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p3/preprocess/Anxiety_disorder/gene_data/GSE119995.csv

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p3/preprocess/Asthma/code/GSE123086.py

@@ -0,0 +1,240 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Asthma"
+cohort = "GSE123086"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Asthma"
+in_cohort_dir = "../DATA/GEO/Asthma/GSE123086"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Asthma/GSE123086.csv"
+out_gene_data_file = "./output/preprocess/3/Asthma/gene_data/GSE123086.csv"
+out_clinical_data_file = "./output/preprocess/3/Asthma/clinical_data/GSE123086.csv"
+json_path = "./output/preprocess/3/Asthma/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Yes, this dataset contains gene expression data from CD4+ T cells using microarray
+is_gene_available = True
+
+# 2.1 Data Availability
+# Trait is in Feature 1 (primary diagnosis), values include ASTHMA and others
+trait_row = 1
+
+# Gender is in Feature 2 and 3 (Sex appears in both)
+gender_row = 2
+
+# Age appears in Features 3 and 4 
+age_row = 3
+
+# 2.2 Data Type Conversion Functions
+
+def convert_trait(value: str) -> int:
+    """Convert trait values to binary (0 for control, 1 for case)"""
+    if not isinstance(value, str):
+        return None
+    value = value.split(': ')[-1].upper()
+    if 'ASTHMA' in value:
+        return 1
+    elif value == 'HEALTHY_CONTROL':
+        return 0
+    return None
+
+def convert_age(value: str) -> float:
+    """Convert age values to continuous numeric"""
+    if not isinstance(value, str):
+        return None
+    if not value.startswith('age: '):
+        return None
+    try:
+        return float(value.split(': ')[-1])
+    except:
+        return None
+
+def convert_gender(value: str) -> int:
+    """Convert gender values to binary (0 for female, 1 for male)"""
+    if not isinstance(value, str):
+        return None
+    if not value.startswith('Sex: '):
+        return None
+    value = value.split(': ')[-1].upper()
+    if value == 'FEMALE':
+        return 0
+    elif value == 'MALE':
+        return 1
+    return None
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    print("Preview of extracted clinical features:")
+    print(preview_df(clinical_features))
+    
+    # Save clinical features
+    clinical_features.to_csv(out_clinical_data_file)
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug 
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+requires_gene_mapping = True
+# Get file paths
+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 annotation dataframe structure
+print("Gene Annotation Preview:")
+print("Column names:", gene_annotation.columns.tolist())
+print("\nFirst few rows as dictionary:")
+print(preview_df(gene_annotation))
+# The IDs in gene annotation correspond to the row IDs in gene expression data
+# The ENTREZ_GENE_ID contains IDs that we first map to
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='ENTREZ_GENE_ID')
+
+# Apply the mapping to convert probe expression to gene expression
+entrez_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Convert Entrez IDs to gene symbols using NCBI synonym database
+gene_data = normalize_gene_symbols_in_index(entrez_data)
+
+# Save the gene expression data 
+gene_data.to_csv(out_gene_data_file)
+# Load previously saved data
+gene_data = pd.read_csv(out_gene_data_file, index_col=0)  
+clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# Inspect data alignment
+print("Clinical data shape:", clinical_data.shape)
+print("Gene data shape:", gene_data.shape)
+print("Clinical data columns:", clinical_data.columns[:5])
+print("Gene data columns:", gene_data.columns[:5])
+
+# Transpose data to get samples in rows, genes in columns
+clinical_data = clinical_data.T
+gene_data = gene_data.T
+linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, 'Asthma')
+
+# 4. Evaluate bias
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, 'Asthma')
+
+# 5. Validate and save cohort info
+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="Dataset contains gene expression data from CD4+ T cells comparing asthma patients with healthy controls."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)
+# Load previously saved data
+gene_data = pd.read_csv(out_gene_data_file, index_col=0)
+clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# Verify data validity
+if gene_data.empty:
+    print("Gene expression data is empty. Previous preprocessing steps likely failed.")
+    is_gene_available = False
+    is_trait_available = True
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=is_gene_available,
+        is_trait_available=is_trait_available,
+        is_biased=False,  # Set a definite value
+        df=clinical_data,  # Provide the clinical data
+        note="Gene expression data processing failed, resulting in empty data."
+    )
+else:
+    # Link clinical and genetic data 
+    linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
+
+    # Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # Evaluate bias
+    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # Validate and save cohort info
+    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="Dataset contains gene expression data from CD4+ T cells comparing asthma patients with healthy controls."
+    )
+
+    # Save linked data if usable
+    if is_usable:
+        linked_data.to_csv(out_data_file)

p3/preprocess/Asthma/code/GSE123088.py

@@ -0,0 +1,283 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Asthma"
+cohort = "GSE123088"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Asthma"
+in_cohort_dir = "../DATA/GEO/Asthma/GSE123088"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Asthma/GSE123088.csv"
+out_gene_data_file = "./output/preprocess/3/Asthma/gene_data/GSE123088.csv"
+out_clinical_data_file = "./output/preprocess/3/Asthma/clinical_data/GSE123088.csv"
+json_path = "./output/preprocess/3/Asthma/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
+# CD4+ T cells data likely contains gene expression, not just miRNA or methylation
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+trait_row = 1  # 'primary diagnosis' contains trait info
+age_row = 3  # 'age' data starts at feature 3 and continues in feature 4
+gender_row = 2  # 'Sex' info is in feature 2
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    if pd.isna(value):
+        return None
+    value = value.split(': ')[1]
+    # Convert values to binary (0: control, 1: asthma)
+    if value in ['HEALTHY_CONTROL', 'Control']:
+        return 0
+    elif value in ['ASTHMA']:
+        return 1
+    return None
+
+def convert_age(value):
+    if pd.isna(value):
+        return None
+    try:
+        # Extract numeric age value after colon
+        age = int(value.split(': ')[1])
+        return age
+    except:
+        return None
+
+def convert_gender(value):
+    if pd.isna(value):
+        return None
+    if not value.startswith('Sex:'):
+        return None
+    value = value.split(': ')[1]
+    # Convert to binary (0: female, 1: male)
+    if value.upper() == 'FEMALE':
+        return 0
+    elif value.upper() == 'MALE':
+        return 1
+    return None
+
+# 3. Save Metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=trait_row is not None
+)
+
+# 4. Clinical Feature Extraction
+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 processed clinical data
+    preview = preview_df(selected_clinical)
+    print("Preview of processed clinical data:")
+    print(preview)
+    
+    # Save to CSV
+    selected_clinical.to_csv(out_clinical_data_file)
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug 
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# The identifiers appear to be numeric IDs from 1-24166
+# These are not gene symbols and will need mapping
+requires_gene_mapping = True
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract platform info from SOFT file line by line
+import gzip
+import re
+platform_lines = []
+gene_symbol_lines = []
+with gzip.open(soft_file, 'rt') as f:
+    in_platform_block = False
+    for line in f:
+        if line.startswith('!Platform_'):
+            platform_lines.append(line.strip())
+            if 'table_begin' in line:
+                in_platform_block = True
+                # Skip header line
+                next(f)
+                continue
+            elif 'table_end' in line:
+                in_platform_block = False
+                continue
+        elif in_platform_block:
+            gene_symbol_lines.append(line.strip())
+
+# Parse platform annotation table
+import pandas as pd 
+import io
+
+platform_table = pd.read_csv(io.StringIO('\n'.join(gene_symbol_lines)), sep='\t')
+
+# Preview annotation data
+print("Platform Information:")
+for line in platform_lines[:5]:
+    print(line)
+
+print("\nPlatform Annotation Table Preview:")
+print("Column names:", platform_table.columns.tolist())
+print("\nFirst few rows:")
+print(preview_df(platform_table))
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Look through entire SOFT file for gene symbol information
+import gzip
+platform_lines = []
+gene_table_lines = []
+with gzip.open(soft_file, 'rt') as f:
+    in_gene_table = False
+    table_found = False
+    for line in f:
+        # Keep track of platform lines for debugging
+        if line.startswith('!Platform_'):
+            platform_lines.append(line.strip())
+            if 'table_begin' in line:
+                in_gene_table = True
+                # Get header line
+                header = next(f).strip()
+                gene_table_lines.append(header)
+                continue
+            elif 'table_end' in line:
+                in_gene_table = False
+        elif in_gene_table and ('gene_symbol' in line.lower() or 'gene_name' in line.lower() or 
+                               'symbol' in line.lower() or 'gene_assignment' in line.lower()):
+            table_found = True
+            gene_table_lines.append(line.strip())
+
+print("Platform information:")
+for line in platform_lines[:10]:
+    print(line)
+
+print("\nFirst few gene table lines (if gene symbols found):")
+if gene_table_lines:
+    for line in gene_table_lines[:5]:
+        print(line)
+
+print("\nSearching for alternative annotation fields...")
+# Extract gene annotation trying both methods
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview annotation dataframe structure
+print("\nGene Annotation Preview:")
+print("Column names:", gene_annotation.columns.tolist())
+print("\nFirst few rows as dictionary (showing all columns):")
+pd.set_option('display.max_columns', None)
+print(gene_annotation.head().to_dict('records'))
+# Create mapping dataframe using platform's annotation
+mapping_data = gene_annotation.loc[:, ['ID', 'ENTREZ_GENE_ID']].dropna()
+
+# Create a list of genes for the Gene column, single gene ID per row
+mapping_data['Gene'] = mapping_data['ENTREZ_GENE_ID'].map(lambda x: [str(int(float(x)))] if pd.notnull(x) and float(x) > 0 else [])
+mapping_data = mapping_data.drop(columns=['ENTREZ_GENE_ID'])
+
+# Apply gene mapping to transform probe-level data to gene-level data 
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_data)
+
+# Normalize gene symbols to consistent format
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Save gene expression data
+gene_data.to_csv(out_gene_data_file)
+# Get file paths and read data
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get gene annotation and expression data
+gene_annotation = get_gene_annotation(soft_file)
+gene_data = get_genetic_data(matrix_file)
+
+# Clean and create mapping dataframe 
+gene_annotation = gene_annotation[gene_annotation['ENTREZ_GENE_ID'].str.isnumeric().fillna(False)]
+mapping_data = gene_annotation.loc[:, ['ID', 'ENTREZ_GENE_ID']].dropna()
+mapping_data['Gene'] = mapping_data['ENTREZ_GENE_ID'].map(lambda x: [str(int(float(x)))] if pd.notnull(x) and float(x) > 0 else [])
+mapping_data = mapping_data.drop(columns=['ENTREZ_GENE_ID'])
+
+# Apply gene mapping to transform probe-level data to gene-level data
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_data)
+
+# Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# Link clinical and genetic data
+clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
+
+# Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# Evaluate bias
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# Validate and save cohort info
+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="Dataset contains gene expression data from CD4+ T cells."
+)
+
+# Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Asthma/code/GSE182797.py

@@ -0,0 +1,185 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Asthma"
+cohort = "GSE182797"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Asthma"
+in_cohort_dir = "../DATA/GEO/Asthma/GSE182797"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Asthma/GSE182797.csv"
+out_gene_data_file = "./output/preprocess/3/Asthma/gene_data/GSE182797.csv"
+out_clinical_data_file = "./output/preprocess/3/Asthma/clinical_data/GSE182797.csv"
+json_path = "./output/preprocess/3/Asthma/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Based on series title and overall design, this dataset contains transcriptomic data from nasal biopsies
+is_gene_available = True
+
+# 2.1 Data Availability
+# trait data is in Feature 0 (diagnosis)
+trait_row = 0
+
+# gender data is in Feature 1 but only contains females
+gender_row = None  # Not useful since all subjects are female
+
+# age data is in Feature 2
+age_row = 2
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    if not isinstance(x, str):
+        return None
+    value = x.split(': ')[-1].lower()
+    if 'adult-onset asthma' in value:
+        return 1
+    elif 'healthy' in value:
+        return 0
+    return None
+
+def convert_age(x):
+    if not isinstance(x, str):
+        return None
+    try:
+        value = x.split(': ')[-1]
+        return float(value)
+    except:
+        return None
+
+def convert_gender(x):
+    # This function won't be used but included for completeness
+    if not isinstance(x, str):
+        return None
+    value = x.split(': ')[-1].lower()
+    if 'female' in value:
+        return 0
+    elif 'male' in value:
+        return 1
+    return None
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+
+# 4. Clinical Feature Extraction
+# Since trait_row is not None, we need to extract clinical features
+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)
+
+# Preview the extracted features
+print("Preview of extracted clinical features:")
+print(preview_df(selected_clinical))
+
+# Save clinical data
+selected_clinical.to_csv(out_clinical_data_file)
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 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)
+# These are Agilent probe identifiers starting with A_19_P, NOT human gene symbols
+requires_gene_mapping = True
+# Get file paths
+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 annotation dataframe structure
+print("Gene Annotation Preview:")
+print("Column names:", gene_annotation.columns.tolist())
+print("\nFirst few rows as dictionary:")
+print(preview_df(gene_annotation))
+# 1. Based on the dataframes, 'ID' column contains probe identifiers, and 'GENE_SYMBOL' contains gene symbols
+prob_col = 'ID'
+gene_col = 'GENE_SYMBOL'
+
+# 2. Get gene mapping dataframe
+mapping_data = get_gene_mapping(gene_annotation, prob_col, gene_col)
+
+# 3. Convert probe-level measurements to gene expression 
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Print dimensions and first few rows to verify the mapping
+print("Shape of gene expression data after mapping:", gene_data.shape)
+print("\nFirst few rows of mapped data:")
+print(gene_data.head())
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data 
+clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Evaluate bias
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate and save cohort info
+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="Dataset contains RNA transcriptome data in human sinonasal epithelial cells."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Asthma/code/GSE182798.py

@@ -0,0 +1,178 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Asthma"
+cohort = "GSE182798"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Asthma"
+in_cohort_dir = "../DATA/GEO/Asthma/GSE182798"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Asthma/GSE182798.csv"
+out_gene_data_file = "./output/preprocess/3/Asthma/gene_data/GSE182798.csv"
+out_clinical_data_file = "./output/preprocess/3/Asthma/clinical_data/GSE182798.csv"
+json_path = "./output/preprocess/3/Asthma/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# The dataset contains PBMC and Nasal biopsy samples, which are suitable for gene expression profiling
+is_gene_available = True
+
+# 2.1 Data Row Identification
+trait_row = 0  # diagnosis is in row 0
+age_row = 2    # age is in row 2
+gender_row = 1  # gender is in row 1, but all female so mark as None since constant
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    if pd.isna(x):
+        return None
+    value = x.split(': ')[1].lower()
+    if 'adult-onset asthma' in value:
+        return 1
+    elif 'healthy' in value:
+        return 0
+    return None
+
+def convert_age(x):
+    if pd.isna(x):
+        return None
+    try:
+        return float(x.split(': ')[1])
+    except:
+        return None
+
+def convert_gender(x):
+    if pd.isna(x):
+        return None
+    value = x.split(': ')[1].lower()
+    if 'female' in value:
+        return 0
+    elif 'male' in value:
+        return 1
+    return None
+
+# 3. Save Metadata
+is_usable = validate_and_save_cohort_info(is_final=False, 
+                                         cohort=cohort,
+                                         info_path=json_path,
+                                         is_gene_available=is_gene_available,
+                                         is_trait_available=trait_row is not None)
+
+# 4. Clinical Feature Extraction 
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(clinical_df=clinical_data,
+                                                   trait=trait,
+                                                   trait_row=trait_row,
+                                                   convert_trait=convert_trait,
+                                                   age_row=age_row,
+                                                   convert_age=convert_age,
+                                                   gender_row=gender_row,
+                                                   convert_gender=convert_gender)
+    
+    # Preview the data
+    preview = preview_df(clinical_features)
+    print("Clinical Features Preview:", preview)
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 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, they start with "A_19_P" followed by numbers
+# These are Agilent microarray probe IDs, not human gene symbols
+requires_gene_mapping = True
+# Get file paths
+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 annotation dataframe structure
+print("Gene Annotation Preview:")
+print("Column names:", gene_annotation.columns.tolist())
+print("\nFirst few rows as dictionary:")
+print(preview_df(gene_annotation))
+# 1. Create gene mapping dataframe from annotation
+mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# 2. Apply gene mapping to convert probe-level measurements to gene expression values
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# 3. Print a quick preview of the mapped gene data
+print("Gene expression data after mapping:")
+print("Shape:", gene_data.shape)
+print("\nFirst few rows:")
+print(gene_data.head())
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data 
+clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Evaluate bias
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate and save cohort info
+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="Dataset contains RNA transcriptome data in human sinonasal epithelial cells."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Asthma/code/GSE184382.py

@@ -0,0 +1,66 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Asthma"
+cohort = "GSE184382"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Asthma"
+in_cohort_dir = "../DATA/GEO/Asthma/GSE184382"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Asthma/GSE184382.csv"
+out_gene_data_file = "./output/preprocess/3/Asthma/gene_data/GSE184382.csv"
+out_clinical_data_file = "./output/preprocess/3/Asthma/clinical_data/GSE184382.csv"
+json_path = "./output/preprocess/3/Asthma/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Based on series title and summary, this dataset contains miRNA data rather than gene expression data
+is_gene_available = False
+
+# 2. Variable Availability and Data Type Conversion 
+# 2.1 Check data availability from sample characteristics
+trait_row = None  # Cannot reliably determine asthma status from AIT treatment alone
+age_row = None  # Age data not available 
+gender_row = None  # Gender data not available
+
+# 2.2 Define conversion functions (though data not available in this case)
+def convert_trait(x):
+    return None
+
+def convert_age(x):
+    return None
+
+def convert_gender(x):
+    return None
+
+# 3. Save metadata about dataset usability
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# 4. Clinical Feature Extraction
+# Skip this step since trait_row is None

p3/preprocess/Asthma/code/GSE185658.py

@@ -0,0 +1,174 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Asthma"
+cohort = "GSE185658"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Asthma"
+in_cohort_dir = "../DATA/GEO/Asthma/GSE185658"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Asthma/GSE185658.csv"
+out_gene_data_file = "./output/preprocess/3/Asthma/gene_data/GSE185658.csv"
+out_clinical_data_file = "./output/preprocess/3/Asthma/clinical_data/GSE185658.csv"
+json_path = "./output/preprocess/3/Asthma/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Yes, this dataset contains microarray data for gene expression (see Series_summary)
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability and Location
+trait_row = 1  # 'group' feature contains asthma status
+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) -> Optional[int]:
+    """Convert asthma status to binary: 1 for asthma, 0 for healthy"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip()
+    if 'Asthma' in value:  # Both AsthmaHDM and AsthmaHDMNeg are asthma cases
+        return 1
+    elif value == 'Healthy':
+        return 0
+    return None
+
+convert_age = None  # No age data
+convert_gender = None  # No gender data
+
+# 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:
+    # Extract clinical features
+    clinical_features = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the extracted features
+    print("Preview of clinical features:")
+    print(preview_df(clinical_features))
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug 
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# The identifiers are numeric IDs like '7892501', not standard gene symbols
+# These appear to be probe IDs from a microarray platform that need to be mapped to gene symbols
+requires_gene_mapping = True
+# Get file paths
+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 annotation dataframe structure
+print("Gene Annotation Preview:")
+print("Column names:", gene_annotation.columns.tolist())
+print("\nFirst few rows as dictionary:")
+print(preview_df(gene_annotation))
+# 1. Identify mapping columns from gene annotation
+# 'ID' column in annotation matches probe IDs in gene expression data
+# 'gene_assignment' contains gene symbols and annotations
+prob_col = 'ID'
+gene_col = 'gene_assignment'
+
+# 2. Get mapping between probe IDs and gene symbols
+mapping_data = get_gene_mapping(gene_annotation, prob_col, gene_col)
+
+# 3. Apply gene mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Print the shape and preview of mapped gene data
+print("\nShape of mapped gene expression data:", gene_data.shape)
+print("\nFirst few rows of mapped gene expression data:")
+print(gene_data.head())
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data 
+clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Evaluate bias
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate and save cohort info
+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="Dataset contains RNA transcriptome data in human sinonasal epithelial cells."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Asthma/code/GSE188424.py

@@ -0,0 +1,146 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Asthma"
+cohort = "GSE188424"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Asthma"
+in_cohort_dir = "../DATA/GEO/Asthma/GSE188424"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Asthma/GSE188424.csv"
+out_gene_data_file = "./output/preprocess/3/Asthma/gene_data/GSE188424.csv"
+out_clinical_data_file = "./output/preprocess/3/Asthma/clinical_data/GSE188424.csv"
+json_path = "./output/preprocess/3/Asthma/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Yes, this dataset contains gene expression data from human blood samples,
+# not just miRNA or methylation
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+trait_row = None  # Not available in characteristics dictionary
+gender_row = 0  # Gender data is available at index 0 
+age_row = None  # No age data available
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    # Won't be used since trait data is not available
+    return None
+
+def convert_gender(value):
+    if not isinstance(value, str):
+        return None
+    value = value.lower().split(': ')[-1].strip()
+    if value == 'female':
+        return 0
+    elif value == 'male':
+        return 1
+    return None
+
+def convert_age(value):
+    # Won't be used since age data is not available
+    return None
+
+# 3. Save Metadata - 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=False  # trait_row is None
+)
+
+# 4. Clinical Feature Extraction
+# Skip this step since trait_row is None
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 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)
+# These are Illumina probe IDs (starting with ILMN_) and need to be mapped to gene symbols
+requires_gene_mapping = True
+# Get file paths
+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 annotation dataframe structure
+print("Gene Annotation Preview:")
+print("Column names:", gene_annotation.columns.tolist())
+print("\nFirst few rows as dictionary:")
+print(preview_df(gene_annotation))
+# 1. Identify mapping columns from gene annotation data
+# 'ID' column matches the probe IDs (ILMN_) in expression data
+# 'Symbol' column contains the gene symbols to map to
+prob_col = 'ID'
+gene_col = 'Symbol'
+
+# 2. Get gene mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)
+
+# 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 mapped data
+print("\nShape of gene-level expression data:", gene_data.shape)
+print("\nPreview of gene-level expression data:")
+print(gene_data.head())
+# 1. Normalize gene symbols and save gene data
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Update cohort info - dataset not usable due to missing trait data
+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=True,  # Dataset unusable without trait data
+    df=gene_data,    # Pass gene expression data
+    note="Dataset contains gene expression data but lacks trait information needed for association studies."
+)

p3/preprocess/Asthma/code/GSE205151.py

@@ -0,0 +1,153 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Asthma"
+cohort = "GSE205151"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Asthma"
+in_cohort_dir = "../DATA/GEO/Asthma/GSE205151"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Asthma/GSE205151.csv"
+out_gene_data_file = "./output/preprocess/3/Asthma/gene_data/GSE205151.csv"
+out_clinical_data_file = "./output/preprocess/3/Asthma/clinical_data/GSE205151.csv"
+json_path = "./output/preprocess/3/Asthma/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Yes, this dataset contains gene expression data from Nanostring array
+is_gene_available = True
+
+# 2.1. Data Availability
+# Use cluster information as trait data since it represents asthma phenotypes
+trait_row = 1  # Feature 1 contains cluster info
+age_row = None # Age not explicitly provided
+gender_row = None # Gender not provided
+
+# 2.2. Data Type Conversion Functions
+def convert_trait(x):
+    if pd.isna(x):
+        return None
+    # Extract cluster number after colon and convert to binary
+    # cluster 1 -> 0, cluster 2 -> 1
+    try:
+        cluster = int(x.split(':')[1].strip())
+        if cluster == 1:
+            return 0
+        elif cluster == 2:
+            return 1
+        return None
+    except:
+        return None
+
+def convert_age(x):
+    # Not used since age data unavailable
+    return None
+
+def convert_gender(x):
+    # Not used since gender data unavailable
+    return None
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait
+    )
+    
+    # Preview the extracted features
+    print("Preview of extracted clinical features:")
+    print(preview_df(selected_clinical_df))
+    
+    # Save to CSV
+    selected_clinical_df.to_csv(out_clinical_data_file)
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 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 in the first few rows, they appear to be valid human gene symbols
+# For example, ABCB1, ABCF1, ABL1, ADA, AHR are all standard human gene symbols
+# The ID column contains recognized official human gene symbols that do not need mapping
+requires_gene_mapping = False
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data 
+clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Evaluate bias
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate and save cohort info
+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="Dataset contains RNA transcriptome data in human sinonasal epithelial cells."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Asthma/code/GSE230164.py

@@ -0,0 +1,144 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Asthma"
+cohort = "GSE230164"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Asthma"
+in_cohort_dir = "../DATA/GEO/Asthma/GSE230164"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Asthma/GSE230164.csv"
+out_gene_data_file = "./output/preprocess/3/Asthma/gene_data/GSE230164.csv"
+out_clinical_data_file = "./output/preprocess/3/Asthma/clinical_data/GSE230164.csv"
+json_path = "./output/preprocess/3/Asthma/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
+# Since "Gene expression profiling" is mentioned in series title, 
+# and series summary indicates this is a SuperSeries containing SubSeries,
+# this dataset is likely to contain gene expression data
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+# Based on sample characteristics, we can find:
+# - Gender is available at key 0
+# - Trait and age information are not explicitly available
+trait_row = None 
+age_row = None
+gender_row = 0
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    return None # Not available
+
+def convert_age(x):
+    return None # Not available
+
+def convert_gender(x):
+    # Extract value after colon and convert to binary
+    if not isinstance(x, str):
+        return None
+    value = x.split(': ')[-1].lower()
+    if value == 'female':
+        return 0
+    elif value == 'male':
+        return 1
+    return None
+
+# 3. Save metadata about data 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. Since trait_row is None, skip clinical feature extraction
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug 
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# The identifiers start with ILMN_ which indicates they are Illumina probe IDs
+# These need to be mapped to human gene symbols for standardization
+requires_gene_mapping = True
+# Get file paths
+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 annotation dataframe structure
+print("Gene Annotation Preview:")
+print("Column names:", gene_annotation.columns.tolist())
+print("\nFirst few rows as dictionary:")
+print(preview_df(gene_annotation))
+# 1. Based on the preview, 'ID' in annotation matches probe IDs in expression data (ILMN_*),
+# and 'Symbol' contains gene symbols
+
+# 2. Extract ID and Symbol columns to create mapping dataframe
+gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# 3. Apply gene mapping to convert probe-level measurements to gene-level expression
+gene_data = apply_gene_mapping(gene_data, gene_mapping)
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# Create a minimal DataFrame for validation
+linked_data = gene_data.T  # Transpose to have samples as rows
+
+# Validate and save cohort info 
+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 data means it's biased by definition
+    df=linked_data,
+    note="Dataset contains gene expression data but lacks required trait information."
+)

p3/preprocess/Asthma/code/GSE270312.py

@@ -0,0 +1,149 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Asthma"
+cohort = "GSE270312"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Asthma"
+in_cohort_dir = "../DATA/GEO/Asthma/GSE270312"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Asthma/GSE270312.csv"
+out_gene_data_file = "./output/preprocess/3/Asthma/gene_data/GSE270312.csv"
+out_clinical_data_file = "./output/preprocess/3/Asthma/clinical_data/GSE270312.csv"
+json_path = "./output/preprocess/3/Asthma/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")
+# Data type conversion functions
+def convert_trait(value: str) -> Optional[int]:
+    if not value or ':' not in value:
+        return None
+    asthma_status = value.split(':')[1].strip().lower()
+    if 'yes' in asthma_status:
+        return 1
+    elif 'no' in asthma_status:
+        return 0
+    return None
+
+def convert_gender(value: str) -> Optional[int]:
+    if not value or ':' not in value:
+        return None
+    gender = value.split(':')[1].strip().lower()
+    if 'female' in gender:
+        return 0
+    elif 'male' in gender:
+        return 1
+    return None
+
+# Gene expression data availability
+is_gene_available = True  # RNA transcriptome data available
+
+# Variable row identification
+trait_row = 3  # 'asthma status'
+gender_row = 2  # 'gender'
+age_row = None  # Age not available in characteristics
+
+# Initial validation and save metadata
+is_trait_available = trait_row is not None
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Clinical feature extraction 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,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Preview and save clinical data
+preview_result = preview_df(selected_clinical)
+print("Preview of clinical data:", preview_result)
+
+# Save clinical data
+os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+selected_clinical.to_csv(out_clinical_data_file)
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug 
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# The gene IDs appear to be valid human gene symbols like ABCF1, ACE, ACKR2, ACKR3, etc.
+# The IDs match with official HGNC gene symbols, so no mapping is needed
+requires_gene_mapping = False
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data 
+clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Evaluate bias
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate and save cohort info
+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="Dataset contains RNA transcriptome data in human sinonasal epithelial cells."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Asthma/code/TCGA.py

@@ -0,0 +1,34 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Asthma"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Asthma/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Asthma/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Asthma/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Asthma/cohort_info.json"
+
+# Review available cohorts for asthma relevance
+tcga_dirs = os.listdir(tcga_root_dir)
+# Filter out non-directory files
+tcga_dirs = [d for d in tcga_dirs if os.path.isdir(os.path.join(tcga_root_dir, d))]
+
+# For asthma trait, none of the TCGA cancer cohorts are directly relevant
+print(f"No suitable TCGA cancer cohort was found for the trait: {trait}")
+
+# Save cohort info to mark this trait as completed
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=False, 
+    is_trait_available=False
+)
+# Exit preprocessing as no suitable data exists
+clinical_df = None
+genetic_df = None

p3/preprocess/Asthma/gene_data/GSE123086.csv

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p3/preprocess/Asthma/gene_data/GSE123088.csv

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p3/preprocess/Asthma/gene_data/GSE182797.csv

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p3/preprocess/Atrial_Fibrillation/GSE115574.csv

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p3/preprocess/Atrial_Fibrillation/GSE235307.csv

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p3/preprocess/Atrial_Fibrillation/clinical_data/GSE115574.csv

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p3/preprocess/Atrial_Fibrillation/clinical_data/GSE143924.csv

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p3/preprocess/Atrial_Fibrillation/clinical_data/GSE235307.csv

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+Atrial_Fibrillation,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+Age,67.0,54.0,73.0,52.0,75.0,59.0,74.0,75.0,74.0,76.0,73.0,67.0,58.0,60.0,66.0,70.0,75.0,70.0,78.0,77.0,72.0,78.0,57.0,77.0,63.0,62.0,52.0,74.0,59.0,64.0,60.0,60.0,63.0,67.0,61.0,69.0,61.0,69.0,60.0,62.0,66.0,60.0,63.0,77.0,78.0,78.0,76.0,69.0,68.0,70.0,72.0,68.0,75.0,76.0,72.0,72.0,73.0,67.0,62.0,76.0,82.0,76.0,73.0,75.0,78.0,57.0,77.0,60.0,75.0,75.0,77.0,72.0,73.0,72.0,74.0,78.0,71.0,70.0,76.0,74.0,76.0,71.0,61.0,63.0,68.0,67.0,64.0,56.0,52.0,72.0,73.0,53.0,63.0,49.0,54.0,54.0,52.0,52.0,51.0,63.0,71.0,76.0,73.0,68.0,73.0,76.0,64.0,79.0,58.0,67.0,71.0,80.0,71.0,73.0,71.0,69.0,70.0,63.0,65.0,64.0,67.0,67.0
+Gender,1.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0

p3/preprocess/Atrial_Fibrillation/code/GSE115574.py

@@ -0,0 +1,150 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atrial_Fibrillation"
+cohort = "GSE115574"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atrial_Fibrillation"
+in_cohort_dir = "../DATA/GEO/Atrial_Fibrillation/GSE115574"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Atrial_Fibrillation/GSE115574.csv"
+out_gene_data_file = "./output/preprocess/3/Atrial_Fibrillation/gene_data/GSE115574.csv"
+out_clinical_data_file = "./output/preprocess/3/Atrial_Fibrillation/clinical_data/GSE115574.csv"
+json_path = "./output/preprocess/3/Atrial_Fibrillation/cohort_info.json"
+
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Create dictionary of unique values for each feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for feature, values in unique_values_dict.items():
+    print(f"\n{feature}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# The background info mentions "Affymetrix human gene expression microarrays"
+is_gene_available = True
+
+# 2.1 Variable Availability
+trait_row = 0  # Disease state field contains AF vs SR status
+age_row = None  # Age not available 
+gender_row = None  # Gender not available
+
+# 2.2 Data Type Conversion
+def convert_trait(value):
+    if not isinstance(value, str):
+        return None
+    val = value.lower().split(': ')[-1].strip()
+    if 'atrial fibrillation' in val:
+        return 1
+    elif 'sinus rhythm' in val:
+        return 0
+    return None
+
+def convert_age(value):
+    return None  # Not used but defined for completeness
+
+def convert_gender(value):
+    return None  # Not used but defined for completeness
+
+# 3. Save Metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age, 
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the extracted features
+    preview = preview_df(clinical_features)
+    print("Preview of clinical features:")
+    print(preview)
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs
+print("First 20 gene/probe IDs:")
+print(list(genetic_data.index[:20]))
+# These appear to be Affymetrix probe IDs (e.g. '1007_s_at') rather than human gene symbols
+# They will need to be mapped to standard gene symbols for analysis
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file_path) 
+
+# Preview column names and first few values
+preview = preview_df(gene_metadata)
+print("\nGene annotation columns and sample values:")
+print(preview)
+# 1. ID and Gene Symbol columns identified from the annotation preview
+# ID column matches the probe IDs in gene expression data (e.g. '1007_s_at')
+# Gene Symbol column contains the target gene symbols
+prob_col = 'ID'
+gene_col = 'Gene Symbol'
+
+# 2. Get gene mapping dataframe
+mapping_data = get_gene_mapping(gene_metadata, prob_col, gene_col)
+
+# 3. Apply gene mapping to convert probe measurements to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Preview the first few rows to verify the mapping
+print("\nFirst few rows of mapped gene expression data:")
+print(preview_df(gene_data))
+# 1. Normalize gene symbols and save gene data
+genetic_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+genetic_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data 
+clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_features, genetic_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Judge bias in features and remove biased ones
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+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="Sample size adequate. Gene expression data quality good. Trait is postoperative atrial fibrillation vs sinus rhythm."
+)
+
+# 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/Atrial_Fibrillation/code/GSE143924.py

@@ -0,0 +1,133 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atrial_Fibrillation"
+cohort = "GSE143924"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atrial_Fibrillation"
+in_cohort_dir = "../DATA/GEO/Atrial_Fibrillation/GSE143924"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Atrial_Fibrillation/GSE143924.csv"
+out_gene_data_file = "./output/preprocess/3/Atrial_Fibrillation/gene_data/GSE143924.csv"
+out_clinical_data_file = "./output/preprocess/3/Atrial_Fibrillation/clinical_data/GSE143924.csv"
+json_path = "./output/preprocess/3/Atrial_Fibrillation/cohort_info.json"
+
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Create dictionary of unique values for each feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for feature, values in unique_values_dict.items():
+    print(f"\n{feature}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# Based on background information, this is a transcriptome analysis study from tissue biopsies
+is_gene_available = True
+
+# 2.1 Data Availability
+# From sample characteristics, trait data is in row 1 (POAF vs SR)
+trait_row = 1
+# Age and gender are not available in the sample characteristics
+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.split(": ")[-1].lower()
+    if "postoperative atrial fibrillation" in value or "poaf" in value:
+        return 1
+    elif "sinus rhythm" in value:
+        return 0
+    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 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_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the processed clinical data
+    print("Preview of processed clinical data:")
+    print(preview_df(clinical_df))
+    
+    # Save clinical data
+    clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs
+print("First 20 gene/probe IDs:")
+print(list(genetic_data.index[:20]))
+# These appear to be human gene symbols - they follow standard HUGO nomenclature with accepted formats like:
+# - Standard gene symbols (e.g. AACS, AADAC)
+# - Antisense RNA annotations (-AS1 suffix)
+# - Pseudogene annotations (P suffix)
+# No mapping needed as they are already in the desired format
+
+requires_gene_mapping = False
+# 1. Normalize gene symbols and save gene data
+genetic_data = normalize_gene_symbols_in_index(genetic_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+genetic_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data 
+linked_data = geo_link_clinical_genetic_data(clinical_df, genetic_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Judge bias in features and remove biased ones
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+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="Sample size adequate. Gene expression data quality good. Trait is postoperative atrial fibrillation vs sinus rhythm."
+)
+
+# 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/Atrial_Fibrillation/code/GSE235307.py

@@ -0,0 +1,169 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atrial_Fibrillation"
+cohort = "GSE235307"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atrial_Fibrillation"
+in_cohort_dir = "../DATA/GEO/Atrial_Fibrillation/GSE235307"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Atrial_Fibrillation/GSE235307.csv"
+out_gene_data_file = "./output/preprocess/3/Atrial_Fibrillation/gene_data/GSE235307.csv"
+out_clinical_data_file = "./output/preprocess/3/Atrial_Fibrillation/clinical_data/GSE235307.csv"
+json_path = "./output/preprocess/3/Atrial_Fibrillation/cohort_info.json"
+
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Create dictionary of unique values for each feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for feature, values in unique_values_dict.items():
+    print(f"\n{feature}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# From background info, this is a blood gene expression study
+is_gene_available = True
+
+# 2.1 Data Row Identification
+trait_row = 5  # cardiac rhythm after 1 year follow-up
+age_row = 2    # age is available
+gender_row = 1 # gender is available
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> Optional[int]:
+    """Convert AF status to binary: 1 for AF, 0 for sinus rhythm"""
+    if value is None:
+        return None
+    value = value.split(': ')[-1].lower().strip()
+    if 'atrial fibrillation' in value:
+        return 1
+    elif 'sinus rhythm' in value:
+        return 0
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    """Convert age to float"""
+    if value is None:
+        return None
+    try:
+        return float(value.split(': ')[-1])
+    except:
+        return None
+
+def convert_gender(value: str) -> Optional[int]:
+    """Convert gender to binary: 0 for female, 1 for male"""
+    if value is None:
+        return None
+    value = value.split(': ')[-1].lower().strip()
+    if value == 'female':
+        return 0
+    elif value == 'male':
+        return 1
+    return None
+
+# 3. Save Metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=trait_row is not None
+)
+
+# 4. Clinical Feature Extraction
+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
+    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 from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs
+print("First 20 gene/probe IDs:")
+print(list(genetic_data.index[:20]))
+# The gene identifiers in this dataset appear to be simple numeric values, 
+# which are not standard human gene symbols.
+# Standard gene symbols would be like "BRCA1", "TP53", etc
+# Therefore mapping is required to convert these to proper gene symbols.
+
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file_path) 
+
+# Preview column names and first few values
+preview = preview_df(gene_metadata)
+print("\nGene annotation columns and sample values:")
+print(preview)
+# 1. From the preview, 'ID' contains numeric identifiers matching gene expression data,
+# and 'GENE_SYMBOL' contains human gene symbols
+
+# 2. Extract mapping between probe IDs and gene symbols
+mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# 3. Apply mapping to convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Normalize gene symbols in the gene expression data
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Preview the mapped gene data
+print("\nFirst 20 genes after mapping:")
+print(list(gene_data.index[:20]))
+
+# Save gene expression data
+gene_data.to_csv(out_gene_data_file)
+# 1. Normalize gene symbols and save gene data
+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_features, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Judge bias in features and remove biased ones
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+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="Sample size adequate. Gene expression data quality good. Trait is early vs late recurrence."
+)
+
+# 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/Atrial_Fibrillation/code/GSE41177.py

@@ -0,0 +1,152 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atrial_Fibrillation"
+cohort = "GSE41177"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atrial_Fibrillation"
+in_cohort_dir = "../DATA/GEO/Atrial_Fibrillation/GSE41177"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Atrial_Fibrillation/GSE41177.csv"
+out_gene_data_file = "./output/preprocess/3/Atrial_Fibrillation/gene_data/GSE41177.csv"
+out_clinical_data_file = "./output/preprocess/3/Atrial_Fibrillation/clinical_data/GSE41177.csv"
+json_path = "./output/preprocess/3/Atrial_Fibrillation/cohort_info.json"
+
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Create dictionary of unique values for each feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for feature, values in unique_values_dict.items():
+    print(f"\n{feature}:")
+    print(values)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Dataset contains microarray gene expression data per background info
+
+# 2.1 Data Availability
+trait_row = 3  # 'af duration' indicates AF status duration
+age_row = 2    # Age data available
+gender_row = 1 # Gender data available 
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    if not isinstance(x, str):
+        return None
+    value = x.split(': ')[-1].strip()
+    # Convert AF duration to binary - any duration indicates AF presence
+    if value == '0M':
+        return 0
+    elif 'M' in value:  # Has months duration
+        return 1
+    return None
+
+def convert_age(x):
+    if not isinstance(x, str):
+        return None
+    value = x.split(': ')[-1].strip()
+    if value.endswith('Y'):
+        try:
+            return float(value[:-1])  # Remove 'Y' and convert to float
+        except:
+            return None
+    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
+
+# 3. Save Metadata 
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=trait_row is not None)
+
+# 4. Extract Clinical Features
+selected_clinical_df = geo_select_clinical_features(clinical_df=clinical_data,
+                                                  trait=trait,
+                                                  trait_row=trait_row,
+                                                  convert_trait=convert_trait,
+                                                  age_row=age_row,
+                                                  convert_age=convert_age, 
+                                                  gender_row=gender_row,
+                                                  convert_gender=convert_gender)
+
+# Preview the processed clinical data
+print(preview_df(selected_clinical_df))
+
+# Save clinical data
+selected_clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs
+print("First 20 gene/probe IDs:")
+print(list(genetic_data.index[:20]))
+# These are Affymetrix probe IDs (starting with numbers and containing "_at"), not human gene symbols
+# They need to be mapped to standard gene symbols for analysis
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file_path) 
+
+# Preview column names and first few values
+preview = preview_df(gene_metadata)
+print("\nGene annotation columns and sample values:")
+print(preview)
+# Get gene mapping dataframe from annotation data
+# 'ID' column contains probe IDs matching genetic_data, 'Gene Symbol' contains gene symbols
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')
+
+# Apply gene mapping to convert from probes to genes
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Preview the first few rows and columns of the mapped gene data
+print("\nFirst few rows of mapped gene expression data:")
+print(preview_df(gene_data))
+# 1. Normalize gene symbols and save gene data
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data 
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Judge bias in features and remove biased ones
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+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="Sample size adequate. Gene expression data quality good. Trait is early vs late recurrence."
+)
+
+# 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/Atrial_Fibrillation/code/GSE47727.py

@@ -0,0 +1,97 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atrial_Fibrillation"
+cohort = "GSE47727"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atrial_Fibrillation"
+in_cohort_dir = "../DATA/GEO/Atrial_Fibrillation/GSE47727"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Atrial_Fibrillation/GSE47727.csv"
+out_gene_data_file = "./output/preprocess/3/Atrial_Fibrillation/gene_data/GSE47727.csv"
+out_clinical_data_file = "./output/preprocess/3/Atrial_Fibrillation/clinical_data/GSE47727.csv"
+json_path = "./output/preprocess/3/Atrial_Fibrillation/cohort_info.json"
+
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Create dictionary of unique values for each feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for feature, values in unique_values_dict.items():
+    print(f"\n{feature}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# Based on the background info mentioning "gene expression" and "HumanHT-12", this is gene expression data
+is_gene_available = True
+
+# 2.1 Data Row Identification 
+# From characteristics dictionary:
+# For trait: no direct AF status, all are controls based on background info
+trait_row = None 
+
+# Age is in row 0
+age_row = 0
+
+# Gender is in row 1  
+gender_row = 1
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    # All samples are controls
+    return 0
+
+def convert_age(x):
+    # Extract numeric age from strings like "age (yrs): 67"
+    try:
+        age = int(x.split(': ')[1])
+        return age
+    except:
+        return None
+        
+def convert_gender(x):
+    # Convert gender strings to binary (female=0, male=1)
+    gender = x.split(': ')[1].lower()
+    if gender == 'female':
+        return 0
+    elif gender == 'male':
+        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=False  # trait_row is None
+)
+
+# 4. Extract clinical features
+clinical_df = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Preview the extracted features
+print("\nPreview of extracted clinical features:")
+print(preview_df(clinical_df))
+
+# Save to CSV
+clinical_df.to_csv(out_clinical_data_file)