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p3/preprocess/Cervical_Cancer/clinical_data/GSE131027.csv

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p3/preprocess/Cervical_Cancer/clinical_data/GSE138079.csv

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p3/preprocess/Cervical_Cancer/clinical_data/GSE138080.csv

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p3/preprocess/Cervical_Cancer/clinical_data/GSE163114.csv

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p3/preprocess/Cervical_Cancer/clinical_data/GSE63678.csv

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p3/preprocess/Cervical_Cancer/clinical_data/GSE75132.csv

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p3/preprocess/Cervical_Cancer/code/GSE107754.py

@@ -0,0 +1,139 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cervical_Cancer"
+cohort = "GSE107754"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cervical_Cancer"
+in_cohort_dir = "../DATA/GEO/Cervical_Cancer/GSE107754"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Cervical_Cancer/GSE107754.csv"
+out_gene_data_file = "./output/preprocess/3/Cervical_Cancer/gene_data/GSE107754.csv"
+out_clinical_data_file = "./output/preprocess/3/Cervical_Cancer/clinical_data/GSE107754.csv"
+json_path = "./output/preprocess/3/Cervical_Cancer/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Yes, the background info mentions "whole human genome gene expression microarrays"
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+# Trait (cervical cancer status) is not available as all samples are cancer cases
+trait_row = None
+
+# Gender is available in Feature 0
+gender_row = 0
+
+# Age is not available
+age_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    return None
+
+def convert_gender(x):
+    if not isinstance(x, str):
+        return None
+    value = x.split(': ')[1].strip().lower()
+    if value == 'female':
+        return 0
+    elif value == 'male':
+        return 1
+    return None
+
+def convert_age(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
+# Skip as trait_row is None
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# Looking at the identifiers (e.g. A_23_P100001), these are Agilent array probe IDs, not gene symbols
+# Therefore mapping to gene symbols will be required
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation columns and example values:")
+print(preview_df(gene_annotation))
+# Get the mapping between probe IDs and gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# Apply the gene mapping to convert probe-level data to gene-level data  
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# 1. Normalize gene symbols and save normalized gene data
+gene_data = normalize_gene_symbols_in_index(gene_data) 
+gene_data.to_csv(out_gene_data_file)
+
+# When trait data is unavailable, use gene data as the linked data
+linked_data = gene_data.T  # Transpose to match expected format
+
+# As this dataset only contains cancer samples without controls,
+# it's inherently biased for case-control analysis
+is_biased = True
+
+# Validate and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=is_biased,
+    df=linked_data,
+    note="Dataset contains only cancer samples, no control group for comparison"
+)

p3/preprocess/Cervical_Cancer/code/GSE114243.py

@@ -0,0 +1,255 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cervical_Cancer"
+cohort = "GSE114243"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cervical_Cancer"
+in_cohort_dir = "../DATA/GEO/Cervical_Cancer/GSE114243"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Cervical_Cancer/GSE114243.csv"
+out_gene_data_file = "./output/preprocess/3/Cervical_Cancer/gene_data/GSE114243.csv"
+out_clinical_data_file = "./output/preprocess/3/Cervical_Cancer/clinical_data/GSE114243.csv"
+json_path = "./output/preprocess/3/Cervical_Cancer/cohort_info.json"
+
+# Get all matrix files in directory
+matrix_files = [f for f in os.listdir(in_cohort_dir) if 'matrix' in f.lower()]
+print("Found matrix files:", matrix_files)
+
+# Find a matrix file that's not from the SuperSeries summary
+matrix_file = None
+soft_file = None
+for f in matrix_files:
+    with gzip.open(os.path.join(in_cohort_dir, f), 'rt') as file:
+        first_lines = "".join([next(file) for _ in range(10)])
+        if "SuperSeries" not in first_lines:
+            matrix_file = os.path.join(in_cohort_dir, f)
+            soft_files = [sf for sf in os.listdir(in_cohort_dir) if 'soft' in sf.lower() 
+                         and f.split('_')[0] in sf]
+            if soft_files:
+                soft_file = os.path.join(in_cohort_dir, soft_files[0])
+            break
+
+if matrix_file is None or soft_file is None:
+    raise ValueError("Could not find suitable matrix and SOFT files")
+
+print(f"Using SOFT file: {os.path.basename(soft_file)}")  
+print(f"Using matrix file: {os.path.basename(matrix_file)}\n")
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# Get all matrix files in directory to check options
+matrix_files = [f for f in os.listdir(in_cohort_dir) if 'matrix' in f.lower()]
+print("Available matrix files:", matrix_files)
+
+# Try each matrix file until we find one that's not SuperSeries
+matrix_file = None
+soft_file = None
+
+for f in matrix_files:
+    test_file = os.path.join(in_cohort_dir, f)
+    test_soft = os.path.join(in_cohort_dir, f.replace('matrix.txt.gz', 'soft.gz'))
+    
+    if os.path.exists(test_soft):
+        with gzip.open(test_file, 'rt') as file:
+            first_lines = "".join([next(file) for _ in range(10)])
+            if "SuperSeries" not in first_lines:
+                matrix_file = test_file
+                soft_file = test_soft
+                break
+
+if matrix_file is None or soft_file is None:
+    raise ValueError("Could not find suitable matrix and SOFT files")
+
+print(f"\nUsing SOFT file: {os.path.basename(soft_file)}")
+print(f"Using matrix file: {os.path.basename(matrix_file)}\n")
+
+# Extract background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# Get file paths
+files = os.listdir(in_cohort_dir)
+matrix_files = [f for f in files if 'matrix' in f.lower()]
+matrix_file = None
+soft_file = None
+
+for mf in matrix_files:
+    matrix_file = os.path.join(in_cohort_dir, mf)
+    with gzip.open(matrix_file, 'rt') as f:
+        header = ''.join([next(f) for _ in range(10)])
+        if 'SuperSeries' not in header:
+            soft_file = os.path.join(in_cohort_dir, mf.replace('matrix.txt.gz', 'soft.gz'))
+            if os.path.exists(soft_file):
+                break
+
+if not matrix_file or not soft_file:
+    raise ValueError("Could not find suitable matrix and soft files")
+
+print(f"Using matrix file: {os.path.basename(matrix_file)}")
+print(f"Using soft file: {os.path.basename(soft_file)}\n")
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# Get file paths for each platform GPL
+matrix_files = sorted([f for f in os.listdir(in_cohort_dir) if 'matrix' in f.lower() and 'GPL6480' in f])
+matrix_file = os.path.join(in_cohort_dir, matrix_files[0])
+soft_file = os.path.join(in_cohort_dir, matrix_files[0].replace('matrix.txt.gz', 'soft.gz'))
+
+print(f"Using matrix file: {os.path.basename(matrix_file)}")
+print(f"Using SOFT file: {os.path.basename(soft_file)}\n")
+
+# Extract background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature  
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on GPL6480 platform and HEK293T cells, likely to contain gene expression data
+
+# 2.1 Data Availability
+trait_row = None # All samples are from HEK293T cells, no cancer/control status
+age_row = None # Age info not provided
+gender_row = None # Gender info not provided
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    return None
+
+def convert_age(x): 
+    return None
+    
+def convert_gender(x):
+    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=False  # trait_row is None
+)
+
+# 4. Skip clinical feature extraction since trait_row is None
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation columns and example values:")
+print(preview_df(gene_annotation))
+# Get gene expression data from matrix file
+gene_expression = get_genetic_data(matrix_file)
+
+# Get gene mapping using probe ID ('ID') and gene symbol ('GENE_SYMBOL') columns
+gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# Map probe IDs to gene symbols and aggregate expression data
+gene_data = apply_gene_mapping(gene_expression, gene_mapping)
+
+# Preview the gene expression data
+print("\nGene expression data preview:")
+print(preview_df(gene_data))
+# Recreate gene expression data from Step 7
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+gene_annotation = get_gene_annotation(soft_file)
+gene_expression = get_genetic_data(matrix_file) 
+gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+gene_data = apply_gene_mapping(gene_expression, gene_mapping)
+
+# 1. Normalize gene symbols and save normalized gene data
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2-4. Skip clinical data linking and bias checking since trait data is unavailable
+
+# 5. Validate data quality and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False, # No trait data available
+    is_biased=None, # Cannot determine bias without trait data
+    df=gene_data,
+    note="Cell line gene expression data without trait information"
+)
+
+# 6. Skip saving linked data since trait data is unavailable
+# Setup gene expression data first
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+gene_annotation = get_gene_annotation(soft_file)
+gene_expression = get_genetic_data(matrix_file) 
+gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+gene_data = apply_gene_mapping(gene_expression, gene_mapping)
+
+# 1. Normalize gene symbols and save normalized gene data
+gene_data = normalize_gene_symbols_in_index(gene_data) 
+gene_data.to_csv(out_gene_data_file)
+
+# 2-4. Skip clinical/trait processing as no trait data available
+
+# 5. Save metadata indicating dataset is unusable for trait studies
+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,  # No trait comparison possible with single cell line
+    df=gene_data,
+    note="Cell line gene expression data without trait comparison information"
+)

p3/preprocess/Cervical_Cancer/code/GSE131027.py

@@ -0,0 +1,92 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cervical_Cancer"
+cohort = "GSE131027"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cervical_Cancer"
+in_cohort_dir = "../DATA/GEO/Cervical_Cancer/GSE131027"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Cervical_Cancer/GSE131027.csv"
+out_gene_data_file = "./output/preprocess/3/Cervical_Cancer/gene_data/GSE131027.csv"
+out_clinical_data_file = "./output/preprocess/3/Cervical_Cancer/clinical_data/GSE131027.csv"
+json_path = "./output/preprocess/3/Cervical_Cancer/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Based on series summary, this dataset focuses on germline variants/mutations, not gene expression
+is_gene_available = False
+
+# 2.1 Data Availability
+# For trait: cancer status can be inferred from feature 1 (cancer type)
+trait_row = 1
+# Age and gender are not available in characteristics data
+age_row = None 
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    if not isinstance(value, str):
+        return None
+    value = value.lower()
+    if "cancer:" in value:
+        value = value.split("cancer:")[1].strip()
+        # Convert to binary - 1 for cervical cancer, 0 for other cancers
+        return 1 if "cervical" in value else 0
+    return None
+
+def convert_age(value):
+    return None  # Age data not available
+
+def convert_gender(value):
+    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. 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,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the processed clinical data
+    preview = preview_df(clinical_df)
+    print("Preview of processed clinical data:", preview)
+    
+    # Save clinical data
+    clinical_df.to_csv(out_clinical_data_file)

p3/preprocess/Cervical_Cancer/code/GSE137034.py

@@ -0,0 +1,57 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cervical_Cancer"
+cohort = "GSE137034"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cervical_Cancer"
+in_cohort_dir = "../DATA/GEO/Cervical_Cancer/GSE137034"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Cervical_Cancer/GSE137034.csv"
+out_gene_data_file = "./output/preprocess/3/Cervical_Cancer/gene_data/GSE137034.csv"
+out_clinical_data_file = "./output/preprocess/3/Cervical_Cancer/clinical_data/GSE137034.csv"
+json_path = "./output/preprocess/3/Cervical_Cancer/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+is_gene_available = False # This dataset appears to study chromatin accessibility rather than gene expression
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1. Identify feature rows
+trait_row = None # Cannot reliably classify samples as cervical cancer vs control
+age_row = None # No age information
+gender_row = None # No gender information
+
+# 2.2. Define conversion functions  
+# Not needed since no clinical data is available
+
+# 3. Save metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=trait_row is not None
+)
+
+# 4. Skip clinical feature extraction since trait_row is None

p3/preprocess/Cervical_Cancer/code/GSE138079.py

@@ -0,0 +1,176 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cervical_Cancer"
+cohort = "GSE138079"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cervical_Cancer"
+in_cohort_dir = "../DATA/GEO/Cervical_Cancer/GSE138079"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Cervical_Cancer/GSE138079.csv"
+out_gene_data_file = "./output/preprocess/3/Cervical_Cancer/gene_data/GSE138079.csv"
+out_clinical_data_file = "./output/preprocess/3/Cervical_Cancer/clinical_data/GSE138079.csv"
+json_path = "./output/preprocess/3/Cervical_Cancer/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Yes, this is a mRNA microarray dataset (Agilent 4x44K) according to background info
+is_gene_available = True
+
+# 2.1 Data Availability 
+# None of trait, age, or gender data is directly available in sample characteristics
+# But trait (transformation stage) can be extracted from Feature 1
+trait_row = 1  
+age_row = None
+gender_row = None
+
+# 2.2 Data Type Conversion
+def convert_trait(value):
+    if not value or ':' not in value:
+        return None
+    stage = value.split(':')[1].strip()
+    # Convert transformation stages to binary (0=early/benign, 1=late/malignant)
+    if stage == 'extended lifespan':
+        return 0  # Early stage
+    elif stage == 'immortal':
+        return 0  # Early stage
+    elif stage == 'anchorage independent':
+        return 1  # Late stage, more transformed/malignant
+    return None
+
+def convert_age(value):
+    return None  # Not used
+
+def convert_gender(value):
+    return None  # Not used
+
+# 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
+    )
+    
+    # Preview the processed clinical data
+    preview_result = preview_df(selected_clinical_df)
+    print("Clinical data preview:", preview_result)
+    
+    # Save to CSV
+    selected_clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# Looking at the gene identifiers (which are just numbers like '12', '13', etc.)
+# and comparing with the raw file which shows "ID_REF" as column header,
+# these appear to be probe IDs from a microarray rather than gene symbols
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation columns and example values:")
+print(preview_df(gene_annotation))
+# 1. From previous output we can see:
+# - Gene expression data uses 'ID' as identifiers
+# - Gene annotation data has 'ID' and 'GENE_SYMBOL' columns
+
+# 2. Get gene mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# 3. Apply gene mapping to convert probe-level data to gene-level data 
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Print shape and preview to verify the mapping
+print("\nShape of mapped gene expression data:", gene_data.shape)
+print("\nFirst few rows of mapped data:")
+print(gene_data.head())
+# 1. Normalize gene symbols and save normalized gene data
+gene_data = normalize_gene_symbols_in_index(gene_data) 
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data and trait
+# First get selected clinical features using the extraction function from previous step
+selected_clinical = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)
+
+# 3. Handle missing values systematically
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features and remove them if needed
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate data quality and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True, 
+    is_biased=is_biased,
+    df=linked_data,
+    note="Gene expression data comparing cervical carcinoma vs normal tissue samples"
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Cervical_Cancer/code/GSE138080.py

@@ -0,0 +1,229 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cervical_Cancer"
+cohort = "GSE138080"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cervical_Cancer"
+in_cohort_dir = "../DATA/GEO/Cervical_Cancer/GSE138080"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Cervical_Cancer/GSE138080.csv"
+out_gene_data_file = "./output/preprocess/3/Cervical_Cancer/gene_data/GSE138080.csv"
+out_clinical_data_file = "./output/preprocess/3/Cervical_Cancer/clinical_data/GSE138080.csv"
+json_path = "./output/preprocess/3/Cervical_Cancer/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability 
+# Based on background info mentioning "mRNA tissues" and "whole human genome oligo microarrays"
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 For trait: 
+# Feature 0 contains info about cancer stage (normal vs CIN2/3 vs SCC)
+trait_row = 0
+
+def convert_trait(value: str) -> float:
+    # Binary: 0 for normal, 1 for cancer/pre-cancer
+    if not value or ':' not in value:
+        return None
+    value = value.split(': ')[1].lower()
+    if 'normal' in value:
+        return 0.0
+    elif 'carcinoma' in value or 'neoplasia' in value:
+        return 1.0
+    return None
+
+# 2.2 Age and gender not explicitly available
+age_row = None 
+gender_row = None
+
+def convert_age(value: str) -> float:
+    return None
+
+def convert_gender(value: str) -> float:
+    return None
+
+# 3. Save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical feature extraction
+if trait_row is not None:
+    selected_clinical = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview data
+    preview = preview_df(selected_clinical)
+    print("Preview of selected clinical features:")
+    print(preview)
+    
+    # Save to CSV
+    selected_clinical.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs
+print("First 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+# These appear to be probe IDs rather than human gene symbols
+# They are numerical identifiers which is not the format for gene symbols
+# Gene symbols typically follow patterns like "TP53", "BRCA1", "IL6", etc.
+# Therefore mapping will be required to convert these to gene symbols
+requires_gene_mapping = True
+# Examine first few hundred lines of SOFT file to find where gene annotations are stored
+with gzip.open(soft_file, 'rt') as f:
+    content = ''.join([next(f) for _ in range(100)])
+print("First 100 lines of SOFT file:")
+print(content)
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file)
+
+print("Preview of gene annotation data columns:")
+print(gene_metadata.columns.tolist())
+print("\nFirst few rows:")
+print(gene_metadata.head())
+
+# Create mapping between probe IDs and gene symbols 
+mapping_data = get_gene_mapping(gene_metadata, 'ID', 'GENE_SYMBOL')
+print("\nFirst few rows of mapping data:")
+print(mapping_data.head())
+
+# Apply mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+print("\nFirst few gene symbols after mapping:")
+print(gene_data.index[:10])
+
+# Save gene expression data
+gene_data.to_csv(out_gene_data_file)
+# 1. Normalize gene symbols and save normalized gene data
+gene_data = normalize_gene_symbols_in_index(gene_data) 
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
+
+# 3. Handle missing values systematically
+linked_data = handle_missing_values(linked_data, "Cervical_Cancer") 
+
+# 4. Check for biased features and remove them if needed
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, "Cervical_Cancer")
+
+# 5. Validate data quality and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note="Gene expression data comparing cervical carcinoma vs normal tissue samples"
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)
+# 1. Normalize gene symbols and save normalized gene data
+gene_data = normalize_gene_symbols_in_index(gene_data) 
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+selected_clinical = pd.DataFrame(clinical_data.loc[trait])
+linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)
+
+# 3. Handle missing values systematically 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features and remove them if needed
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate data quality and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True, 
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note="Gene expression data comparing cervical carcinoma vs normal tissue samples"
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)
+# 1. Normalize gene symbols and save normalized gene data
+gene_data = normalize_gene_symbols_in_index(gene_data) 
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data and trait
+# First get selected clinical features using the extraction function from previous step
+selected_clinical = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)
+
+# 3. Handle missing values systematically
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features and remove them if needed
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate data quality and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True, 
+    is_biased=is_biased,
+    df=linked_data,
+    note="Gene expression data comparing cervical carcinoma vs normal tissue samples"
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Cervical_Cancer/code/GSE146114.py

@@ -0,0 +1,161 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cervical_Cancer"
+cohort = "GSE146114"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cervical_Cancer"
+in_cohort_dir = "../DATA/GEO/Cervical_Cancer/GSE146114"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Cervical_Cancer/GSE146114.csv"
+out_gene_data_file = "./output/preprocess/3/Cervical_Cancer/gene_data/GSE146114.csv"
+out_clinical_data_file = "./output/preprocess/3/Cervical_Cancer/clinical_data/GSE146114.csv"
+json_path = "./output/preprocess/3/Cervical_Cancer/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Based on Series_summary, this study used RNA-seq with Illumina platforms,
+# so gene expression data should be available
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+# Trait (cancer stage) is available in feature 7 with FIGO stages
+trait_row = 7
+
+# Age is not available in any feature
+age_row = None 
+
+# Gender is not available/mentioned in any feature
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    if pd.isna(x):
+        return None
+    # Extract value after colon
+    val = x.split(': ')[1].strip() if ': ' in x else x
+    
+    # Convert FIGO stages to binary
+    # Stage 2B and above are considered advanced
+    early_stages = ['1B1', '1B2', '2A']
+    advanced_stages = ['2B', '3A', '3B', '4A']
+    
+    if val in early_stages:
+        return 0  # Early stage
+    elif val in advanced_stages:
+        return 1  # Advanced stage
+    return None
+
+def convert_age(x):
+    # Not needed since age data is not available
+    return None
+
+def convert_gender(x):
+    # Not needed since gender data is not available
+    return None
+
+# 3. Save Metadata
+# Conduct 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=(trait_row is not None)
+)
+
+# 4. Clinical Feature Extraction
+# Since trait_row is not None, we need to extract clinical features
+selected_clinical = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Preview the extracted features
+preview = preview_df(selected_clinical)
+print("Preview of clinical features:")
+print(preview)
+
+# Save clinical data
+selected_clinical.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs
+print("First 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+# These are Illumina probe IDs (starting with ILMN_) which need to be mapped to official gene symbols
+# They are not standard human gene symbols like BRCA1, TP53 etc.
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation columns and example values:")
+print(preview_df(gene_annotation))
+# 1. Identify mapping columns
+# ID column contains Illumina probe IDs (e.g., ILMN_1825594)
+# Symbol column contains gene symbols (e.g., JMJD1A)
+prob_col = 'ID'
+gene_col = 'Symbol'
+
+# 2. Get gene mapping dataframe
+gene_mapping = get_gene_mapping(gene_annotation, prob_col, gene_col)
+
+# 3. Apply gene mapping to convert probe expression to gene expression 
+gene_data = apply_gene_mapping(gene_data, gene_mapping)
+# 1. Normalize gene symbols and save normalized gene data
+gene_data = normalize_gene_symbols_in_index(gene_data) 
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)
+
+# 3. Handle missing values systematically
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features and remove them if needed
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate data quality and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path, 
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note="Gene expression data comparing cervical cancer stages (early vs advanced) based on FIGO staging"
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Cervical_Cancer/code/GSE163114.py

@@ -0,0 +1,114 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cervical_Cancer"
+cohort = "GSE163114"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cervical_Cancer"
+in_cohort_dir = "../DATA/GEO/Cervical_Cancer/GSE163114"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Cervical_Cancer/GSE163114.csv"
+out_gene_data_file = "./output/preprocess/3/Cervical_Cancer/gene_data/GSE163114.csv"
+out_clinical_data_file = "./output/preprocess/3/Cervical_Cancer/clinical_data/GSE163114.csv"
+json_path = "./output/preprocess/3/Cervical_Cancer/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Based on "HeLa" cell line experiments, this is likely a gene expression dataset
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion 
+# 2.1 Data Availability
+# Feature 1 indicates treatment groups - can be used as trait 
+trait_row = 1
+# Age and gender not available since this is cell line data
+age_row = None 
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    if not isinstance(x, str):
+        return None
+    value = x.split(": ")[-1].strip().lower()
+    # Convert control vs Ki-67 knockdown to binary
+    if "control" in value:
+        return 0  # control
+    elif "ki-67" in value:
+        return 1  # Ki-67 knockdown
+    return None
+
+convert_age = None
+convert_gender = None
+
+# 3. Save Metadata
+# Conduct 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=trait_row is not None
+)
+
+# 4. Clinical Feature Extraction
+# Since trait_row is available, 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
+print("Preview of clinical features:")
+print(preview_df(clinical_df))
+
+# Save clinical data
+os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+clinical_df.to_csv(out_clinical_data_file)
+# First let's identify the SubSeries information
+with gzip.open(soft_file, 'rt') as file:
+    for line in file:
+        if 'Series = GSE' in line:
+            print(line.strip())
+        elif '!Series_type' in line:
+            print(line.strip())
+        # Stop after finding subseries info
+        elif '!series_matrix_table_begin' in line:
+            break
+
+print("\nAnalyzing first subseries:")
+# Construct path to first subseries
+subseries_dir = os.path.join(os.path.dirname(in_cohort_dir), "GSE163112")
+subseries_soft, subseries_matrix = geo_get_relevant_filepaths(subseries_dir)
+
+# Extract gene expression data from subseries matrix file
+gene_data = get_genetic_data(subseries_matrix)
+
+# Print first 20 row IDs
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])

p3/preprocess/Cervical_Cancer/code/GSE63678.py

@@ -0,0 +1,128 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cervical_Cancer"
+cohort = "GSE63678"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cervical_Cancer"
+in_cohort_dir = "../DATA/GEO/Cervical_Cancer/GSE63678"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Cervical_Cancer/GSE63678.csv"
+out_gene_data_file = "./output/preprocess/3/Cervical_Cancer/gene_data/GSE63678.csv"
+out_clinical_data_file = "./output/preprocess/3/Cervical_Cancer/clinical_data/GSE63678.csv"
+json_path = "./output/preprocess/3/Cervical_Cancer/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Process clinical data to get trait information - disease state from row 1
+clinical_df = geo_select_clinical_features(
+    clinical_data,
+    trait="disease_state", 
+    trait_row=1,
+    convert_trait=lambda x: 1 if "carcinoma" in str(x).lower() else (0 if "normal" in str(x).lower() else None)
+)
+
+# Save clinical data
+clinical_df.to_csv(out_clinical_data_file)
+
+# Normalize gene symbols and save normalized gene data
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
+
+# Handle missing values systematically
+linked_data = handle_missing_values(linked_data, "disease_state")
+
+# Check for biased features and remove them if needed 
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, "disease_state")
+
+# Validate data quality and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note="Gene expression data comparing cervical carcinoma vs normal tissue samples"
+)
+
+# Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs
+print("First 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+# Review identifiers - using knowledge of gene naming conventions
+# These are from Affymetrix HG-U133A platform which requires mapping to gene symbols
+
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation columns and example values:")
+print(preview_df(gene_annotation))
+# Extract probe-to-gene mapping from annotation data
+# ID column contains probe IDs matching gene expression data
+# Gene Symbol column contains target gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# Apply gene mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# 1. Normalize gene symbols and save normalized gene data
+gene_data = normalize_gene_symbols_in_index(gene_data) 
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
+
+# 3. Handle missing values systematically
+linked_data = handle_missing_values(linked_data, "disease_state")
+
+# 4. Check for biased features and remove them if needed
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, "disease_state")
+
+# 5. Validate data quality and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path, 
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note="Gene expression data comparing cervical carcinoma vs normal tissue samples"
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Cervical_Cancer/code/GSE75132.py

@@ -0,0 +1,145 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cervical_Cancer"
+cohort = "GSE75132"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cervical_Cancer"
+in_cohort_dir = "../DATA/GEO/Cervical_Cancer/GSE75132"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Cervical_Cancer/GSE75132.csv"
+out_gene_data_file = "./output/preprocess/3/Cervical_Cancer/gene_data/GSE75132.csv"
+out_clinical_data_file = "./output/preprocess/3/Cervical_Cancer/clinical_data/GSE75132.csv"
+json_path = "./output/preprocess/3/Cervical_Cancer/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Based on background info, this is gene expression data (TMEM45A, SERPINB5, p16INK4A)
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+trait_row = 1  # Category/progression info in row 1
+age_row = None  # Age not available 
+gender_row = None  # Gender not available
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    """Convert progression category to binary:
+    0 = normal/no progression
+    1 = progression (categories 1 & 2 both indicate HPV infection)
+    """
+    if not isinstance(x, str):
+        return None
+    value = x.split(': ')[-1]
+    try:
+        category = int(value)
+        if category == 0:
+            return 0
+        elif category in [1, 2]:
+            return 1
+        return None
+    except:
+        return None
+
+convert_age = None
+convert_gender = None
+
+# 3. Save metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    selected_clinical = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait
+    )
+    
+    # Preview the processed clinical data
+    print("Preview of processed clinical data:")
+    print(preview_df(selected_clinical))
+    
+    # Save clinical data
+    selected_clinical.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs
+print("First 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation columns and example values:")
+print(preview_df(gene_annotation))
+# 1 & 2. Get gene mapping from annotation data
+# The 'ID' column in gene_annotation matches the probe IDs in gene_data
+# The 'Gene Symbol' column contains the corresponding gene symbols
+mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# 3. Map probes to genes and convert to gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Print preview of mapped gene data 
+print("\nPreview of gene expression data after mapping:")
+print(gene_data.index[:5])
+# 1. Normalize gene symbols and save normalized gene data
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
+
+# Convert index to column with trait name before handling missing values
+linked_data = linked_data.reset_index().rename(columns={linked_data.index.name: trait})
+
+# 3. Handle missing values systematically 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features and remove them if needed
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate data quality and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort, 
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note="Gene expression data from cervical tissue samples. Trait defined based on HPV infection and progression."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Cervical_Cancer/code/TCGA.py

@@ -0,0 +1,104 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cervical_Cancer"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Cervical_Cancer/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Cervical_Cancer/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Cervical_Cancer/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Cervical_Cancer/cohort_info.json"
+
+# 1. Select the relevant subdirectory for cervical cancer
+subdirectory = 'TCGA_Cervical_Cancer_(CESC)' 
+cohort_dir = os.path.join(tcga_root_dir, subdirectory)
+
+# 2. Get the file paths
+clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+# 3. Load the data files
+clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t')
+genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t')
+
+# 4. Print clinical data columns 
+print("Clinical data columns:")
+print(clinical_df.columns.tolist())
+# Step 1: Identify candidate columns
+candidate_age_cols = ["age_at_initial_pathologic_diagnosis", "age_began_smoking_in_years", "days_to_birth"]
+candidate_gender_cols = ["gender"]
+
+# Step 2: Use correct TCGA cohort name
+clinical_path, _ = tcga_get_relevant_filepaths(os.path.join(tcga_root_dir, "CESC"))
+clinical_df = pd.read_csv(clinical_path, sep='\t', index_col=0)
+
+# Preview selected columns
+preview_cols = candidate_age_cols + candidate_gender_cols
+preview_data = preview_df(clinical_df[preview_cols])
+print("Preview of demographic columns:", preview_data)
+# 1. Select the relevant subdirectory for cervical cancer
+subdirectory = 'TCGA_Cervical_Cancer_(CESC)' 
+cohort_dir = os.path.join(tcga_root_dir, subdirectory)
+
+# 2. Get the file paths
+clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+# 3. Load the data files
+clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t')
+genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t')
+
+# 4. Print clinical data columns 
+print("Clinical data columns:")
+print(clinical_df.columns.tolist())
+# Age column candidates contain words like 'age' or 'birth'
+age_col = 'age_at_initial_pathologic_diagnosis'  # Contains clear numerical age values
+
+# Gender column candidates contain word 'gender'
+gender_col = 'gender'  # Contains clear gender values
+
+# Print selected columns
+print(f"Selected age column: {age_col}")
+print(f"Selected gender column: {gender_col}")
+# 1. Extract and standardize clinical features
+# First create trait labels using sample IDs, then add demographics if available
+clinical_features = tcga_select_clinical_features(
+    clinical_df, 
+    trait=trait,
+    age_col='age_at_initial_pathologic_diagnosis',
+    gender_col='gender'
+)
+
+# 2. Normalize gene symbols and save
+normalized_gene_df = normalize_gene_symbols_in_index(genetic_df)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_df.to_csv(out_gene_data_file)
+
+# 3. Link clinical and genetic data
+linked_data = pd.concat([clinical_features, normalized_gene_df.T], axis=1)
+
+# 4. Handle missing values systematically
+linked_data = handle_missing_values(linked_data, trait)
+
+# 5. Check for bias in trait and demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 6. Validate data quality and save cohort info
+note = "Contains molecular data from tumor and normal samples with patient demographics."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note=note
+)
+
+# 7. 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/Cervical_Cancer/gene_data/GSE107754.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a82da64a5edcbde2a3ccf1c07d005767e02ceeaad822b4ed6217419063405079
+size 19822703

p3/preprocess/Cervical_Cancer/gene_data/GSE138079.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ce5db634b575bdbdafa08fb5f64269273ee335c54eee01add0988aeff973e030
+size 14805206

p3/preprocess/Cervical_Cancer/gene_data/GSE146114.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f1451952dd968a27803c16c51aa325f075ec49a344592f530380fee4e49a2c3f
+size 18757988

p3/preprocess/Chronic_Fatigue_Syndrome/clinical_data/GSE251792.csv

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+Age,61.0,37.0,56.0,56.0,24.0,58.0,43.0,26.0,40.0,47.0,22.0,54.0,58.0,44.0,20.0,26.0,23.0,33.0,54.0,25.0,58.0,37.0,23.0,22.0,51.0,48.0,36.0,56.0,38.0,60.0,37.0,25.0,44.0,61.0,50.0,60.0,47.0,49.0,50.0,55.0,60.0,57.0,44.0,60.0,37.0,58.0,60.0,56.0,24.0,50.0,51.0,55.0,48.0,26.0,22.0,38.0,50.0,56.0,33.0,47.0,22.0,23.0,23.0,58.0,54.0,37.0,36.0,61.0,49.0,57.0,60.0,25.0,47.0,44.0,56.0,54.0,58.0,20.0,37.0,26.0,25.0,43.0,40.0,61.0
+Gender,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0

p3/preprocess/Chronic_Fatigue_Syndrome/clinical_data/GSE39684.csv

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+,GSM977688,GSM977689,GSM977690,GSM977691,GSM977692,GSM977693,GSM977694,GSM977695,GSM977696,GSM977697,GSM977698,GSM977699,GSM977700,GSM977701,GSM977702,GSM977703,GSM977704,GSM977705,GSM977706,GSM977707,GSM977708,GSM977709,GSM977710,GSM977711,GSM977712,GSM977713,GSM977714,GSM977715,GSM977716,GSM977717,GSM977718,GSM977719,GSM977720,GSM977721,GSM977722,GSM977723,GSM977724,GSM977725,GSM977726,GSM977727,GSM977728,GSM977729,GSM977730,GSM977731,GSM977732,GSM977733,GSM977734,GSM977735,GSM977736,GSM977737,GSM977738,GSM977739,GSM977740,GSM977741,GSM977742,GSM977743,GSM977744,GSM977745,GSM977746,GSM977747,GSM977748,GSM977749,GSM977750
+Chronic_Fatigue_Syndrome,,,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0

p3/preprocess/Chronic_Fatigue_Syndrome/clinical_data/GSE67311.csv

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p3/preprocess/Chronic_Fatigue_Syndrome/code/GSE251792.py

@@ -0,0 +1,154 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_Fatigue_Syndrome"
+cohort = "GSE251792"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_Fatigue_Syndrome"
+in_cohort_dir = "../DATA/GEO/Chronic_Fatigue_Syndrome/GSE251792"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Chronic_Fatigue_Syndrome/GSE251792.csv"
+out_gene_data_file = "./output/preprocess/3/Chronic_Fatigue_Syndrome/gene_data/GSE251792.csv"
+out_clinical_data_file = "./output/preprocess/3/Chronic_Fatigue_Syndrome/clinical_data/GSE251792.csv"
+json_path = "./output/preprocess/3/Chronic_Fatigue_Syndrome/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 appears available given this is a deep phenotyping study
+is_gene_available = True 
+
+# Feature row identification 
+trait_row = 2  # 'group' field contains trait data
+age_row = 1    # 'age' field contains age data
+gender_row = 0 # 'Sex' field contains gender data
+
+# Convert trait (Patient=1, Control=0)
+def convert_trait(x):
+    if not isinstance(x, str):
+        return None
+    x = x.lower().split(': ')[-1].strip()
+    if 'patient' in x:
+        return 1
+    elif 'control' in x:
+        return 0
+    return None
+
+# Convert age to float
+def convert_age(x):
+    if not isinstance(x, str):
+        return None
+    try:
+        return float(x.split(': ')[-1])
+    except:
+        return None
+
+# Convert gender (Female=0, Male=1) 
+def convert_gender(x):
+    if not isinstance(x, str):
+        return None
+    x = x.lower().split(': ')[-1].strip()
+    if 'female' in x:
+        return 0
+    elif 'male' in x:
+        return 1
+    return None
+
+# 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)
+
+# Extract clinical features since trait data is available
+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 clinical data
+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])
+# Row IDs like 'HCE000104' are Helicos BioSciences Corporation Probe IDs, not standard gene symbols
+# These 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))
+# Get gene mapping using ID and EntrezGeneSymbol columns
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='EntrezGeneSymbol')
+
+# Apply gene mapping to convert probe values to gene expression values
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Preview the resulting gene expression data
+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  
+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=is_gene_available,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Dataset contains gene expression data from blood samples used to study chronic fatigue syndrome"
+)
+
+# 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_Fatigue_Syndrome/code/GSE39684.py

@@ -0,0 +1,129 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_Fatigue_Syndrome"
+cohort = "GSE39684"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_Fatigue_Syndrome"
+in_cohort_dir = "../DATA/GEO/Chronic_Fatigue_Syndrome/GSE39684"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Chronic_Fatigue_Syndrome/GSE39684.csv"
+out_gene_data_file = "./output/preprocess/3/Chronic_Fatigue_Syndrome/gene_data/GSE39684.csv"
+out_clinical_data_file = "./output/preprocess/3/Chronic_Fatigue_Syndrome/clinical_data/GSE39684.csv"
+json_path = "./output/preprocess/3/Chronic_Fatigue_Syndrome/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 microarray data from prostate tissue samples analyzing genes
+is_gene_available = True
+
+# 2. Clinical Feature Analysis
+# 2.1 Data Availability
+# From cohort in sample char row 1, we can infer cohort year represents CFS cases vs controls
+trait_row = 1
+# No age information available
+age_row = None  
+# No gender information available 
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(val):
+    if not isinstance(val, str):
+        return None
+    # Cohort 2006 = CFS cases, 2012 = controls
+    if "cohort:" in val:
+        year = val.split(":")[1].strip()
+        if year == "2006":
+            return 1  # Cases
+        elif year == "2012": 
+            return 0  # Controls
+    return None
+
+def convert_age(val):
+    # No age data
+    return None
+
+def convert_gender(val):
+    # No gender data
+    return None
+
+# 3. Save Metadata 
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=trait_row is not None
+)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        clinical_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
+    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 custom probe IDs from a microarray platform (XXX-V3-70mer format)
+# and will need to be mapped to official gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file)
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+
+print("\nIMPORTANT NOTE: After reviewing the gene annotation data,")
+print("it is clear this dataset contains viral gene expression data (Parvovirus, Retrovirus etc.)")
+print("rather than human gene expression data. Therefore this dataset is not suitable for human trait analysis.")
+
+# Invalidate our previous assessment
+is_gene_available = False
+
+# Re-run validation with updated gene availability
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=trait_row is not None
+)

p3/preprocess/Chronic_Fatigue_Syndrome/code/GSE67311.py

@@ -0,0 +1,157 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_Fatigue_Syndrome"
+cohort = "GSE67311"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_Fatigue_Syndrome"
+in_cohort_dir = "../DATA/GEO/Chronic_Fatigue_Syndrome/GSE67311"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Chronic_Fatigue_Syndrome/GSE67311.csv"
+out_gene_data_file = "./output/preprocess/3/Chronic_Fatigue_Syndrome/gene_data/GSE67311.csv"
+out_clinical_data_file = "./output/preprocess/3/Chronic_Fatigue_Syndrome/clinical_data/GSE67311.csv"
+json_path = "./output/preprocess/3/Chronic_Fatigue_Syndrome/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
+# Dataset uses Affymetrix Human Gene arrays, indicating gene expression data is available
+is_gene_available = True
+
+# 2. Variable Availability and Data Type
+# 2.1 Row identification
+trait_row = 8  # Chronic fatigue syndrome status in row 8
+age_row = None  # Age not available 
+gender_row = None  # Gender not available
+
+# 2.2 Data type conversion functions
+def convert_trait(value: str) -> int:
+    """Convert CFS status to binary (0: No CFS, 1: Has CFS)"""
+    if pd.isna(value):
+        return None
+    value = value.lower().split(': ')[-1]
+    if value == 'yes':
+        return 1
+    elif value == 'no':
+        return 0
+    return None
+
+def convert_age(value: str) -> float:
+    """Convert age to float - not used as age unavailable"""
+    return None
+
+def convert_gender(value: str) -> int:
+    """Convert gender to binary - not used as gender unavailable"""
+    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
+clinical_df = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Preview the extracted features
+print("Preview of clinical features:")
+print(preview_df(clinical_df))
+
+# Save clinical features
+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])
+# Gene probe identifier pattern suggests they are not gene symbols
+# The identifiers are numeric values in the format 7XXXXXX, which appear to be Illumina probe IDs
+# We will need to perform identifier 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))
+# 1. Identify columns for mapping
+# 'ID' column in gene_metadata contains probe IDs matching genetic_df index
+# 'gene_assignment' column contains gene symbols in the format "NM_XXX // GENESYMBOL // description"
+
+# 2. Get mapping dataframe by extracting probe IDs and gene symbols
+# Use text extraction to get gene symbols from gene_assignment strings
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='gene_assignment')
+
+# 3. Apply mapping to convert probe data to gene expression data
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Print info about the mapping result
+print(f"Original probe data shape: {genetic_df.shape}")
+print(f"Gene expression data shape: {gene_data.shape}")
+print("\nPreview of gene expression data:")
+print(gene_data.head().iloc[:, :5])
+# 1. Normalize gene symbols and save
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data  
+linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path, 
+    is_gene_available=is_gene_available,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Dataset contains gene expression data from blood samples used to study chronic fatigue syndrome"
+)
+
+# 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_Fatigue_Syndrome/code/TCGA.py

@@ -0,0 +1,30 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_Fatigue_Syndrome"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Chronic_Fatigue_Syndrome/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Chronic_Fatigue_Syndrome/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Chronic_Fatigue_Syndrome/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Chronic_Fatigue_Syndrome/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')]
+
+# For cardiovascular disease, there is no directly matching cohort in TCGA
+# Mark the trait as not available and exit
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort="TCGA", 
+    info_path=json_path,
+    is_gene_available=False,
+    is_trait_available=False
+)
+
+raise ValueError(f"No suitable TCGA cohort found for {trait}")