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p3/preprocess/Depression/gene_data/TCGA.csv

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p3/preprocess/Endometriosis/TCGA.csv

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p3/preprocess/Esophageal_Cancer/TCGA.csv

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p3/preprocess/Esophageal_Cancer/clinical_data/GSE75241.csv

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p3/preprocess/Esophageal_Cancer/clinical_data/GSE77790.csv

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p3/preprocess/Esophageal_Cancer/code/GSE100843.py

@@ -0,0 +1,158 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Esophageal_Cancer"
+cohort = "GSE100843"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
+in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE100843"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Esophageal_Cancer/GSE100843.csv"
+out_gene_data_file = "./output/preprocess/3/Esophageal_Cancer/gene_data/GSE100843.csv"
+out_clinical_data_file = "./output/preprocess/3/Esophageal_Cancer/clinical_data/GSE100843.csv"
+json_path = "./output/preprocess/3/Esophageal_Cancer/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
+# From background info, this is a microarray gene expression dataset
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion 
+# Trait (Barrett's esophagus):
+# Available in field 0 as tissue type, where IM indicates disease and NSQ indicates normal
+trait_row = 0
+def convert_trait(value: str) -> int:
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip().lower()
+    if "barrett" in value:
+        return 1  # Disease tissue
+    elif "normal" in value:
+        return 0  # Normal tissue
+    return None
+
+# Age: Not available in characteristics
+age_row = None
+convert_age = None
+
+# Gender: Not available in characteristics  
+gender_row = None
+convert_gender = None
+
+# 3. Save metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# 4. Extract clinical features
+if trait_row is not None:
+    selected_clinical = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the selected features
+    print("Preview of selected clinical features:")
+    print(preview_df(selected_clinical))
+    
+    # Save to CSV
+    selected_clinical.to_csv(out_clinical_data_file)
+# Extract gene expression data
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 probe IDs
+print("First 20 probe IDs:")
+print(genetic_data.index[:20])
+# The probe IDs are numeric identifiers from an Illumina array, not standard gene symbols
+# They need to be mapped to proper human gene symbols
+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)
+# Extract mapping between probe IDs and gene symbols
+mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')
+
+# Apply gene mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Save the gene expression data
+gene_data.to_csv(out_gene_data_file)
+# Read the gene data that was saved in previous step
+gene_data = pd.read_csv(out_gene_data_file, index_col=0)
+
+# 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 and gene data files
+clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+gene_data = pd.read_csv(out_gene_data_file, index_col=0)
+
+# 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, 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 = ("This dataset studies gene expression profiles in esophageal squamous cell carcinoma, "
+        "comparing tumor samples with matched nonmalignant mucosa. The sample size is moderate with paired samples.")
+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/Esophageal_Cancer/code/GSE104958.py

@@ -0,0 +1,147 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Esophageal_Cancer"
+cohort = "GSE104958"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
+in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE104958"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Esophageal_Cancer/GSE104958.csv"
+out_gene_data_file = "./output/preprocess/3/Esophageal_Cancer/gene_data/GSE104958.csv"
+out_clinical_data_file = "./output/preprocess/3/Esophageal_Cancer/clinical_data/GSE104958.csv"
+json_path = "./output/preprocess/3/Esophageal_Cancer/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
+# Based on the background info mentioning "DNA microarray data", this dataset contains gene expression data
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+# Trait (pCR status) is not directly available in sample characteristics,
+# but needs to be inferred from RNA IDs in a later step
+trait_row = None  # Not available in sample characteristics
+age_row = None  # Age data not available
+gender_row = None  # Gender data not available
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    # Get sample ID from string
+    if not isinstance(value, str):
+        return None
+    try:
+        # Extract RNA sample number from identifiers
+        rna_id = int(''.join(filter(str.isdigit, value)))
+        # Check if RNA ID is in pCR group based on background info
+        pcr_samples = [1, 4, 7, 10, 12, 17, 24, 29, 35, 43]
+        return 1 if rna_id in pcr_samples else 0
+    except:
+        return None
+
+# Age and gender conversion functions not needed since data unavailable
+convert_age = None
+convert_gender = None
+
+# 3. Save initial metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical feature extraction skipped since trait_row is None
+# 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])
+# These identifiers appear to be probe IDs from a microarray/RNA-seq platform
+# They are not standard human gene symbols (which would look like BRCA1, TP53, etc)
+# The format A_19_P* suggests these are likely Agilent array probe IDs that need mapping
+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)
+# Extract probe ID and gene symbol mapping from annotation
+mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# Apply gene mapping to convert probe-level measurements to gene expression
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Print dimensions of original and mapped data
+print(f"Original probe data dimensions: {genetic_data.shape}")
+print(f"Mapped gene data dimensions: {gene_data.shape}")
+# 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)
+
+# Create clinical data using sample IDs and convert_trait function
+sample_ids = normalized_gene_data.columns
+clinical_data = pd.DataFrame(index=['Esophageal_Cancer'])
+clinical_data[sample_ids] = [convert_trait(id) for id in sample_ids]
+clinical_data.to_csv(out_clinical_data_file)
+
+# Link clinical and genetic data 
+linked_data = geo_link_clinical_genetic_data(clinical_data, normalized_gene_data)
+
+# Handle missing values systematically
+linked_data = handle_missing_values(linked_data, 'Esophageal_Cancer')
+
+# Detect bias in trait and demographic features
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, 'Esophageal_Cancer')
+
+# Validate data quality and save cohort info
+note = ("This dataset studies gene expression related to pathological complete response (pCR) " 
+        "after neoadjuvant chemotherapy in esophageal cancer. The trait information was derived "
+        "from RNA sample IDs mentioned in the background information.")
+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/Esophageal_Cancer/code/GSE107754.py

@@ -0,0 +1,167 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Esophageal_Cancer"
+cohort = "GSE107754"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
+in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE107754"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Esophageal_Cancer/GSE107754.csv"
+out_gene_data_file = "./output/preprocess/3/Esophageal_Cancer/gene_data/GSE107754.csv"
+out_clinical_data_file = "./output/preprocess/3/Esophageal_Cancer/clinical_data/GSE107754.csv"
+json_path = "./output/preprocess/3/Esophageal_Cancer/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
+# The background info mentions "whole human genome gene expression microarrays"
+is_gene_available = True
+
+# 2.1 Data Availability
+# Trait (cancer) info is in key 2 under "tissue: ..."
+trait_row = 2
+# Age is not available in the sample characteristics
+age_row = None  
+# Gender is in key 0
+gender_row = 0
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert tissue type to binary indicating if it's esophageal cancer"""
+    if not value or ':' not in value:
+        return None
+    tissue = value.split(':')[1].strip().lower()
+    # Return 1 for esophageal cancer, 0 for other cancers
+    return 1 if 'esophagus cancer' in tissue else 0
+
+def convert_age(value: str) -> float:
+    """Convert age string to float"""
+    # No age data available
+    return None
+
+def convert_gender(value: str) -> int:
+    """Convert gender to binary (0=female, 1=male)"""
+    if not value or ':' not in value:
+        return None
+    gender = value.split(':')[1].strip().lower()
+    if gender == 'female':
+        return 0
+    elif gender == 'male':
+        return 1
+    return None
+
+# 3. Save initial validation info
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=trait_row is not None
+)
+
+# 4. Extract clinical features
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age, 
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the extracted features
+    preview = preview_df(clinical_features)
+    print("Preview of clinical features:")
+    print(preview)
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 probe IDs
+print("First 20 probe IDs:")
+print(genetic_data.index[:20])
+# These identifiers are Agilent probe IDs, not HGNC gene symbols
+# They follow the typical Agilent format "A_23_P######" 
+# Therefore mapping to gene symbols is required
+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)
+# 1. The 'ID' column in gene_annotation matches the probe IDs in genetic_data
+# The 'GENE_SYMBOL' column contains the corresponding gene symbols
+probe_col = 'ID'
+symbol_col = 'GENE_SYMBOL'
+
+# 2. Get gene mapping dataframe 
+gene_mapping = get_gene_mapping(gene_annotation, probe_col, symbol_col)
+
+# 3. Apply gene mapping to convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(genetic_data, gene_mapping)
+# 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)
+
+# 2. Link clinical and genetic data
+clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)
+
+# 3. Handle missing values systematically 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Detect bias in trait and demographic features, remove biased demographic features
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate data quality and save cohort info
+note = ("This dataset studies gene expression profiles in esophageal squamous cell carcinoma, "
+        "comparing tumor samples with matched nonmalignant mucosa. The sample size is moderate with paired samples.")
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True, 
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note=note
+)
+
+# 6. Save 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/Esophageal_Cancer/code/GSE131027.py

@@ -0,0 +1,152 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Esophageal_Cancer"
+cohort = "GSE131027"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
+in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE131027"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Esophageal_Cancer/GSE131027.csv"
+out_gene_data_file = "./output/preprocess/3/Esophageal_Cancer/gene_data/GSE131027.csv"
+out_clinical_data_file = "./output/preprocess/3/Esophageal_Cancer/clinical_data/GSE131027.csv"
+json_path = "./output/preprocess/3/Esophageal_Cancer/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
+is_gene_available = True  # Based on background info showing expression features analysis
+
+# 2. Variable Availability and Type Conversion
+# 2.1 Data Availability
+trait_row = 1  # Cancer type is recorded in row 1
+age_row = None  # Age data not available 
+gender_row = None  # Gender data not available
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert cancer type to binary for esophageal cancer"""
+    if pd.isna(value) or ':' not in value:
+        return None
+    cancer_type = value.split(': ')[1].lower()
+    # Match variations of esophageal cancer spelling
+    if 'oesophageal' in cancer_type or 'esophageal' in cancer_type:
+        return 1
+    return 0
+
+def convert_age(value: str) -> float:
+    return None  # Not used since age data unavailable
+
+def convert_gender(value: str) -> int:
+    return None  # Not used since gender data unavailable
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the processed clinical features
+    print("Preview of clinical features:")
+    print(preview_df(clinical_features))
+    
+    # Save to CSV
+    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])
+# Based on the probe IDs shown (e.g., '1007_s_at', '1053_at'), these are Affymetrix probe IDs
+# and not human gene symbols. They need to be mapped to standard gene symbols for 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)
+# The gene identifiers are in the 'ID' column of gene annotation data, which matches 
+# the probe IDs in gene expression data. Gene symbols are in the 'Gene Symbol' column.
+gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# Apply the gene mapping to convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(genetic_data, gene_mapping)
+
+# Preview the first few rows and columns of the gene data
+print("\nPreview of gene expression data:")
+print(preview_df(gene_data))
+# 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)
+
+# 2. Link clinical and genetic data 
+linked_data = geo_link_clinical_genetic_data(clinical_data, normalized_gene_data)
+
+# 3. Handle missing values systematically
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Detect bias in trait and demographic features, remove biased demographic features
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate data quality and save cohort info
+note = ("This dataset studies gene expression profiles in esophageal squamous cell carcinoma, "
+        "comparing tumor samples with matched nonmalignant mucosa. The sample size is moderate with paired samples.")
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort, 
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note=note
+)
+
+# 6. Save 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/Esophageal_Cancer/code/GSE156915.py

@@ -0,0 +1,144 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Esophageal_Cancer"
+cohort = "GSE156915"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
+in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE156915"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Esophageal_Cancer/GSE156915.csv"
+out_gene_data_file = "./output/preprocess/3/Esophageal_Cancer/gene_data/GSE156915.csv"
+out_clinical_data_file = "./output/preprocess/3/Esophageal_Cancer/clinical_data/GSE156915.csv"
+json_path = "./output/preprocess/3/Esophageal_Cancer/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
+# From the background info, we can see this is a gene expression study investigating
+# DNA damage immune response in colorectal cancer
+is_gene_available = True
+
+# 2.1 Data Availability 
+# Looking at the sample characteristics:
+# - Row 0 shows DDIR status which indicates DNA damage response status
+trait_row = 0  
+# Age and gender info not available in sample characteristics
+age_row = None
+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
+    val = x.split(':')[1].strip()
+    # DDIR NEG = control = 0, DDIR POS = case = 1
+    if 'NEG' in val:
+        return 0
+    elif 'POS' in val:
+        return 1
+    return None
+
+def convert_age(x):
+    # Not available
+    return None
+
+def convert_gender(x):
+    # Not available 
+    return None
+
+# 3. Save Initial Metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(is_final=False, 
+                                cohort=cohort,
+                                info_path=json_path,
+                                is_gene_available=is_gene_available,
+                                is_trait_available=is_trait_available)
+
+# 4. Extract Clinical Features
+if trait_row is not None:
+    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 extracted features
+    preview = preview_df(clinical_df)
+    print("Preview of clinical features:")
+    print(preview)
+    
+    # Save to CSV
+    clinical_df.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])
+# These appear to be human gene symbols, with some RNA genes and pseudogenes
+# The identifiers match official HGNC gene symbols and nomenclature patterns
+requires_gene_mapping = False
+# 1. Normalize gene symbols and save normalized gene data
+normalized_gene_data = normalize_gene_symbols_in_index(genetic_data) 
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Read the processed clinical and gene data files
+clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+gene_data = pd.read_csv(out_gene_data_file, index_col=0)
+
+# 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, 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 = ("This dataset studies gene expression profiles in esophageal squamous cell carcinoma, "
+        "comparing tumor samples with matched nonmalignant mucosa. The sample size is moderate with paired samples.")
+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/Esophageal_Cancer/code/GSE218109.py

@@ -0,0 +1,187 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Esophageal_Cancer"
+cohort = "GSE218109"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
+in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE218109"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Esophageal_Cancer/GSE218109.csv"
+out_gene_data_file = "./output/preprocess/3/Esophageal_Cancer/gene_data/GSE218109.csv"
+out_clinical_data_file = "./output/preprocess/3/Esophageal_Cancer/clinical_data/GSE218109.csv"
+json_path = "./output/preprocess/3/Esophageal_Cancer/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
+is_gene_available = True  # Based on Series_title and Series_summary, this contains transcriptional profiling data
+
+# 2.1 Data Availability
+trait_row = 5  # p53 status indicates cancer condition
+age_row = 1    # Age data available
+gender_row = 0 # Sex data available 
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    if pd.isna(value):
+        return None
+    value = value.split(': ')[-1].lower()
+    if 'ns+' in value or 'nuclear-stabilized' in value:
+        return 1
+    elif 'ns-' in value or 'unstable' in value:
+        return 0
+    return None
+
+def convert_age(value):
+    if pd.isna(value):
+        return None
+    try:
+        return float(value.split(': ')[1])
+    except:
+        return None
+
+def convert_gender(value):
+    if pd.isna(value):
+        return None
+    value = value.split(': ')[1].upper()
+    if value == 'F':
+        return 0
+    elif value == 'M':
+        return 1
+    return None
+
+# 3. Save Initial Metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# 4. Extract Clinical Features
+selected_clinical = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Preview the extracted features
+print(preview_df(selected_clinical))
+
+# Save clinical data
+selected_clinical.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])
+# These probes appear to be numerical identifiers rather than standard human gene symbols
+# Human gene symbols typically follow a specific format (e.g., BRCA1, TP53, IL6)
+# Therefore gene mapping will be required
+requires_gene_mapping = True
+# Since the SOFT file doesn't contain usable annotation data, load platform annotation from external source
+annotation_file = "./metadata/GPL4133.tsv"
+
+# Load and preview the platform annotation
+gene_annotation = pd.read_csv(annotation_file, sep='\t', comment='#')
+
+# Show column names and preview the data
+print("Platform Annotation Preview:")
+print("-" * 50)
+print(f"Number of rows: {len(gene_annotation)}")
+print(f"\nColumns:")
+for col in gene_annotation.columns:
+    print(col)
+print("\nFirst few rows:")
+print(preview_df(gene_annotation))
+# Extract gene annotation from SOFT file
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Preview the data
+print("Gene Annotation Preview:") 
+print("-" * 50)
+print(f"Number of rows: {len(gene_annotation)}")
+print(f"\nColumns:")
+for col in gene_annotation.columns:
+    print(col)
+print("\nFirst few rows:")
+print(preview_df(gene_annotation))
+# 1. The 'ID' column in gene annotation matches the gene expression data indices
+# The 'GENE_SYMBOL' column contains the gene symbols we want to map to
+mapping_df = get_gene_mapping(gene_annotation, 'ID', 'GENE_SYMBOL')
+
+# 2. Apply gene mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Preview results
+print("Gene Expression Data Preview:")
+print("-" * 50)
+print(f"Number of genes: {len(gene_data)}")
+print("\nFirst few rows:")
+print(preview_df(gene_data))
+# 1. Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(genetic_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data 
+clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)
+
+# 3. Handle missing values systematically
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Detect bias in trait and demographic features
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate data quality and save cohort info
+note = ("This dataset studies gene expression profiles in esophageal squamous cell carcinoma tumors, "
+        "comparing nuclear-stabilized p53 (NS+) versus unstable p53 (NS-) protein tumor samples.")
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)
+else:
+    print(f"Dataset {cohort} did not pass quality validation and will not be saved.")

p3/preprocess/Esophageal_Cancer/code/GSE55857.py

@@ -0,0 +1,99 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Esophageal_Cancer"
+cohort = "GSE55857"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
+in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE55857"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Esophageal_Cancer/GSE55857.csv"
+out_gene_data_file = "./output/preprocess/3/Esophageal_Cancer/gene_data/GSE55857.csv"
+out_clinical_data_file = "./output/preprocess/3/Esophageal_Cancer/clinical_data/GSE55857.csv"
+json_path = "./output/preprocess/3/Esophageal_Cancer/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
+# This is a microRNA dataset (SuperSeries) studying small non-coding RNAs
+# MicroRNA data is not suitable for our gene expression analysis
+is_gene_available = False
+
+# 2. Clinical Data Variables Analysis
+# 2.1 Data Availability
+# Trait (cancer status) is available in row 1 as "tissue" field
+# Age and gender are not recorded
+trait_row = 1
+age_row = None 
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    """Convert tissue type to binary cancer status"""
+    if not isinstance(value, str):
+        return None
+    value = value.split(": ")[-1].lower().strip()
+    if "tumor" in value:
+        return 1
+    elif "normal" in value:
+        return 0
+    return None
+
+def convert_age(value):
+    """Convert age value - not used"""
+    return None
+
+def convert_gender(value):
+    """Convert gender value - not used"""
+    return None
+
+# 3. Save Metadata
+# trait_row is not None, so trait data is available
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+
+# 4. Extract Clinical Features
+# Since trait_row is not None, we extract clinical features
+clinical_features = geo_select_clinical_features(clinical_data,
+                                               trait=trait,
+                                               trait_row=trait_row,
+                                               convert_trait=convert_trait,
+                                               age_row=age_row,
+                                               convert_age=convert_age,
+                                               gender_row=gender_row, 
+                                               convert_gender=convert_gender)
+
+# Preview the extracted features
+preview_results = preview_df(clinical_features)
+print("Preview of clinical features:")
+print(preview_results)
+
+# Save clinical features
+clinical_features.to_csv(out_clinical_data_file)

p3/preprocess/Esophageal_Cancer/code/GSE66258.py

@@ -0,0 +1,90 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Esophageal_Cancer"
+cohort = "GSE66258"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
+in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE66258"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Esophageal_Cancer/GSE66258.csv"
+out_gene_data_file = "./output/preprocess/3/Esophageal_Cancer/gene_data/GSE66258.csv"
+out_clinical_data_file = "./output/preprocess/3/Esophageal_Cancer/clinical_data/GSE66258.csv"
+json_path = "./output/preprocess/3/Esophageal_Cancer/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
+# Based on the series description, this is a microRNA dataset, not suitable for gene expression analysis
+is_gene_available = False
+
+# 2.1 Data Availability
+# From sample characteristics:
+# - trait: Row 0 shows all samples are ESCC tumor tissue
+# - age: Not available 
+# - gender: Not available
+trait_row = 0
+age_row = None 
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert ESCC tumor status to binary"""
+    if 'esophageal squamous cell carcinoma' in value.lower():
+        return 1
+    return None
+
+def convert_age(value: str) -> float:
+    """Convert age to float"""
+    return None # Not used since age data not available
+
+def convert_gender(value: str) -> int:
+    """Convert gender to binary"""
+    return None # Not used since gender data not 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. Extract clinical features since 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 and save
+print("Clinical data preview:")
+print(preview_df(selected_clinical))
+
+# Save clinical data
+selected_clinical.to_csv(out_clinical_data_file)

p3/preprocess/Esophageal_Cancer/code/GSE75241.py

@@ -0,0 +1,160 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Esophageal_Cancer"
+cohort = "GSE75241"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
+in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE75241"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Esophageal_Cancer/GSE75241.csv"
+out_gene_data_file = "./output/preprocess/3/Esophageal_Cancer/gene_data/GSE75241.csv"
+out_clinical_data_file = "./output/preprocess/3/Esophageal_Cancer/clinical_data/GSE75241.csv"
+json_path = "./output/preprocess/3/Esophageal_Cancer/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
+# From title and summary, this is a gene expression profile dataset
+is_gene_available = True
+
+# 2.1 Data Availability
+# trait (cancer status) is in row 1 (tissue type)
+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(value):
+    if pd.isna(value):
+        return None
+    # Extract value after colon and strip whitespace
+    value = value.split(':')[1].strip()
+    # Convert to binary: nonmalignant (0) vs tumor (1)
+    if 'nonmalignant' in value.lower():
+        return 0
+    elif 'tumor' in value.lower():
+        return 1
+    return None
+
+def convert_age(value):
+    return None # Not used since age data not available
+
+def convert_gender(value):
+    return None # Not used since gender data not available
+
+# 3. Save Metadata
+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 extract 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_result = preview_df(selected_clinical)
+
+# Save clinical data
+selected_clinical.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])
+# These probes appear to be numerical IDs from Illumina platform
+# rather than standardized gene symbols like "BRCA1", "TP53" etc.
+# Therefore mapping to gene symbols will be required
+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)
+# 'ID' in gene_annotation matches the probe IDs in genetic_data
+# 'gene_assignment' contains gene symbol information
+mapping_data = get_gene_mapping(gene_annotation, 'ID', 'gene_assignment')
+
+# Apply gene mapping to get gene expression data 
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Save gene data
+gene_data.to_csv(out_gene_data_file)
+
+# Preview the gene data
+preview_result = preview_df(gene_data)
+# Read the processed clinical and gene data files 
+clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+gene_data = pd.read_csv(out_gene_data_file, index_col=0)  # Already normalized in step 6
+
+# 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, 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 = ("This dataset studies gene expression profiles in esophageal squamous cell carcinoma, "
+        "comparing tumor samples with matched nonmalignant mucosa. The sample size is moderate with paired samples.")
+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/Esophageal_Cancer/code/GSE77790.py

@@ -0,0 +1,227 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Esophageal_Cancer"
+cohort = "GSE77790"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
+in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE77790"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Esophageal_Cancer/GSE77790.csv"
+out_gene_data_file = "./output/preprocess/3/Esophageal_Cancer/gene_data/GSE77790.csv"
+out_clinical_data_file = "./output/preprocess/3/Esophageal_Cancer/clinical_data/GSE77790.csv"
+json_path = "./output/preprocess/3/Esophageal_Cancer/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()
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Create trait column from cell line information
+cell_lines = clinical_data.iloc[0]
+clinical_df = pd.DataFrame(index=cell_lines.index)
+clinical_df[trait] = cell_lines.str.contains('TE8|TE9').astype(int)
+
+# Normalize gene symbols and save to file
+normalized_gene_data = normalize_gene_symbols_in_index(genetic_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Save clinical data to file
+os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) 
+clinical_df.to_csv(out_clinical_data_file)
+
+# Link clinical and genetic 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 = ("This dataset studies gene expression changes in cancer cell lines after miRNA/siRNA treatments. " 
+        "Data quality evaluation indicates the trait distribution is biased.")
+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:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)
+else:
+    print(f"Dataset {cohort} did not pass quality validation and will not be saved.")
+# Set initial availability flags
+is_gene_available = False  # Cannot determine without data
+
+# No data available yet
+trait_row = None
+age_row = None 
+gender_row = None
+
+def convert_trait(x):
+    return None
+    
+def convert_age(x):
+    return None
+
+def convert_gender(x):
+    return None
+
+# 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=False  # Since trait_row is None
+)
+# Check gene expression data availability (GPL570 platform indicates gene expression data)
+is_gene_available = True
+
+# Data availability from sample characteristics
+trait_row = 2  # "source name: esophageal tumor or paired normal"
+age_row = 9    # "age (years): [numeric values]"
+gender_row = 8 # "sex: male/female"
+
+def convert_trait(val: str) -> Optional[int]:
+    if val is None:
+        return None
+    val = val.split(":")[-1].strip().lower()
+    if "tumor" in val:
+        return 1
+    elif "normal" in val:
+        return 0
+    return None
+
+def convert_age(val: str) -> Optional[float]:
+    if val is None:
+        return None
+    val = val.split(":")[-1].strip()
+    try:
+        return float(val)
+    except:
+        return None
+
+def convert_gender(val: str) -> Optional[int]:
+    if val is None:
+        return None
+    val = val.split(":")[-1].strip().lower()
+    if "female" in val:
+        return 0
+    elif "male" in val:
+        return 1
+    return None
+
+# 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=(trait_row is not None))
+# Extract gene expression data
+genetic_data = get_genetic_data(matrix_file_path)
+genetic_data.index = genetic_data.index.astype(str)  # Convert probe IDs to strings
+
+# Print first 20 probe IDs
+print("First 20 probe IDs:")
+print(genetic_data.index[:20])
+# The indices appear to be just sequential numbers rather than any meaningful gene identifiers
+# This indicates the gene identifiers need to be mapped to proper gene symbols
+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)
+# Extract probe-gene mapping columns
+mapping_data = get_gene_mapping(gene_annotation, 'ID', 'GENE_SYMBOL')
+
+# Apply gene mapping to convert probe data to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+# Define clinical data parameters based on sample characteristics
+trait_row = 1  # cell type row
+def convert_trait(x):
+    if not isinstance(x, str):
+        return None
+    x = x.lower()
+    return 1 if 'esophageal cancer' in x else 0
+
+# Extract clinical features 
+clinical_df = geo_select_clinical_features(
+    clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait
+)
+
+# Normalize gene symbols
+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)
+
+# 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, 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 = ("This dataset studies gene expression in esophageal cancer cell lines. "
+        "Data quality evaluation indicates potential trait distribution bias.")
+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:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)
+else:
+    print(f"Dataset {cohort} did not pass quality validation and will not be saved.")

p3/preprocess/Esophageal_Cancer/code/TCGA.py

@@ -0,0 +1,115 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Esophageal_Cancer"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Esophageal_Cancer/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Esophageal_Cancer/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Esophageal_Cancer/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Esophageal_Cancer/cohort_info.json"
+
+# Select the ESCA (Esophageal Cancer) cohort directory
+cohort_dir = os.path.join(tcga_root_dir, 'TCGA_Esophageal_Cancer_(ESCA)')
+
+# Get paths for clinical and genetic data files  
+clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_dir)
+
+# Load the data files
+clinical_df = pd.read_csv(clinical_file, index_col=0, sep='\t')
+genetic_df = pd.read_csv(genetic_file, index_col=0, sep='\t')
+
+# Print clinical data columns for review
+print("Clinical data columns:")
+print(clinical_df.columns.tolist())
+# 1. Identify candidate columns for age and gender
+candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'age_began_smoking_in_years', 'days_to_birth']
+candidate_gender_cols = ['gender']
+
+# 2. Preview the data
+# First check available directories
+print("Available directories in TCGA root:")
+print(os.listdir(tcga_root_dir))
+
+# Get clinical data path using actual directory structure
+cohort_dir = os.path.join(tcga_root_dir, "TCGA-ESCA") 
+clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir)
+
+# Read clinical data 
+clinical_df = pd.read_csv(clinical_file_path, index_col=0)
+
+# Extract candidate columns
+age_data = clinical_df[candidate_age_cols]
+gender_data = clinical_df[candidate_gender_cols]
+
+# Preview data as dictionaries
+print("\nAge columns preview:")
+print(preview_df(age_data))
+print("\nGender columns preview:") 
+print(preview_df(gender_data))
+# Select the ESCA (Esophageal Cancer) cohort directory
+cohort_dir = os.path.join(tcga_root_dir, 'TCGA_Esophageal_Cancer_(ESCA)')
+
+# Get paths for clinical and genetic data files  
+clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_dir)
+
+# Load the data files
+clinical_df = pd.read_csv(clinical_file, index_col=0, sep='\t')
+genetic_df = pd.read_csv(genetic_file, index_col=0, sep='\t')
+
+# Print clinical data columns for review
+print("Clinical data columns:")
+print(clinical_df.columns.tolist())
+# Check values in candidate columns 
+age_col = "age_at_initial_pathologic_diagnosis"
+
+# Gender column is straightforward
+gender_col = "gender"
+
+# Print chosen columns
+print(f"Selected age column: {age_col}")
+print(f"Selected gender column: {gender_col}")
+# Carry over the selected demographic columns
+age_col = "age_at_initial_pathologic_diagnosis"
+gender_col = "gender"
+
+# 1. Extract and standardize clinical features 
+clinical_df = tcga_select_clinical_features(clinical_df, trait, age_col, gender_col)
+
+# 2. Normalize gene expression data
+normalized_genetic_df = normalize_gene_symbols_in_index(genetic_df)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_genetic_df.to_csv(out_gene_data_file)
+
+# 3. Link clinical and genetic data
+linked_data = pd.merge(normalized_genetic_df.T, clinical_df, left_index=True, right_index=True)
+
+# Add trait labels based on sample IDs
+linked_data[trait] = linked_data.index.map(tcga_convert_trait)
+
+# 4. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 5. Check for bias in features
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 6. Validate and save cohort info 
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort="TCGA_Esophageal_Cancer_(ESCA)",
+    info_path=json_path,
+    is_gene_available=len(normalized_genetic_df.columns) > 0,
+    is_trait_available=trait in linked_data.columns,
+    is_biased=is_biased,
+    df=linked_data,
+    note="Data from TCGA Esophageal Cancer cohort"
+)
+
+# 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/Esophageal_Cancer/gene_data/GSE100843.csv

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+oid sha256:6d9abb76c462b0a0290fb8bd1e665e3345055fcabe727f8ddb92919532d6b26c
+size 28273352

p3/preprocess/Esophageal_Cancer/gene_data/GSE104958.csv

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+size 11772704

p3/preprocess/Esophageal_Cancer/gene_data/GSE107754.csv

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+size 19822703

p3/preprocess/Esophageal_Cancer/gene_data/GSE131027.csv

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+oid sha256:a8582008fe6fc5b90879aea7d12ca4ea58cc0bbe03209acb333425d229be6e33
+size 24379939

p3/preprocess/Esophageal_Cancer/gene_data/GSE218109.csv

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p3/preprocess/Esophageal_Cancer/gene_data/GSE75241.csv

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+version https://git-lfs.github.com/spec/v1
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+size 18737528

p3/preprocess/Esophageal_Cancer/gene_data/GSE77790.csv

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p3/preprocess/Esophageal_Cancer/gene_data/TCGA.csv

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p3/preprocess/Essential_Thrombocythemia/GSE159514.csv

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p3/preprocess/Essential_Thrombocythemia/GSE57793.csv

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+size 17564694

p3/preprocess/Essential_Thrombocythemia/clinical_data/GSE103176.csv

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p3/preprocess/Essential_Thrombocythemia/clinical_data/GSE103237.csv

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p3/preprocess/Essential_Thrombocythemia/clinical_data/GSE12295.csv

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p3/preprocess/Essential_Thrombocythemia/clinical_data/GSE159514.csv

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p3/preprocess/Essential_Thrombocythemia/clinical_data/GSE174060.csv

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p3/preprocess/Essential_Thrombocythemia/clinical_data/GSE55976.csv

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p3/preprocess/Essential_Thrombocythemia/clinical_data/GSE57793.csv

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p3/preprocess/Essential_Thrombocythemia/clinical_data/GSE61629.csv

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p3/preprocess/Essential_Thrombocythemia/code/GSE103176.py

@@ -0,0 +1,232 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Essential_Thrombocythemia"
+cohort = "GSE103176"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Essential_Thrombocythemia"
+in_cohort_dir = "../DATA/GEO/Essential_Thrombocythemia/GSE103176"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Essential_Thrombocythemia/GSE103176.csv"
+out_gene_data_file = "./output/preprocess/3/Essential_Thrombocythemia/gene_data/GSE103176.csv"
+out_clinical_data_file = "./output/preprocess/3/Essential_Thrombocythemia/clinical_data/GSE103176.csv"
+json_path = "./output/preprocess/3/Essential_Thrombocythemia/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
+# Series title mentions "Gene... expression profiles", so gene data is available
+is_gene_available = True
+
+# 2. Variable Availability and Data Row Identification
+# 2.1 Data Type Selection and Data Row Identification
+# Trait (ET vs Control) can be found in row 3 under 'disease'
+trait_row = 3
+
+# Age is not provided in the characteristics 
+age_row = None
+
+# Gender is in row 1 under 'Sex'
+gender_row = 1
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert disease status to binary (0: control, 1: ET)"""
+    if pd.isna(value):
+        return None
+    value = value.split(': ')[-1].strip().lower()
+    if 'et' in value:
+        return 1
+    elif 'healthy control' in value:
+        return 0
+    return None
+
+def convert_age(value: str) -> float:
+    """Convert age to float - not used since age not available"""
+    return None
+
+def convert_gender(value: str) -> int:
+    """Convert gender to binary (0: female, 1: male)"""
+    if pd.isna(value):
+        return None
+    value = value.split(': ')[-1].strip().lower()
+    if value == 'f':
+        return 0
+    elif value == 'm':
+        return 1
+    return None
+
+# 3. Save Metadata  
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Extract Clinical Features
+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_result = preview_df(selected_clinical)
+    print("Preview of extracted clinical features:")
+    print(preview_result)
+    
+    # Save to CSV
+    selected_clinical.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])
+# The identifiers appear to be probe IDs from a microarray platform, not standard gene symbols
+# They need to be mapped to human gene symbols for 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)
+# Get unique probe IDs from gene expression data to understand the format
+probe_examples = genetic_data.index[:5].tolist()
+
+# Extract the complete platform annotation table
+gene_annotation = get_gene_annotation(soft_file_path, prefixes=['!platform_table_begin', '!platform_table_end'])
+
+# Extract columns for mapping and rename them
+mapping_data = get_gene_mapping(gene_annotation, prob_col='ID_REF', gene_col='Gene Symbol')
+
+# Apply mapping to convert probe-level data to gene-level data  
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Preview the result
+print("\nExample probe IDs from expression data:")
+print(probe_examples)
+
+print("\nFirst 5 rows of mapping data:")
+print(mapping_data.head())
+
+print("\nFirst 5 rows and 3 columns of mapped gene expression data:")
+print(gene_data.iloc[:5, :3])
+# Get unique probe IDs from gene expression data to understand the format
+probe_examples = genetic_data.index[:5].tolist()
+
+# Extract the complete platform annotation table
+gene_annotation = get_gene_annotation(soft_file_path)
+print("\nRaw annotation data columns:")
+print(gene_annotation.columns.tolist())
+
+# Based on column names in the raw data, we can see that probe IDs are in the 'ID' column
+# and gene symbols are in the 'Gene Symbol' column
+mapping_data = pd.DataFrame({
+    'ID': gene_annotation['ID'],
+    'Gene': gene_annotation['Gene Symbol']
+})
+
+# Fix any NaN values that might cause mapping issues
+mapping_data = mapping_data.dropna()
+
+# Apply mapping to convert probe-level data to gene-level data  
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Save gene expression data
+gene_data.to_csv(out_gene_data_file)
+
+# Preview results
+print("\nExample probe IDs from expression data:")
+print(probe_examples)
+
+print("\nFirst 5 rows of mapping data:")
+print(mapping_data.head())
+
+print("\nFirst 5 rows and 3 columns of mapped gene expression data:")
+print(gene_data.iloc[:5, :3])
+# Check if genetic data is empty
+if genetic_data.empty:
+    print("Gene expression data is empty - cannot proceed with linking and analysis")
+    # Record failure in cohort info
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort, 
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=True,
+        is_biased=None,
+        df=None,
+        note="Gene mapping failed - unable to match probe IDs between expression and annotation data"
+    )
+else:
+    # 1. Normalize gene symbols and save normalized gene data 
+    normalized_gene_data = normalize_gene_symbols_in_index(genetic_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/Essential_Thrombocythemia/code/GSE103237.py

@@ -0,0 +1,172 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Essential_Thrombocythemia"
+cohort = "GSE103237"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Essential_Thrombocythemia"
+in_cohort_dir = "../DATA/GEO/Essential_Thrombocythemia/GSE103237"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Essential_Thrombocythemia/GSE103237.csv"
+out_gene_data_file = "./output/preprocess/3/Essential_Thrombocythemia/gene_data/GSE103237.csv"
+out_clinical_data_file = "./output/preprocess/3/Essential_Thrombocythemia/clinical_data/GSE103237.csv"
+json_path = "./output/preprocess/3/Essential_Thrombocythemia/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, as indicated in the background info
+# "Gene expression profile (GEP) and miRNA expression profile (miEP) were performed..."
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+# Trait (Essential Thrombocythemia) can be inferred from disease field (row 3)
+trait_row = 3
+
+# Gender is available in row 1
+gender_row = 1
+
+# No age information available
+age_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert disease status to binary (0: control, 1: ET)"""
+    if not value or 'disease: ' not in value:
+        return None
+    value = value.split('disease: ')[1].strip().lower()
+    if value == 'et':
+        return 1
+    elif value == 'healthy control':
+        return 0
+    return None
+
+def convert_gender(value: str) -> int:
+    """Convert gender to binary (0: female, 1: male)"""
+    if not value or 'Sex: ' not in value:
+        return None
+    value = value.split('Sex: ')[1].strip().lower()
+    if value == 'f':
+        return 0
+    elif value == 'm':
+        return 1
+    return None
+
+def convert_age(value: str) -> float:
+    """Placeholder function since age is not available"""
+    return None
+
+# 3. Save Metadata
+# Initial filtering based on gene and trait 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
+)
+
+# 4. Clinical Feature Extraction
+if 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,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the extracted features
+    print("Preview of extracted clinical features:")
+    print(preview_df(clinical_df))
+    
+    # Save clinical data
+    clinical_df.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])
+# These identifiers look like Affymetrix probe IDs (e.g. 11715100_at format)
+# Rather than standard human gene symbols (e.g. BRCA1)
+# They will need to be mapped to gene symbols
+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)
+# Extract probe ID and gene symbol mapping from annotation data
+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(genetic_data, mapping_df)
+
+# Preview the results
+print("First 5 genes and their expression values:")
+print(preview_df(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.")