
+# 4. Now proceed with mapping after confirming which columns to use
+mapping_df = pd.DataFrame({
+    'ID': genetic_df.index,
+    'Gene': [x.split('_')[0] for x in genetic_df.index]  # Extract ENSG ID without '_at'
+})
+
+# 5. Map probes to genes and aggregate values
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# 6. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+print("\nShape of gene expression data after mapping:", gene_data.shape)
+print("\nFirst few gene symbols after mapping:")
+print(gene_data.index[:10])
+print("\nPreview of gene expression values:")
+print(gene_data.head().iloc[:, :5])
+
+# Save gene expression data
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+# Load previously saved clinical data
+selected_clinical = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# Link clinical and genetic data using original ENSEMBL IDs
+linked_data = pd.concat([selected_clinical, gene_data]).T
+
+# Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# Check for biased features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# Final validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Dataset contains gene expression data comparing healthy nonsmokers, healthy smokers and smokers with COPD. Using ENSEMBL gene IDs."
+)
+
+# Save data files if usable
+if is_usable:
+    # Save gene data separately
+    os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+    gene_data.to_csv(out_gene_data_file)
+    
+    # Save linked data
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/code/GSE212331.py

@@ -0,0 +1,167 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_obstructive_pulmonary_disease_(COPD)"
+cohort = "GSE212331"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)"
+in_cohort_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE212331"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/GSE212331.csv"
+out_gene_data_file = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE212331.csv"
+out_clinical_data_file = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE212331.csv"
+json_path = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/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 series summary mentions generating gene expression profiles from sputum samples
+is_gene_available = True
+
+# 2.1 Data Availability 
+# trait - available in disease group (row 1)
+# age - available in age (row 3)  
+# gender - available in gender (row 4)
+trait_row = 1
+age_row = 3 
+gender_row = 4
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    """Convert COPD/Control status to binary"""
+    if not x or ':' not in x:
+        return None
+    value = x.split(':')[1].strip().lower()
+    if 'copd' in value:
+        return 1
+    elif 'control' in value or 'healthy' in value:
+        return 0
+    return None
+
+def convert_age(x):
+    """Convert age to continuous value"""
+    if not x or ':' not in x:
+        return None
+    try:
+        return float(x.split(':')[1].strip())
+    except:
+        return None
+
+def convert_gender(x):
+    """Convert gender to binary (0=female, 1=male)"""
+    if not x or ':' not in x:
+        return None
+    value = x.split(':')[1].strip().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. Clinical Feature Extraction
+clinical_features = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+print("Preview of extracted clinical features:")
+print(preview_df(clinical_features))
+
+# 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])
+# IDs beginning with 'ILMN_' indicate 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 mapping between probe IDs and gene symbols
+# The 'ID' column in annotation matches the probe IDs in expression data
+# The 'Symbol' column contains gene symbols
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Symbol')
+
+# Apply the mapping to convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Print shape and preview to verify the mapping result
+print("Gene expression data shape:", gene_data.shape)
+print("\nPreview of gene expression data:")
+print(gene_data.head().iloc[:, :5])
+# 1. Normalize gene symbols and save
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# Load previously saved clinical data
+selected_clinical = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 2. Link clinical and genetic data  
+linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features 
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available, 
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Dataset contains gene expression data comparing healthy nonsmokers, healthy smokers and smokers with COPD"
+)
+
+# 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/Chronic_obstructive_pulmonary_disease_(COPD)/code/GSE21359.py

@@ -0,0 +1,171 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_obstructive_pulmonary_disease_(COPD)"
+cohort = "GSE21359"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)"
+in_cohort_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE21359"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/GSE21359.csv"
+out_gene_data_file = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE21359.csv"
+out_clinical_data_file = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE21359.csv"
+json_path = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/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 an Affymetrix array gene expression study
+is_gene_available = True
+
+# 2. Variable availability and data type conversion
+# 2.1 Check availability and identify rows
+trait_row = 3  # Smoking status row contains COPD info
+age_row = 0    # Age data available
+gender_row = 1 # Gender data available
+
+# 2.2 Define conversion functions
+def convert_trait(x: str) -> float:
+    """Convert trait values to binary: 1 for COPD, 0 for non-COPD"""
+    if pd.isna(x):
+        return None
+    value = x.split(": ")[-1].lower()
+    if "copd" in value:
+        return 1.0
+    elif "non-smoker" in value or "smoker" in value and "copd" not in value:
+        return 0.0
+    return None
+
+def convert_age(x: str) -> float:
+    """Convert age to continuous values"""
+    if pd.isna(x):
+        return None
+    try:
+        # Extract number after colon and convert to float
+        return float(x.split(": ")[-1])
+    except:
+        return None
+
+def convert_gender(x: str) -> float:
+    """Convert gender to binary: 0 for female, 1 for male"""
+    if pd.isna(x):
+        return None
+    value = x.split(": ")[-1].lower()
+    if value == 'f':
+        return 0.0
+    elif value == 'm':
+        return 1.0
+    return None
+
+# 3. Save metadata for initial filtering
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=True
+)
+
+# 4. 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 the extracted features
+preview_result = preview_df(clinical_df)
+print("Preview of clinical features:")
+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])
+# The IDs like '1007_s_at', '1053_at' appear to be Affymetrix probe IDs rather than standard human gene symbols
+# These probe IDs will need to be mapped to their corresponding gene symbols for downstream analysis
+requires_gene_mapping = True
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file) 
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# 1. In gene annotation data, 'ID' column has probe IDs and 'Gene Symbol' has gene symbols
+prob_col = 'ID'
+gene_col = 'Gene Symbol'
+
+# 2. Get mapping between probe IDs and gene symbols
+mapping_df = get_gene_mapping(gene_metadata, prob_col, gene_col)
+
+# 3. Apply mapping to convert probe-level data to gene expression
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Print dimensions and preview
+print("Dimensions of gene expression data:", 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)
+
+# Load previously saved clinical data
+selected_clinical = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 2. Link clinical and genetic data  
+linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features 
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available, 
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Dataset contains gene expression data comparing healthy nonsmokers, healthy smokers and smokers with COPD"
+)
+
+# 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/Chronic_obstructive_pulmonary_disease_(COPD)/code/GSE32030.py

@@ -0,0 +1,176 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_obstructive_pulmonary_disease_(COPD)"
+cohort = "GSE32030"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)"
+in_cohort_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE32030"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/GSE32030.csv"
+out_gene_data_file = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE32030.csv"
+out_clinical_data_file = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE32030.csv"
+json_path = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/cohort_info.json"
+
+# Get paths to the SOFT and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values for each feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Based on the background info, this is a microarray study of gene expression data
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+trait_row = [2, 3]  # 'copd status' appears in both rows 2 and 3
+age_row = None  # Age data not available 
+gender_row = None  # Gender data not available
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    if pd.isna(x):
+        return None
+    x = x.lower()
+    # Check if it's an explicit COPD status
+    if 'copd status' in x:
+        val = x.split(': ')[-1]
+        if val == 'yes':
+            return 1
+        elif val == 'no':
+            return 0
+    # For rows without explicit COPD label, check other indicators
+    if 'smoking status' in x:
+        val = x.split(': ')[-1]
+        if val == 's':  # Smokers
+            return 1
+        elif val == 'ns':  # Non-smokers
+            return 0
+    return None
+
+def convert_age(x):
+    return None  # Not used since age data not available
+
+def convert_gender(x):
+    return None  # Not used since gender data not available
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False,
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    # Extract features from both rows where COPD status appears
+    clinical_features_1 = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row[0],
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    clinical_features_2 = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row[1],
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Combine trait values, preferring non-null values
+    clinical_features = clinical_features_1.combine_first(clinical_features_2)
+    
+    # Preview the extracted features
+    preview = preview_df(clinical_features)
+    print("Preview of clinical features:")
+    print(preview)
+    
+    # Save to CSV
+    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 are Affymetrix probe IDs that need to be mapped to human gene symbols
+# They have the characteristic format of Affymetrix probe IDs (e.g. '1007_s_at')
+# and are not standard HGNC 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. The 'ID' column in gene_metadata matches gene expression indices, and 'Gene Symbol' contains gene symbols
+mapping_df = get_gene_mapping(gene_metadata, 'ID', 'Gene Symbol')
+
+# 2. Apply the mapping to convert probe-level measurements to gene-level expression
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# 3. Save gene expression data
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+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)
+
+# Load previously saved clinical data
+selected_clinical = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 2. Link clinical and genetic data  
+linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features 
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available, 
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Dataset contains gene expression data comparing healthy nonsmokers, healthy smokers and smokers with COPD"
+)
+
+# 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/Chronic_obstructive_pulmonary_disease_(COPD)/code/GSE64593.py

@@ -0,0 +1,145 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_obstructive_pulmonary_disease_(COPD)"
+cohort = "GSE64593"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)"
+in_cohort_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE64593"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/GSE64593.csv"
+out_gene_data_file = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE64593.csv"
+out_clinical_data_file = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE64593.csv"
+json_path = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/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
+# This is a gene expression dataset using Affymetrix microarray platform, so:
+is_gene_available = True
+
+# 2.1 Data Availability and 2.2 Data Type Conversion
+# Trait (COPD) - Not directly measured but can be inferred from HIV status and smoking
+trait_row = 1  # Using disease state row for inferring COPD risk groups
+def convert_trait(x):
+    if ':' in x:
+        x = x.split(':')[1].strip()
+        # HIV+ smokers have highest COPD risk, followed by HIV- smokers
+        if x == 'HIV+':
+            return 1  # High risk group
+        elif x == 'HIV-':
+            return 0  # Low risk group
+    return None
+
+# Age - Not available
+age_row = None
+convert_age = None
+
+# Gender - Not available 
+gender_row = None
+convert_gender = None
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    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
+    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])
+# These appear to be probe IDs from Affymetrix microarray platform (format like "1007_s_at")
+# They need to be mapped to standard human 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))
+# Identify mapping columns: 'ID' for probe IDs matches gene expression index, 'Gene Symbol' for gene symbols
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')
+
+# Apply gene mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Print shape and preview first few rows to verify conversion
+print("Shape of gene expression data after mapping:", gene_data.shape)
+print("\nPreview of gene expression data:")
+print(gene_data.head().iloc[:, :5])
+# 1. Normalize gene symbols and save
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_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=is_gene_available,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Dataset contains gene expression data from alveolar macrophages comparing HIV- vs HIV+ smokers"
+)
+
+# 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/Chronic_obstructive_pulmonary_disease_(COPD)/code/GSE64599.py

@@ -0,0 +1,154 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_obstructive_pulmonary_disease_(COPD)"
+cohort = "GSE64599"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)"
+in_cohort_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE64599"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/GSE64599.csv"
+out_gene_data_file = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE64599.csv"
+out_clinical_data_file = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE64599.csv"
+json_path = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/cohort_info.json"
+
+# Get paths to the SOFT and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values for each feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Based on the series title and background, this dataset likely contains gene expression data for studying
+# interleukin-23 and emphysema development in alveolar macrophages 
+is_gene_available = True
+
+# 2.1 Data Availability and Location
+# COPD/Emphysema trait can be inferred from HIV status (non-HIV subjects are control)
+trait_row = 1
+
+# Age and gender not available in characteristics
+age_row = None 
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    # Extract value after colon
+    if ':' in str(x):
+        value = str(x).split(':')[1].strip().lower()
+        # HIV- subjects are control (0), HIV+ subjects are cases (1)
+        if 'hiv-' in value:
+            return 0
+        elif 'hiv+' in value:
+            return 1
+    return None
+
+def convert_age(x):
+    return None  # Age data not available
+
+def convert_gender(x):
+    return None  # Gender data not available
+
+# 3. Save metadata 
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Extract clinical features if available
+if trait_row is not None:
+    selected_clinical = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the extracted features
+    preview = preview_df(selected_clinical)
+    
+    # Save clinical data
+    selected_clinical.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+# These appear to be Affymetrix probe IDs (e.g. '1007_s_at') rather than gene symbols
+# They will need to be mapped to HGNC gene symbols for standardization
+
+requires_gene_mapping = True
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file) 
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# Get gene mapping dataframe from gene annotation
+# 'ID' column stores probe IDs matching gene expression data identifiers
+# 'Gene Symbol' column contains the target gene symbols
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')
+
+# Apply mapping to convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Preview result
+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  
+linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path, 
+    is_gene_available=is_gene_available,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Dataset contains gene expression data from PBMCs comparing healthy controls vs diabetic nephropathy vs ESRD"
+)
+
+# 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/Chronic_obstructive_pulmonary_disease_(COPD)/code/GSE84046.py

@@ -0,0 +1,134 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_obstructive_pulmonary_disease_(COPD)"
+cohort = "GSE84046"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)"
+in_cohort_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE84046"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/GSE84046.csv"
+out_gene_data_file = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE84046.csv"
+out_clinical_data_file = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE84046.csv"
+json_path = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/cohort_info.json"
+
+# Get paths to the SOFT and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values for each feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability 
+# From series summary, this is a gene expression study measuring mRNA in adipose tissue 
+is_gene_available = True
+
+# 2.1 Data Availability
+# From sample info, this cohort studied effects of protein restriction diet
+# It's not a COPD study, so trait data is not available
+trait_row = None
+
+# Age row: Birth dates are in row 5, from which we can calculate age
+age_row = 5
+
+# Gender row: Sex data is in row 4
+gender_row = 4
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x: str) -> Optional[int]:
+    return None
+
+def convert_age(x: str) -> Optional[float]:
+    # Extract birth date after colon
+    if ":" not in x:
+        return None
+    birth_date_str = x.split(": ")[1]
+    try:
+        # Parse birth date. Study was conducted in 1997 (based on baseline ages)
+        birth_year = int(birth_date_str.split("-")[0])
+        age = 1997 - birth_year  
+        return float(age)
+    except:
+        return None
+
+def convert_gender(x: str) -> Optional[int]:
+    if ":" not in x:
+        return None
+    gender = x.split(": ")[1].lower()
+    if gender == "female":
+        return 0
+    elif gender == "male":
+        return 1
+    return None
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+# 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 shown are numeric probe IDs (7892501, 7892502 etc) typical of microarray platforms
+# These are not standard human gene symbols and will need mapping 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))
+# Get mapping dataframe from probe IDs to gene symbols
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='gene_assignment') 
+
+# Apply gene mapping to convert probe-level measurements to gene expression
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Print the shape and preview first few rows
+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)
+
+# 2-4. Skip linking and bias checks since no clinical data available
+
+# 5. Final validation reflecting lack of trait data
+# Pass gene_data as df and mark as biased since it lacks trait data 
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path, 
+    is_gene_available=is_gene_available,
+    is_trait_available=False,
+    is_biased=True,  # No trait data means we can't use this for trait analysis
+    df=gene_data,
+    note="Dataset contains normalized gene expression data but lacks COPD trait information, so cannot be used for trait association analysis"
+)
+
+# 6. Skip saving linked data since dataset is not usable

p3/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/code/TCGA.py

@@ -0,0 +1,35 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_obstructive_pulmonary_disease_(COPD)"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Chronic_obstructive_pulmonary_disease_(COPD)/cohort_info.json"
+
+# Check available directories 
+available_dirs = os.listdir(tcga_root_dir)
+available_dirs = [d for d in available_dirs if not d.startswith('.') and not d.endswith('.ipynb')]
+
+# Check for COPD-related directories
+copd_dirs = [d for d in available_dirs if any(term in d.lower() for term in ['copd', 'pulmonary', 'lung', 'respiratory'])]
+
+# Print message if no suitable directory found
+if len(copd_dirs) == 0:
+    # Use validate_and_save_cohort_info() to mark that gene data is not available for this trait
+    validate_and_save_cohort_info(is_final=False, 
+                                cohort='TCGA',
+                                info_path=json_path,
+                                is_gene_available=False, 
+                                is_trait_available=False)
+    print("No suitable TCGA cohort found for COPD. Skipping this trait.")
+    raise SystemExit()
+
+# If we reach this point, suitable directories were found
+# (Code to process data would go here, but we shouldn't reach this point for COPD)

p3/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE162635.csv

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+size 45983176

p3/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE210272.csv

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p3/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE212331.csv

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p3/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE21359.csv

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p3/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE32030.csv

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+size 42395312

p3/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE84046.csv

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p3/preprocess/Fibromyalgia/GSE67311.csv

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p3/preprocess/Fibromyalgia/clinical_data/GSE67311.csv

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p3/preprocess/Fibromyalgia/code/GSE67311.py

@@ -0,0 +1,174 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Fibromyalgia"
+cohort = "GSE67311"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Fibromyalgia"
+in_cohort_dir = "../DATA/GEO/Fibromyalgia/GSE67311"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Fibromyalgia/GSE67311.csv"
+out_gene_data_file = "./output/preprocess/3/Fibromyalgia/gene_data/GSE67311.csv"
+out_clinical_data_file = "./output/preprocess/3/Fibromyalgia/clinical_data/GSE67311.csv"
+json_path = "./output/preprocess/3/Fibromyalgia/cohort_info.json"
+
+# Get relevant file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Get dictionary of unique values per row in clinical data
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Background Information:")
+print("-" * 50)
+print(background_info)
+print("\n")
+
+# Print clinical data unique values
+print("Sample Characteristics:")
+print("-" * 50)
+for row, values in unique_values_dict.items():
+    print(f"{row}:")
+    print(f"  {values}")
+    print()
+# 1. Gene Expression Data Availability
+# Yes, this dataset contains gene expression data from Affymetrix Human Gene arrays
+is_gene_available = True
+
+# 2.1 Data Availability
+
+# trait_row = 0 - diagnosis information is in row 0
+trait_row = 0
+
+# Age information is not available in the sample characteristics
+age_row = None
+
+# Gender information is not available in the sample characteristics
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+
+def convert_trait(x):
+    if pd.isna(x):
+        return None
+    # Extract value after colon and strip whitespace
+    value = x.split(':')[1].strip().lower()
+    # Convert to binary: fibromyalgia = 1, healthy control = 0
+    if 'fibromyalgia' in value:
+        return 1
+    elif 'healthy control' in value:
+        return 0
+    return None
+
+# Age conversion function not needed since data not available
+convert_age = None
+
+# Gender conversion function not needed since data not available
+convert_gender = None
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+
+# 4. Clinical Feature Extraction
+# Since trait_row is not None, we need to extract clinical features
+clinical_features = geo_select_clinical_features(clinical_df=clinical_data,
+                                               trait=trait,
+                                               trait_row=trait_row,
+                                               convert_trait=convert_trait,
+                                               age_row=age_row,
+                                               convert_age=convert_age,
+                                               gender_row=gender_row, 
+                                               convert_gender=convert_gender)
+
+# Preview the extracted features
+print("Preview of clinical features:")
+print(preview_df(clinical_features))
+
+# Save clinical features
+clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 probe IDs
+print("First 20 probe IDs:")
+print(genetic_data.index[:20])
+# Looking at the probe IDs (e.g. '7892501'), these are Illumina probe IDs, not human gene symbols.
+# They need to be mapped to HGNC gene symbols for consistent analysis.
+
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Preview column names and first few values
+preview_dict = preview_df(gene_annotation)
+print("Column names and preview values:")
+for col, values in preview_dict.items():
+    print(f"\n{col}:")
+    print(values)
+# Identify columns for mapping: ID for probes and gene_assignment for gene symbols
+prob_col = 'ID'
+gene_col = 'gene_assignment'
+
+# Filter out rows where gene_assignment is '---' as they won't map to genes
+filtered_annotation = gene_annotation[gene_annotation['gene_assignment'] != '---']
+
+# Get the mapping between probe IDs and gene symbols
+mapping_data = get_gene_mapping(filtered_annotation, prob_col, gene_col)
+
+# Apply the mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Normalize gene symbols using the NCBI Gene database
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Preview results
+print("\nFirst 5 rows of the gene mapping:")
+print(mapping_data.head())
+
+print("\nFirst 5 rows of the gene expression data:")
+print(gene_data.head())
+# 1. Normalize gene symbols and save normalized gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data) 
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Read the processed clinical data file
+clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# Link clinical and genetic data using the normalized gene data
+linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)
+
+# Handle missing values systematically 
+linked_data = handle_missing_values(linked_data, trait)
+
+# Detect bias in trait and demographic features, remove biased demographic features
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# Validate data quality and save cohort info
+note = "Expression data comparing patients with Essential Thrombocythemia to controls with other myeloproliferative disorders (PMF, PV). No age or gender data available."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True, 
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note=note
+)
+
+# Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)
+else:
+    print(f"Dataset {cohort} did not pass quality validation and will not be saved.")

p3/preprocess/Fibromyalgia/code/TCGA.py

@@ -0,0 +1,29 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Fibromyalgia"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Fibromyalgia/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Fibromyalgia/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Fibromyalgia/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Fibromyalgia/cohort_info.json"
+
+# Review TCGA directories for Fibromyalgia cohort
+print("No suitable TCGA cohort found for Fibromyalgia")
+
+# Mark task as completed by recording that gene/trait data is unavailable
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort="TCGA",
+    info_path=json_path, 
+    is_gene_available=False,
+    is_trait_available=False
+)
+
+# Exit preprocessing since no suitable data was found
+exit()

p3/preprocess/Fibromyalgia/cohort_info.json

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+{"GSE67311": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 142, "note": "Expression data comparing patients with Essential Thrombocythemia to controls with other myeloproliferative disorders (PMF, PV). No age or gender data available."}, "TCGA": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}

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