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p3/preprocess/Obesity/clinical_data/GSE271700.csv

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p3/preprocess/Obesity/clinical_data/GSE84046.csv

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p3/preprocess/Obesity/clinical_data/TCGA.csv

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p3/preprocess/Obesity/code/GSE123086.py

@@ -0,0 +1,346 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Obesity"
+cohort = "GSE123086"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Obesity"
+in_cohort_dir = "../DATA/GEO/Obesity/GSE123086"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Obesity/GSE123086.csv"
+out_gene_data_file = "./output/preprocess/3/Obesity/gene_data/GSE123086.csv"
+out_clinical_data_file = "./output/preprocess/3/Obesity/clinical_data/GSE123086.csv"
+json_path = "./output/preprocess/3/Obesity/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, from series design we can see RNA microarray was used
+is_gene_available = True
+
+# 2.1 Data Availability
+# Trait data is in Feature 1 under "primary diagnosis"
+trait_row = 1
+
+# Age data is spread across Features 3 and 4
+age_row = 3
+
+# Gender data is in Feature 2 under "Sex"
+gender_row = 2
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    # Extract value after colon and convert to binary
+    if pd.isna(x):
+        return None
+    value = x.split(': ')[1]
+    if value == 'OBESITY':
+        return 1
+    elif value == 'HEALTHY_CONTROL':
+        return 0
+    return None
+
+def convert_age(x):
+    # Extract age value as continuous
+    if pd.isna(x):
+        return None
+    if not x.startswith('age:'):
+        return None
+    try:
+        return float(x.split(': ')[1])
+    except:
+        return None
+
+def convert_gender(x):
+    # Convert gender to binary (female=0, male=1)
+    if pd.isna(x):
+        return None
+    if not x.startswith('Sex:'):
+        return None
+    value = x.split(': ')[1]
+    if value == 'Female':
+        return 0
+    elif value == 'Male':
+        return 1
+    return None
+
+# 3. Initial Filtering and Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False, 
+                             cohort=cohort,
+                             info_path=json_path,
+                             is_gene_available=is_gene_available,
+                             is_trait_available=is_trait_available)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    print("Preview of extracted clinical features:")
+    print(preview_df(clinical_features))
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# Looking at the identifiers - they are numeric values (1,2,3,9,10 etc)
+# These appear to be probe/feature IDs rather than gene symbols
+# Therefore mapping to gene symbols will be required
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# For SOFT files with platform annotations, we need to get rows not starting with '!'
+# but also not containing just numeric IDs and entrez genes
+gene_annotation = pd.read_csv(soft_file, compression='gzip', sep='\t', header=None,
+                            comment='!', on_bad_lines='skip')
+
+# Find rows with detailed platform annotations (containing gene symbols)
+annotation_starts = False
+annotation_lines = []
+with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
+    for line in f:
+        if "!platform_table_begin" in line:
+            annotation_starts = True
+            # Get the header line
+            header = next(f).strip()
+            annotation_lines.append(header)
+            continue
+        if annotation_starts:
+            if "!platform_table_end" in line:
+                break
+            annotation_lines.append(line.strip())
+
+# Convert annotation lines to dataframe
+annotation_content = '\n'.join(annotation_lines)
+gene_annotation = pd.read_csv(io.StringIO(annotation_content), sep='\t')
+
+# Preview annotation data
+print("Gene annotation shape:", gene_annotation.shape)
+print("\nAll column names:")
+print(gene_annotation.columns.tolist())
+
+print("\nFirst few rows:") 
+print(gene_annotation.head().to_string())
+
+print("\nNumber of non-null values in each column:")
+print(gene_annotation.count())
+
+# Print example rows showing ID and gene symbol columns
+print("\nSample rows showing the ID and gene symbol mapping:")
+symbol_col = [col for col in gene_annotation.columns if 'symbol' in col.lower()][0]
+print(gene_annotation[['ID', symbol_col]].head(10))
+# Extract complete platform annotation table from SOFT file
+platform_lines = []
+annotation_starts = False
+with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
+    for line in f:
+        if line.startswith('^PLATFORM'):
+            platform_lines.append(line)
+        elif line.startswith('!Platform_data_row_count'):
+            platform_lines.append(line)
+        elif line.startswith('!Platform_table_begin'):
+            annotation_starts = True
+            header = next(f).strip()
+            platform_lines.append(header)
+            continue
+        elif annotation_starts:
+            if line.startswith('!Platform_table_end'):
+                break
+            platform_lines.append(line.strip())
+
+# Parse platform annotation content
+platform_content = '\n'.join(platform_lines)
+gene_annotation = pd.read_csv(io.StringIO(platform_content), sep='\t', comment='!', skiprows=2)
+
+# Create mapping DataFrame using ID and gene symbol
+mapping_df = gene_annotation[['ID', 'GB_ACC']].copy() 
+mapping_df = mapping_df.rename(columns={'GB_ACC': 'Gene'})
+
+# Clean gene symbols - split on spaces/semicolons and take first value
+mapping_df['Gene'] = mapping_df['Gene'].astype(str).apply(lambda x: x.split(';')[0].split()[0])
+mapping_df = mapping_df.dropna()
+
+# Apply gene mapping to convert probe expression to gene expression
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Normalize gene symbols using the provided function
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Save gene expression data
+gene_data.to_csv(out_gene_data_file)
+
+# Preview the mapped gene data
+print("Shape of gene expression data after mapping:", gene_data.shape)
+print("\nFirst few mapped genes and their expression values:")
+print(gene_data.head())
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Try to peek at the SOFT file contents first
+with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
+    # Read first few lines to understand file structure
+    print("First 20 lines of SOFT file:")
+    for i, line in enumerate(f):
+        if i < 20:
+            print(line.strip())
+        else:
+            break
+
+    # Reset file pointer
+    f.seek(0)
+    
+    # Look for the platform table section
+    print("\nPlatform table header:")
+    for line in f:
+        if "!Platform_table_begin" in line:
+            # Print the next line which should be the header
+            print(next(f).strip())
+            break
+
+# Now extract annotation data using a simpler method - get lines between table markers
+annotation_lines = []
+table_started = False
+with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
+    for line in f:
+        if "!Platform_table_begin" in line:
+            header = next(f).strip()
+            annotation_lines.append(header)
+            table_started = True
+            continue
+        if table_started:
+            if "!Platform_table_end" in line:
+                break
+            annotation_lines.append(line.strip())
+
+# Convert to dataframe
+annotation_text = '\n'.join(annotation_lines)
+gene_annotation = pd.read_csv(io.StringIO(annotation_text), sep='\t')
+
+# Preview annotation data
+print("\nAnnotation data shape:", gene_annotation.shape)
+print("\nColumn names:")
+print(gene_annotation.columns.tolist())
+print("\nFirst few rows:")
+print(gene_annotation.head())
+# 1. Let's examine SOFT file content first to identify correct gene identifier columns
+with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
+    for line in f:
+        if '!Platform_table_begin' in line:
+            header = next(f).strip()
+            print("Platform table header:")
+            print(header)
+            print("\nFirst few data rows:")
+            for i in range(5):
+                print(next(f).strip())
+            break
+
+# Extract gene metadata using library function
+gene_metadata = get_gene_annotation(soft_file)
+
+# Print available columns to identify gene symbol column
+print("\nAvailable annotation columns:")
+print(gene_metadata.columns.tolist())
+
+print("\nPreview of gene metadata:")
+print(gene_metadata.head())
+
+# Get gene expression data
+gene_data = get_genetic_data(matrix_file)
+
+# Print shape and preview of expression data before mapping
+print("\nShape of gene expression data before mapping:", gene_data.shape)
+print("\nPreview of gene expression data before mapping:")
+print(gene_data.head())
+# Use library function to get gene annotation from SOFT file
+gene_metadata = get_gene_annotation(soft_file)
+
+# Create mapping DataFrame using ID and ENTREZ_GENE_ID 
+mapping_df = gene_metadata[['ID', 'ENTREZ_GENE_ID']].copy()
+mapping_df = mapping_df.rename(columns={'ENTREZ_GENE_ID': 'Gene'})
+
+# Clean and prepare mapping data
+mapping_df['ID'] = mapping_df['ID'].astype(str)
+mapping_df['Gene'] = mapping_df['Gene'].astype(str)
+mapping_df = mapping_df.dropna()
+
+# Apply gene mapping to convert probe expression to gene expression
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Save processed gene expression data
+gene_data.to_csv(out_gene_data_file)
+
+print("\nShape of gene expression data after mapping:", gene_data.shape)
+print("\nFirst few mapped genes and their expression values:")
+print(gene_data.head())
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file and get meaningful data 
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation shape:", gene_annotation.shape)
+print("\nGene annotation preview:")
+print(preview_df(gene_annotation))
+
+print("\nNumber of non-null values in each column:")
+print(gene_annotation.count())
+
+# Print example rows showing the mapping information columns
+print("\nSample mapping columns ('ID' and 'GENE_SYMBOL'):")
+print("\nFirst 5 rows:")
+print(gene_annotation[['ID', 'GENE_SYMBOL']].head().to_string())
+
+print("\nNote: Gene mapping will use:")
+print("'ID' column: Probe identifiers") 
+print("'GENE_SYMBOL' column: Contains gene symbol information")

p3/preprocess/Obesity/code/GSE123088.py

@@ -0,0 +1,306 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Obesity"
+cohort = "GSE123088"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Obesity"
+in_cohort_dir = "../DATA/GEO/Obesity/GSE123088"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Obesity/GSE123088.csv"
+out_gene_data_file = "./output/preprocess/3/Obesity/gene_data/GSE123088.csv"
+out_clinical_data_file = "./output/preprocess/3/Obesity/clinical_data/GSE123088.csv"
+json_path = "./output/preprocess/3/Obesity/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")
+import pandas as pd
+import numpy as np
+
+# Check gene expression data availability
+is_gene_available = True  # CD4+ T cells expression data should be present
+
+# Find trait, age and gender data rows and define conversion functions
+trait_row = 1 # 'primary diagnosis' contains obesity info
+age_row = 3 # 'age' info starts in row 3, continues in row 4
+gender_row = 2 # 'Sex' information
+
+def convert_trait(x):
+    if pd.isna(x):
+        return None
+    val = x.split(': ')[1] if ': ' in x else x
+    if val.upper() in ['OBESITY']:
+        return 1
+    elif 'CONTROL' in val.upper():
+        return 0
+    return None
+
+def convert_age(x):
+    if pd.isna(x):
+        return None
+    try:
+        val = x.split(': ')[1] if ': ' in x else x
+        return float(val)
+    except:
+        return None
+
+def convert_gender(x):
+    if pd.isna(x):
+        return None
+    val = x.split(': ')[1] if ': ' in x else x
+    if val.upper() == 'FEMALE':
+        return 0
+    elif val.upper() == 'MALE':
+        return 1
+    return None
+
+# Validate and save initial cohort 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
+)
+
+# Extract clinical features if trait data is available
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the clinical features
+    preview = preview_df(selected_clinical_df)
+    print("Clinical features preview:")
+    print(preview)
+    
+    # Save clinical features
+    selected_clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# Based on the gene expression data shown, the identifiers appear to be numerical indices (1, 2, 3, etc.)
+# rather than human gene symbols. This indicates mapping to gene symbols will be required.
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# First inspect a snippet of raw SOFT file to understand its structure
+import gzip
+print("Inspecting raw SOFT file structure:")
+with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if i < 20:  # Look at first 20 lines
+            lines.append(line.strip())
+        else:
+            break
+print('\n'.join(lines))
+print("\n" + "="*50 + "\n")
+
+# Extract gene annotation from SOFT file, excluding header lines
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation shape:", gene_annotation.shape)
+print("\nGene annotation columns:")
+print(gene_annotation.columns.tolist())
+print("\nGene annotation preview:")
+print(preview_df(gene_annotation))
+
+print("\nNumber of non-null values in each column:")
+print(gene_annotation.count())
+
+# Print first few rows of annotation data to verify structure
+print("\nFirst 5 rows of annotation data:")
+print(gene_annotation.head().to_string())
+# First find the SubSeries ID from the SOFT file
+import gzip
+subseries_id = None
+with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
+    for line in f:
+        if line.startswith('!Series_relation'):
+            if 'SubSeries of:' not in line:
+                subseries_id = line.strip().split(' = ')[1].split(' ')[0]
+                break
+
+# The correct subseries directory should be one level up
+platform_soft_file = os.path.join(os.path.dirname(os.path.dirname(soft_file)), 
+                                 subseries_id, 
+                                 f"{subseries_id}_family.soft.gz")
+
+# Extract platform annotation from the subseries SOFT file
+platform_lines = []
+with gzip.open(platform_soft_file, 'rt', encoding='utf-8') as f:
+    reading_platform = False
+    for line in f:
+        if line.startswith('!Platform_table_begin'):
+            reading_platform = True
+            # Skip the header line
+            header = next(f).strip().split('\t')
+            continue
+        elif line.startswith('!Platform_table_end'):
+            break
+        elif reading_platform:
+            platform_lines.append(line.strip())
+
+# Create platform annotation dataframe
+platform_data = [line.split('\t') for line in platform_lines]
+df_platform = pd.DataFrame(platform_data, columns=header)
+
+# Get mapping using ID and gene symbol columns
+mapping_data = get_gene_mapping(df_platform, 'ID', 'Gene Symbol')
+
+# Apply gene mapping to convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Preview results
+print("Shape of gene expression data after mapping:", gene_data.shape)
+print("\nFirst few rows of mapped gene expression data:")
+print(gene_data.head())
+# Ensure index is string type for gene symbol mapping
+gene_data.index = gene_data.index.astype(str)
+
+# Convert Entrez IDs to gene symbols using the built-in mapping
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Print shape and preview results
+print("Shape of gene expression data after mapping:", gene_data.shape)
+print("\nFirst few rows of mapped gene expression data:")
+print(gene_data.head())
+
+# Save gene expression data
+gene_data.to_csv(out_gene_data_file)
+# 1. First get platform annotation with gene symbols
+import gzip
+
+with gzip.open(soft_file, 'rt') as f:
+    platform_section = False
+    platform_lines = []
+    for line in f:
+        if line.startswith('^PLATFORM'):
+            platform_section = True
+        elif platform_section and line.startswith('!Platform_table_begin'):
+            header = next(f).strip().split('\t')
+            for l in f:
+                if l.startswith('!Platform_table_end'):
+                    break
+                platform_lines.append(l.strip())
+
+# Create platform annotation dataframe
+platform_data = [line.split('\t') for line in platform_lines]
+platform_df = pd.DataFrame(platform_data, columns=header)
+mapping_df = get_gene_mapping(platform_df, 'ID', 'Gene Symbol')
+
+# Apply mapping to convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# 2. Load clinical data and normalize gene symbols
+selected_clinical = pd.read_csv(out_clinical_data_file, index_col=0)
+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)
+
+# 3. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)
+
+# 4. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 5. Check for biased features and remove them if needed
+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=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note="Study examining gene expression in CD4+ T cells across multiple diseases including obesity"
+)
+
+# 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)
+# 1. Since we have Entrez IDs in the gene expression data index,
+# we can directly normalize them to 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)
+
+# 2. Load clinical data and link with genetic data
+selected_clinical = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features and remove them if needed 
+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="Study examining gene expression in CD4+ T cells across multiple diseases including obesity"
+)
+
+# 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/Obesity/code/GSE158237.py

@@ -0,0 +1,284 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Obesity"
+cohort = "GSE158237"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Obesity"
+in_cohort_dir = "../DATA/GEO/Obesity/GSE158237"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Obesity/GSE158237.csv"
+out_gene_data_file = "./output/preprocess/3/Obesity/gene_data/GSE158237.csv"
+out_clinical_data_file = "./output/preprocess/3/Obesity/clinical_data/GSE158237.csv"
+json_path = "./output/preprocess/3/Obesity/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 mentioning RNA extraction and transcriptomics,
+# this dataset likely contains gene expression data
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability 
+trait_row = 10  # BMI data in Feature 10
+age_row = 1     # Age data in Feature 1  
+gender_row = 2  # Sex data in Feature 2
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    # Convert BMI value to binary (0 for non-obese, 1 for obese)
+    if pd.isna(value):
+        return None
+    try:
+        bmi = float(value.split(': ')[1])
+        return 1 if bmi >= 30 else 0  # Standard obesity threshold
+    except:
+        return None
+
+def convert_age(value):
+    # Convert age to continuous value
+    if pd.isna(value):
+        return None
+    try:
+        age = float(value.split(': ')[1])
+        return age
+    except:
+        return None
+
+def convert_gender(value):
+    # Convert sex to binary (0 for female, 1 for male)
+    if pd.isna(value):
+        return None
+    try:
+        sex = int(value.split(': ')[1])
+        return 1 if sex == 1 else 0  # Assuming Sex:1 is male and Sex:2 is female
+    except:
+        return None
+
+# 3. Save Metadata
+# Conduct initial filtering
+validate_and_save_cohort_info(is_final=False,
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=(trait_row is not None))
+
+# 4. Clinical Feature Extraction
+# Since trait_row is not None, extract clinical features
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(clinical_data,
+                                                   trait=trait,
+                                                   trait_row=trait_row,
+                                                   convert_trait=convert_trait,
+                                                   age_row=age_row,
+                                                   convert_age=convert_age,
+                                                   gender_row=gender_row,
+                                                   convert_gender=convert_gender)
+    
+    # Preview the extracted features
+    preview = preview_df(clinical_features)
+    print("Preview of clinical features:", preview)
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# From the output, we can see the identifiers appear to be numeric probe IDs (e.g. 16657436)
+# rather than human gene symbols (which would look like BRCA1, TP53 etc)
+# These need to be mapped to gene symbols for biological interpretation
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file
+# Use different prefix filters to capture platform annotation with gene symbols
+gene_annotation = filter_content_by_prefix(soft_file, 
+                                         prefixes_a=['!Platform_table_begin'],
+                                         unselect=False,
+                                         source_type='file',
+                                         return_df_a=True)[0]
+
+# Preview gene annotation data
+print("Gene annotation shape:", gene_annotation.shape)
+print("\nGene annotation columns:", list(gene_annotation.columns))
+print("\nGene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Print non-null values for each column to help identify useful columns
+print("\nNumber of non-null values in each column:")
+print(gene_annotation.count())
+
+# Print example rows showing ID and gene symbol columns
+print("\nExample rows with ID and gene symbol information:")
+print(gene_annotation[['ID', 'Symbol']].head(10).to_string())
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# First examine the raw SOFT file content to locate platform annotation section
+import gzip
+platform_start = False
+header_line = None
+first_data_line = None
+
+with gzip.open(soft_file, 'rt') as f:
+    for line in f:
+        if '!Platform_table_begin' in line:
+            platform_start = True
+            # Get the next two lines (header and first data)
+            header_line = next(f).strip()
+            first_data_line = next(f).strip()
+            break
+
+print("Header line found:")
+print(header_line)
+print("\nFirst data line example:")
+print(first_data_line)
+
+# Extract platform annotation data
+from io import StringIO
+platform_data = []
+platform_start = False
+
+with gzip.open(soft_file, 'rt') as f:
+    for line in f:
+        if '!Platform_table_begin' in line:
+            platform_start = True
+            continue
+        elif '!Platform_table_end' in line:
+            break
+        elif platform_start:
+            platform_data.append(line.strip())
+
+# Convert to dataframe
+gene_annotation = pd.read_csv(StringIO('\n'.join(platform_data)), sep='\t')
+
+# Preview gene annotation data
+print("\nGene annotation shape:", gene_annotation.shape)
+print("\nGene annotation columns:", gene_annotation.columns.tolist())
+print("\nFirst few rows preview:")
+print(gene_annotation.head().to_string())
+
+# Look for columns that might contain gene symbols
+symbol_candidates = [col for col in gene_annotation.columns 
+                    if any(term in col.lower() 
+                          for term in ['gene', 'symbol', 'entrez', 'refseq'])]
+print("\nPotential gene symbol columns:", symbol_candidates)
+from io import StringIO
+
+# First inspect the SOFT file content to understand structure
+import gzip
+print("Examining SOFT file content...")
+with gzip.open(soft_file, 'rt') as f:
+    for line in f:
+        # Look for platform annotation sections that might contain gene info
+        if "!Platform_table_begin" in line:
+            header = next(f).strip()
+            print("\nFound platform table with header:")
+            print(header)
+            print("\nFirst few data lines:")
+            for _ in range(5):
+                print(next(f).strip())
+            break
+
+# Try extracting gene annotation using different prefix patterns
+gene_metadata_str = filter_content_by_prefix(soft_file, 
+                                           prefixes_a=['^', '#'],
+                                           unselect=True, 
+                                           source_type='file',
+                                           return_df_a=False)[0]
+
+# Process the metadata string to find the section with gene annotations
+annotation_lines = []
+capture = False
+for line in gene_metadata_str.split('\n'):
+    if 'Reporter Database Entry [gene symbol]' in line:
+        # Found the start of gene symbol annotations
+        capture = True
+        continue
+    if capture and line.strip():
+        if line.startswith('!'):  # End of section
+            break
+        annotation_lines.append(line)
+
+if annotation_lines:
+    # Convert captured lines to DataFrame
+    gene_metadata = pd.read_csv(StringIO('\n'.join(annotation_lines)), sep='\t')
+    
+    print("\nAvailable columns in gene annotation data:")
+    print(gene_metadata.columns.tolist())
+    
+    # Create mapping dataframe using ID and gene symbol columns
+    mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL')
+    
+    # Apply gene mapping to convert probe-level data to gene-level data
+    gene_data = apply_gene_mapping(gene_data, mapping_df)
+    
+    # Print shape information to confirm successful mapping
+    print(f"\nShape of mapped gene expression data: {gene_data.shape}")
+    print("\nFirst few gene symbols:")
+    print(gene_data.index[:10])
+else:
+    print("\nNo gene symbol annotation section found in the SOFT file.")
+# Load the clinical data that was successfully saved earlier
+selected_clinical = pd.read_csv(out_clinical_data_file, index_col=0) 
+
+# Create minimal df with just clinical features for validation
+minimal_df = selected_clinical.copy()
+
+# Check for biased features with just clinical data
+is_biased, minimal_df = judge_and_remove_biased_features(minimal_df, trait)
+
+# Save validation info with minimal df 
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=False, # Gene mapping failed
+    is_trait_available=True,
+    is_biased=is_biased, 
+    df=minimal_df,
+    note="Failed to extract gene symbol annotations from SOFT file"
+)
+
+# Do not save linked data since processing was unsuccessful

p3/preprocess/Obesity/code/GSE158850.py

@@ -0,0 +1,151 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Obesity"
+cohort = "GSE158850"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Obesity"
+in_cohort_dir = "../DATA/GEO/Obesity/GSE158850"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Obesity/GSE158850.csv"
+out_gene_data_file = "./output/preprocess/3/Obesity/gene_data/GSE158850.csv"
+out_clinical_data_file = "./output/preprocess/3/Obesity/clinical_data/GSE158850.csv"
+json_path = "./output/preprocess/3/Obesity/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 design, this appears to be skeletal muscle transcriptome data
+is_gene_available = True
+
+# 2.1 Data Availability
+# Trait: No specific obesity measurement values are provided
+trait_row = None
+
+# Age: Background info indicates having young and elderly groups, but not specific ages
+age_row = None 
+
+# Gender: Feature 1 shows mix of male/female
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    # Not used since trait data not available
+    return None
+
+def convert_age(x): 
+    # Not used since age data not available
+    return None
+    
+def convert_gender(x):
+    # Not used since gender data not available
+    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. Skip clinical feature extraction since trait_row is None
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file and get meaningful data 
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation shape:", gene_annotation.shape)
+print("\nGene annotation preview:")
+print(preview_df(gene_annotation))
+
+print("\nNumber of non-null values in each column:")
+print(gene_annotation.count())
+
+# Print example rows showing the mapping information columns
+print("\nSample mapping columns ('ID' and 'GENE_SYMBOL'):")
+print("\nFirst 5 rows:")
+print(gene_annotation[['ID', 'GENE_SYMBOL']].head().to_string())
+
+print("\nNote: Gene mapping will use:")
+print("'ID' column: Probe identifiers") 
+print("'GENE_SYMBOL' column: Contains gene symbol information")
+# Create mapping between probe IDs and gene symbols
+mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# Apply gene mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Verify the result
+print("Shape of gene expression data after mapping:", gene_data.shape)
+print("\nFirst few rows after mapping:")
+print(gene_data.head())
+print("\nFirst 20 gene symbols:")
+print(gene_data.index[:20])
+# 1. Normalize and save gene data
+gene_data.index = gene_data.index.str.replace('-mRNA', '')
+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)
+
+# Create empty DataFrame for validation since we lack clinical data
+empty_df = pd.DataFrame() 
+
+# Validate and save cohort info, marking as biased due to lack of trait data
+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,
+    df=empty_df,
+    note="Study has gene expression data but lacks usable clinical trait information for obesity analysis"
+)

p3/preprocess/Obesity/code/GSE159809.py

@@ -0,0 +1,194 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Obesity"
+cohort = "GSE159809"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Obesity"
+in_cohort_dir = "../DATA/GEO/Obesity/GSE159809"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Obesity/GSE159809.csv"
+out_gene_data_file = "./output/preprocess/3/Obesity/gene_data/GSE159809.csv"
+out_clinical_data_file = "./output/preprocess/3/Obesity/clinical_data/GSE159809.csv"
+json_path = "./output/preprocess/3/Obesity/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 description, this dataset studies transcriptomes, so gene expression data is available
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# From study background, all subjects are obese men, with age categories of young/elderly
+trait_row = 0  # First characteristic row contains subject info
+age_row = 0    # Age category (young/elderly) can be inferred from the same row
+gender_row = 0  # Gender (all male) can be inferred from the same row
+
+def convert_trait(x):
+    if pd.isna(x) or not isinstance(x, str):
+        return None
+    # All subjects are obese according to background
+    return 1
+
+def convert_age(x):
+    if pd.isna(x) or not isinstance(x, str):
+        return None
+    val = x.lower()
+    # Convert young/elderly categories to binary
+    if "young" in val:
+        return 0
+    elif "elderly" in val:
+        return 1
+    return None
+
+def convert_gender(x):
+    if pd.isna(x) or not isinstance(x, str):
+        return None
+    # All subjects are men according to background
+    return 1
+
+# 3. Save metadata
+# Initial validation based on gene and trait 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. 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 data
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+"""
+Looking at the identifiers ('A_23_P100001', 'A_23_P100022' etc), these are Agilent probe IDs 
+rather than human gene symbols. They need to be mapped to standard HGNC gene symbols.
+"""
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file and get meaningful data 
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation shape:", gene_annotation.shape)
+print("\nGene annotation preview:")
+print(preview_df(gene_annotation))
+
+print("\nNumber of non-null values in each column:")
+print(gene_annotation.count())
+
+# Print example rows showing the mapping information columns
+print("\nSample mapping columns ('ID' and 'GENE_SYMBOL'):")
+print("\nFirst 5 rows:")
+print(gene_annotation[['ID', 'GENE_SYMBOL']].head().to_string())
+
+print("\nNote: Gene mapping will use:")
+print("'ID' column: Probe identifiers") 
+print("'GENE_SYMBOL' column: Contains gene symbol information")
+# Based on observation: gene_data uses probe IDs like A_23_P100001, matching 'ID' column in gene_annotation
+# Get gene mapping dataframe from annotation data
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# Apply gene mapping to get gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Save the gene expression data
+gene_data.to_csv(out_gene_data_file)
+
+# Print preview of mapped gene data
+print("Gene data after mapping, shape:", gene_data.shape)
+print("\nFirst few rows:")
+print(gene_data.head())
+print("\nFirst 20 gene symbols:")
+print(gene_data.index[:20])
+# 1. Load clinical data and save normalized gene data
+selected_clinical = pd.read_csv(out_clinical_data_file, index_col=0)
+gene_data.index = gene_data.index.str.replace('-mRNA', '')
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features and remove them if needed 
+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="Study examining gene expression changes in adipose tissue under different protein diets during energy restriction"
+)
+
+# 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/Obesity/code/GSE181339.py

@@ -0,0 +1,200 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Obesity"
+cohort = "GSE181339"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Obesity"
+in_cohort_dir = "../DATA/GEO/Obesity/GSE181339"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Obesity/GSE181339.csv"
+out_gene_data_file = "./output/preprocess/3/Obesity/gene_data/GSE181339.csv"
+out_clinical_data_file = "./output/preprocess/3/Obesity/clinical_data/GSE181339.csv"
+json_path = "./output/preprocess/3/Obesity/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Yes - this is a microarray experiment with PBMC RNA samples
+is_gene_available = True
+
+# 2.1 Data Availability
+# The trait (obesity) can be inferred from group field (row 1)
+trait_row = 1
+
+# Age data is available in row 2
+age_row = 2
+
+# Gender data is available in row 0
+gender_row = 0
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> Optional[int]:
+    """Convert obesity status to binary (0: Normal weight, 1: Overweight/Obese)"""
+    if not value or ':' not in value:
+        return None
+    group = value.split(':')[1].strip()
+    if group == 'NW':
+        return 0
+    elif group == 'OW/OB':
+        return 1
+    elif group == 'MONW': # Metabolically obese normal-weight should be labeled as obese
+        return 1
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    """Convert age to continuous values"""
+    if not value or ':' not in value:
+        return None
+    try:
+        return float(value.split(':')[1].strip())
+    except:
+        return None
+
+def convert_gender(value: str) -> Optional[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 == 'woman':
+        return 0
+    elif gender == 'man':
+        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:
+    # 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 extracted features
+    preview = preview_df(clinical_features)
+    print("Preview of extracted clinical features:")
+    print(preview)
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# Based on the output shown, the gene identifiers are numeric IDs (e.g. '7', '8', '15', etc)
+# These are not standard human gene symbols and will need to be mapped
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file and get meaningful data 
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation shape:", gene_annotation.shape)
+print("\nGene annotation preview:")
+print(preview_df(gene_annotation))
+
+print("\nNumber of non-null values in each column:")
+print(gene_annotation.count())
+
+# Print example rows showing the mapping information columns
+print("\nSample mapping columns ('ID' and 'GENE_SYMBOL'):")
+print("\nFirst 5 rows:")
+print(gene_annotation[['ID', 'GENE_SYMBOL']].head().to_string())
+
+print("\nNote: Gene mapping will use:")
+print("'ID' column: Probe identifiers") 
+print("'GENE_SYMBOL' column: Contains gene symbol information")
+# 1. Get probe-to-gene mapping from annotation data 
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# 2. Apply mapping to get gene expression values
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# 3. Preview the result
+print("Shape of gene expression data after mapping:", gene_data.shape)
+print("\nPreview of gene expression data:")
+print(gene_data.head())
+# 1. Load clinical data and save normalized gene data
+selected_clinical = pd.read_csv(out_clinical_data_file, index_col=0)
+gene_data.index = gene_data.index.str.replace('-mRNA', '')
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features and remove them if needed 
+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="Study examining gene expression changes in adipose tissue under different protein diets during energy restriction"
+)
+
+# 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/Obesity/code/GSE271700.py

@@ -0,0 +1,162 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Obesity"
+cohort = "GSE271700"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Obesity"
+in_cohort_dir = "../DATA/GEO/Obesity/GSE271700"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Obesity/GSE271700.csv"
+out_gene_data_file = "./output/preprocess/3/Obesity/gene_data/GSE271700.csv"
+out_clinical_data_file = "./output/preprocess/3/Obesity/clinical_data/GSE271700.csv"
+json_path = "./output/preprocess/3/Obesity/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
+# This is whole-genome microarray data, so gene expression data should be available
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+trait_row = 3  # phenotype indicates diabetes remission response
+age_row = 1    # age data available 
+gender_row = 0 # 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()
+    if value == 'Responder':
+        return 1
+    elif value == 'Non-Responder':
+        return 0
+    return None
+
+def convert_age(x):
+    if not isinstance(x, str):
+        return None
+    try:
+        return float(x.split(': ')[-1].strip())
+    except:
+        return None
+
+def convert_gender(x):
+    if not isinstance(x, str):
+        return None
+    value = x.split(': ')[-1].strip()
+    if value.lower() == 'female':
+        return 0
+    elif value.lower() == 'male':
+        return 1
+    return None
+
+# 3. Save Metadata
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=trait_row is not None)
+
+# 4. Clinical Feature Extraction
+# Since trait_row is not None, we 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 of selected clinical features:")
+print(preview_df(selected_clinical))
+
+# Save to CSV
+selected_clinical.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+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)
+# Based on the observance of gene identifiers like "100009676_at", "10000_at", etc,
+# these are probe IDs from an Affymetrix microarray platform, not human gene symbols.
+# The "_at" suffix is a characteristic identifier format used by Affymetrix arrays.
+# These probe IDs need to be mapped to their corresponding gene symbols.
+
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file and get meaningful data
+# Read first 100 lines to inspect structure
+with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
+    print("First 100 lines from SOFT file to inspect structure:")
+    for i, line in enumerate(f):
+        if i < 100:  # Preview structure
+            print(line.strip())
+        else:
+            break
+# Extract gene annotation from SOFT file
+gene_annotation = get_gene_annotation(soft_file)
+
+# Get platform ID from SOFT file
+platform_id = None
+with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
+    for line in f:
+        if line.startswith('!Platform_geo_accession'):
+            platform_id = line.split('=')[1].strip()
+            break
+
+print(f"Dataset uses platform: {platform_id}")
+print("Warning: Gene symbol mapping information is not available in the SOFT file or pre-compiled GPL mappings.")
+print("Saving probe-level expression data for future mapping when platform annotation becomes available.")
+
+# Save probe-level expression data
+gene_data.to_csv(out_gene_data_file)
+
+raise ValueError(
+    f"Cannot complete preprocessing: Platform {platform_id} annotation data is required for mapping "
+    "probe IDs to gene symbols, but the mapping information is not available. "
+    "Please obtain the platform annotation data and rerun preprocessing."
+)

p3/preprocess/Obesity/code/GSE281144.py

@@ -0,0 +1,223 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Obesity"
+cohort = "GSE281144"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Obesity"
+in_cohort_dir = "../DATA/GEO/Obesity/GSE281144"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Obesity/GSE281144.csv"
+out_gene_data_file = "./output/preprocess/3/Obesity/gene_data/GSE281144.csv"
+out_clinical_data_file = "./output/preprocess/3/Obesity/clinical_data/GSE281144.csv"
+json_path = "./output/preprocess/3/Obesity/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 the series summary mentioning "gene expression (GE) determined by microarray"
+is_gene_available = True
+
+# 2.1 Data Row Identifiers
+# Trait (diabetes status) is in row 1
+trait_row = 1  
+# No age data available 
+age_row = None
+# Gender data in row 0
+gender_row = 0
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> Optional[int]:
+    """Convert diabetes status to binary (0: Control, 1: Diabetic)"""
+    if not isinstance(value, str):
+        return None
+    value = value.lower()
+    if 'diabetic' in value:
+        return 1 
+    elif 'control' in value:
+        return 0
+    return None
+
+def convert_gender(value: str) -> Optional[int]:
+    """Convert gender to binary (0: Female, 1: Male)"""
+    if not isinstance(value, str):
+        return None
+    value = value.lower()
+    if ':' in value:
+        value = value.split(':')[1].strip()
+    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. 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,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the processed clinical data
+    preview = preview_df(clinical_features)
+    print("Preview of processed clinical data:", preview)
+    
+    # Save clinical features
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# Looking at the identifiers ending in '_st', these are from an Affymetrix microarray platform
+# and need to be mapped to human gene symbols for proper analysis
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation by reading the SOFT file and skipping header lines
+with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for line in f:
+        if line.startswith('!platform_table_begin'):
+            next(f)  # Skip the header line
+            for data_line in f:
+                if data_line.startswith('!platform_table_end'):
+                    break
+                lines.append(data_line)
+            break
+
+# Convert to DataFrame
+gene_annotation = pd.read_csv(io.StringIO(''.join(lines)), sep='\t')
+
+# Preview columns and content
+print("Gene annotation shape:", gene_annotation.shape)
+print("\nColumns in annotation data:")
+print(gene_annotation.columns.tolist())
+
+# Print example rows showing probe ID and gene symbol columns 
+print("\nFirst 5 rows of key mapping columns:")
+if 'ID' in gene_annotation.columns and 'Gene Symbol' in gene_annotation.columns:
+    print(gene_annotation[['ID', 'Gene Symbol']].head().to_string())
+else:
+    # Show all columns for the first few rows to identify mapping information
+    print(gene_annotation.head().to_string())
+# Create clean probe ID column
+gene_annotation['ID'] = gene_annotation.iloc[:, 0].str.split('.').str[0] + '_st'
+
+# Extract gene symbols from annotation strings
+def extract_genes(annotation):
+    if pd.isna(annotation):
+        return []
+    parts = str(annotation).split(' // ')
+    # Gene symbols typically appear after accession IDs
+    symbols = [parts[i] for i in range(1, len(parts), 3) if i < len(parts)]
+    return symbols
+
+# Create mapping dataframe with probe IDs and gene symbols
+mapping_data = pd.DataFrame({
+    'ID': gene_annotation['ID'],
+    'Gene': gene_annotation.iloc[:, 7].apply(extract_genes)
+})
+
+# Apply mapping using library function
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Preview results
+print("Shape of gene expression data after mapping:", gene_data.shape)
+print("\nFirst few rows of mapped gene data:")
+print(gene_data.head())
+# Create clean probe ID column
+gene_annotation['ID'] = gene_annotation.iloc[:, 0].str.split('.').str[0] + '_st'
+
+# Create mapping dataframe with probe IDs and gene symbols using extract_human_gene_symbols
+mapping_data = pd.DataFrame({
+    'ID': gene_annotation['ID'], 
+    'Gene': gene_annotation.iloc[:, 7].apply(extract_human_gene_symbols)
+})
+
+# Apply mapping using library function
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# 1. Load clinical data and save normalized gene data
+selected_clinical = pd.read_csv(out_clinical_data_file, index_col=0)
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features and remove them if needed 
+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="Study examining gene expression changes in adipose tissue under different protein diets during energy restriction"
+)
+
+# 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/Obesity/code/GSE84046.py

@@ -0,0 +1,203 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Obesity"
+cohort = "GSE84046"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Obesity"
+in_cohort_dir = "../DATA/GEO/Obesity/GSE84046"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Obesity/GSE84046.csv"
+out_gene_data_file = "./output/preprocess/3/Obesity/gene_data/GSE84046.csv"
+out_clinical_data_file = "./output/preprocess/3/Obesity/clinical_data/GSE84046.csv"
+json_path = "./output/preprocess/3/Obesity/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
+# From background info, this study analyzes "whole genome gene expression changes"
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# Trait (BMI) is available in Feature 6 with screening BMI values
+trait_row = 6
+
+# Gender data is available in Feature 4
+gender_row = 4
+
+# Birth dates are given in Feature 5, can calculate age 
+age_row = 5
+
+def convert_trait(val):
+    if not val:
+        return None
+    try:
+        # Extract numeric BMI value after colon
+        bmi = float(val.split(": ")[1])
+        # Convert to binary obesity status (BMI >= 30 is obese)
+        return 1 if bmi >= 30 else 0
+    except:
+        return None
+
+def convert_gender(val):
+    if not val:
+        return None
+    try:
+        gender = val.split(": ")[1].lower()
+        return 1 if gender == "male" else 0 if gender == "female" else None
+    except:
+        return None
+
+def convert_age(val):
+    if not val:
+        return None
+    try:
+        # Extract birth year from date string
+        birth_year = int(val.split(": ")[1].split("-")[0])
+        # Study was conducted in 2012 based on accession info
+        study_year = 2012
+        return study_year - birth_year
+    except:
+        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=True
+)
+
+# 4. Extract clinical features
+selected_clinical_df = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Preview the extracted features
+preview_dict = preview_df(selected_clinical_df)
+print("Preview of extracted clinical features:", preview_dict)
+
+# Save clinical data
+selected_clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# Based on the output shown above, the gene expression data uses numeric probe IDs '7892501', '7892502', etc.
+# These are microarray probe identifiers and need to be mapped to human gene symbols.
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file and get meaningful data 
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation shape:", gene_annotation.shape)
+print("\nGene annotation preview:")
+print(preview_df(gene_annotation))
+
+print("\nNumber of non-null values in each column:")
+print(gene_annotation.count())
+
+# Print example rows showing the mapping information columns
+print("\nSample mapping columns ('ID' and gene_assignment):")
+print("\nFirst 5 rows:")
+print(gene_annotation[['ID', 'gene_assignment']].head().to_string())
+
+print("\nNote: Gene mapping will use:")
+print("'ID' column: Probe identifiers") 
+print("'gene_assignment' column: Contains gene symbol information")
+# Get gene mapping from annotation data
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')
+
+# Apply gene mapping to convert probe data to gene expression data
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Preview mapped gene data
+print("Shape of mapped gene expression data:", gene_data.shape)
+print("\nFirst few rows of mapped data:")
+print(gene_data.head())
+print("\nFirst 20 gene symbols:")
+print(gene_data.index[:20])
+
+# Save mapped gene expression data
+gene_data.to_csv(out_gene_data_file)
+# 1. Load clinical data and save normalized gene data
+selected_clinical = pd.read_csv(out_clinical_data_file, index_col=0)
+gene_data.index = gene_data.index.str.replace('-mRNA', '')
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features and remove them if needed 
+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="Study examining gene expression changes in adipose tissue under different protein diets during energy restriction"
+)
+
+# 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/Obesity/code/GSE99725.py

@@ -0,0 +1,139 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Obesity"
+cohort = "GSE99725"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Obesity"
+in_cohort_dir = "../DATA/GEO/Obesity/GSE99725"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Obesity/GSE99725.csv"
+out_gene_data_file = "./output/preprocess/3/Obesity/gene_data/GSE99725.csv"
+out_clinical_data_file = "./output/preprocess/3/Obesity/clinical_data/GSE99725.csv"
+json_path = "./output/preprocess/3/Obesity/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Based on Series summary mentioning "whole-genome expression profiling" from blood
+is_gene_available = True
+
+# 2.1 Data Availability
+# Trait (obesity) - constant since all subjects are obese
+trait_row = None
+
+# Age - not available in sample characteristics 
+age_row = None 
+
+# Gender - not available in sample characteristics
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    # Not needed since trait data not available (constant)
+    return None
+
+def convert_age(x):
+    # Not needed since age data not available
+    return None
+    
+def convert_gender(x):
+    # Not needed since gender data not available
+    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. Clinical Feature Extraction
+# Skip since trait_row is None
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# Based on the gene identifiers starting with "A_19_" format, these are Agilent probes, not gene symbols
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file and get meaningful data 
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation shape:", gene_annotation.shape)
+print("\nGene annotation preview:")
+print(preview_df(gene_annotation))
+
+print("\nNumber of non-null values in each column:")
+print(gene_annotation.count())
+
+# Print example rows showing the mapping information columns
+print("\nSample mapping columns ('ID' and 'GENE_SYMBOL'):")
+print("\nFirst 5 rows:")
+print(gene_annotation[['ID', 'GENE_SYMBOL']].head().to_string())
+
+print("\nNote: Gene mapping will use:")
+print("'ID' column: Probe identifiers") 
+print("'GENE_SYMBOL' column: Contains gene symbol information")
+# Extract the mapping between probe IDs and gene symbols
+mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# Apply mapping to convert probe data to gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+# Create minimal DataFrame to represent constant trait
+minimal_df = pd.DataFrame({'Obesity': [1]})  # All subjects are obese
+
+# Record that dataset is unusable due to constant trait
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True, 
+    is_trait_available=False,  # No variable trait data
+    is_biased=True,  # Constant trait is maximally biased
+    df=minimal_df, 
+    note="Dataset contains only obese patients (constant trait) and lacks age/gender information"
+)

p3/preprocess/Obesity/code/TCGA.py

@@ -0,0 +1,107 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Obesity"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Obesity/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Obesity/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Obesity/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Obesity/cohort_info.json"
+
+# 1. We'll use TCGA_Breast_Cancer as obesity is a known risk factor for breast cancer
+cohort = "TCGA_Breast_Cancer_(BRCA)"
+cohort_dir = os.path.join(tcga_root_dir, cohort)
+
+# 2. Get paths to clinical and genetic data files
+clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+# 3. Load the data
+clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t')
+genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t')
+
+# 4. Print clinical data columns
+print("Clinical data columns:")
+print(clinical_df.columns.tolist())
+
+# Check initial data availability
+is_gene_available = len(genetic_df) > 0  
+is_trait_available = 'BMI' in clinical_df.columns or any('bmi' in col.lower() for col in clinical_df.columns)
+
+# Record initial 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=is_trait_available
+)
+# 1. Identify candidate demographic columns
+candidate_age_cols = ["Age_at_Initial_Pathologic_Diagnosis_nature2012", "age_at_initial_pathologic_diagnosis"]
+candidate_gender_cols = ["Gender_nature2012", "gender"]
+
+# 2. Preview the column data
+# Read in clinical data directly from pre-loaded DataFrame clinical_df
+age_preview = clinical_df[candidate_age_cols].head(5).to_dict(orient='list')
+print("Age columns preview:", age_preview)
+
+# Extract and preview gender columns
+gender_preview = clinical_df[candidate_gender_cols].head(5).to_dict(orient='list')
+print("Gender columns preview:", gender_preview)
+# Select age column based on preview data - age_at_initial_pathologic_diagnosis has valid numeric values
+age_col = "age_at_initial_pathologic_diagnosis"
+
+# Select gender column based on preview data - gender has valid string values 
+gender_col = "gender"
+
+# Print chosen columns
+print("Selected age column:", age_col)
+print("Selected gender column:", gender_col)
+# 1. Extract and standardize clinical features
+# Create a new dataframe to store sample ID as trait with same dimensions as genetic data
+sample_df = pd.DataFrame(index=genetic_df.columns)
+sample_df[trait] = -1  # Fill with invalid values since trait unavailable
+clinical_features = sample_df
+
+# Save clinical data
+os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+clinical_features.to_csv(out_clinical_data_file)
+
+# 2. Normalize gene symbols and save
+normalized_gene_df = normalize_gene_symbols_in_index(genetic_df)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_df.to_csv(out_gene_data_file)
+
+# 3. Link clinical and genetic data on sample IDs
+linked_data = pd.merge(
+    clinical_features, 
+    normalized_gene_df.T,
+    left_index=True,
+    right_index=True,
+    how='inner'
+)
+
+# 4. Handle missing values systematically 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 5. Check for bias in trait and demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 6. Validate data quality and save cohort info
+note = "Dataset contains gene expression data but lacks obesity/BMI information in clinical data. All samples marked with invalid trait values."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=trait_biased,
+    df=linked_data,
+    note=note
+)
+
+# 7. Skip saving linked data since trait unavailable, making dataset unusable

p3/preprocess/Obesity/gene_data/GSE123086.csv

@@ -0,0 +1 @@
+Gene,GSM3494884,GSM3494885,GSM3494886,GSM3494887,GSM3494888,GSM3494889,GSM3494890,GSM3494891,GSM3494892,GSM3494893,GSM3494894,GSM3494895,GSM3494896,GSM3494897,GSM3494898,GSM3494899,GSM3494900,GSM3494901,GSM3494902,GSM3494903,GSM3494904,GSM3494905,GSM3494906,GSM3494907,GSM3494908,GSM3494909,GSM3494910,GSM3494911,GSM3494912,GSM3494913,GSM3494914,GSM3494915,GSM3494916,GSM3494917,GSM3494918,GSM3494919,GSM3494920,GSM3494921,GSM3494922,GSM3494923,GSM3494924,GSM3494925,GSM3494926,GSM3494927,GSM3494928,GSM3494929,GSM3494930,GSM3494931,GSM3494932,GSM3494933,GSM3494934,GSM3494935,GSM3494936,GSM3494937,GSM3494938,GSM3494939,GSM3494940,GSM3494941,GSM3494942,GSM3494943,GSM3494944,GSM3494945,GSM3494946,GSM3494947,GSM3494948,GSM3494949,GSM3494950,GSM3494951,GSM3494952,GSM3494953,GSM3494954,GSM3494955,GSM3494956,GSM3494957,GSM3494958,GSM3494959,GSM3494960,GSM3494961,GSM3494962,GSM3494963,GSM3494964,GSM3494965,GSM3494966,GSM3494967,GSM3494968,GSM3494969,GSM3494970,GSM3494971,GSM3494972,GSM3494973,GSM3494974,GSM3494975,GSM3494976,GSM3494977,GSM3494978,GSM3494979,GSM3494980,GSM3494981,GSM3494982,GSM3494983,GSM3494984,GSM3494985,GSM3494986,GSM3494987,GSM3494988,GSM3494989,GSM3494990,GSM3494991,GSM3494992,GSM3494993,GSM3494994,GSM3494995,GSM3494996,GSM3494997,GSM3494998,GSM3494999,GSM3495000,GSM3495001,GSM3495002,GSM3495003,GSM3495004,GSM3495005,GSM3495006,GSM3495007,GSM3495008,GSM3495009,GSM3495010,GSM3495011,GSM3495012,GSM3495013,GSM3495014,GSM3495015,GSM3495016,GSM3495017,GSM3495018,GSM3495019,GSM3495020,GSM3495021,GSM3495022,GSM3495023,GSM3495024,GSM3495025,GSM3495026,GSM3495027,GSM3495028,GSM3495029,GSM3495030,GSM3495031,GSM3495032,GSM3495033,GSM3495034,GSM3495035,GSM3495036,GSM3495037,GSM3495038,GSM3495039,GSM3495040,GSM3495041,GSM3495042,GSM3495043,GSM3495044,GSM3495045,GSM3495046,GSM3495047,GSM3495048,GSM3495049

p3/preprocess/Obesity/gene_data/GSE123088.csv

@@ -0,0 +1 @@
+ID,GSM3494884,GSM3494885,GSM3494886,GSM3494887,GSM3494888,GSM3494889,GSM3494890,GSM3494891,GSM3494892,GSM3494893,GSM3494894,GSM3494895,GSM3494896,GSM3494897,GSM3494898,GSM3494899,GSM3494900,GSM3494901,GSM3494902,GSM3494903,GSM3494904,GSM3494905,GSM3494906,GSM3494907,GSM3494908,GSM3494909,GSM3494910,GSM3494911,GSM3494912,GSM3494913,GSM3494914,GSM3494915,GSM3494916,GSM3494917,GSM3494918,GSM3494919,GSM3494920,GSM3494921,GSM3494922,GSM3494923,GSM3494924,GSM3494925,GSM3494926,GSM3494927,GSM3494928,GSM3494929,GSM3494930,GSM3494931,GSM3494932,GSM3494933,GSM3494934,GSM3494935,GSM3494936,GSM3494937,GSM3494938,GSM3494939,GSM3494940,GSM3494941,GSM3494942,GSM3494943,GSM3494944,GSM3494945,GSM3494946,GSM3494947,GSM3494948,GSM3494949,GSM3494950,GSM3494951,GSM3494952,GSM3494953,GSM3494954,GSM3494955,GSM3494956,GSM3494957,GSM3494958,GSM3494959,GSM3494960,GSM3494961,GSM3494962,GSM3494963,GSM3494964,GSM3494965,GSM3494966,GSM3494967,GSM3494968,GSM3494969,GSM3494970,GSM3494971,GSM3494972,GSM3494973,GSM3494974,GSM3494975,GSM3494976,GSM3494977,GSM3494978,GSM3494979,GSM3494980,GSM3494981,GSM3494982,GSM3494983,GSM3494984,GSM3494985,GSM3494986,GSM3494987,GSM3494988,GSM3494989,GSM3494990,GSM3494991,GSM3494992,GSM3494993,GSM3494994,GSM3494995,GSM3494996,GSM3494997,GSM3494998,GSM3494999,GSM3495000,GSM3495001,GSM3495002,GSM3495003,GSM3495004,GSM3495005,GSM3495006,GSM3495007,GSM3495008,GSM3495009,GSM3495010,GSM3495011,GSM3495012,GSM3495013,GSM3495014,GSM3495015,GSM3495016,GSM3495017,GSM3495018,GSM3495019,GSM3495020,GSM3495021,GSM3495022,GSM3495023,GSM3495024,GSM3495025,GSM3495026,GSM3495027,GSM3495028,GSM3495029,GSM3495030,GSM3495031,GSM3495032,GSM3495033,GSM3495034,GSM3495035,GSM3495036,GSM3495037,GSM3495038,GSM3495039,GSM3495040,GSM3495041,GSM3495042,GSM3495043,GSM3495044,GSM3495045,GSM3495046,GSM3495047,GSM3495048,GSM3495049,GSM3495050,GSM3495051,GSM3495052,GSM3495053,GSM3495054,GSM3495055,GSM3495056,GSM3495057,GSM3495058,GSM3495059,GSM3495060,GSM3495061,GSM3495062,GSM3495063,GSM3495064,GSM3495065,GSM3495066,GSM3495067,GSM3495068,GSM3495069,GSM3495070,GSM3495071,GSM3495072,GSM3495073,GSM3495074,GSM3495075,GSM3495076,GSM3495077,GSM3495078,GSM3495079,GSM3495080,GSM3495081,GSM3495082,GSM3495083,GSM3495084,GSM3495085,GSM3495086,GSM3495087

p3/preprocess/Obesity/gene_data/GSE158237.csv

@@ -0,0 +1 @@
+ID,GSM4795701,GSM4795702,GSM4795703,GSM4795704,GSM4795705,GSM4795706,GSM4795707,GSM4795708,GSM4795709,GSM4795710,GSM4795711,GSM4795712,GSM4795713,GSM4795714,GSM4795715,GSM4795716,GSM4795717,GSM4795718,GSM4795719,GSM4795720,GSM4795721,GSM4795722,GSM4795723,GSM4795724,GSM4795725,GSM4795726,GSM4795727,GSM4795728,GSM4795729,GSM4795730,GSM4795731,GSM4795732,GSM4795733,GSM4795734,GSM4795735,GSM4795736,GSM4795737

p3/preprocess/Obesity/gene_data/GSE281144.csv

@@ -0,0 +1 @@
+Gene,GSM8611649,GSM8611650,GSM8611651,GSM8611652,GSM8611653,GSM8611654,GSM8611655,GSM8611656,GSM8611657,GSM8611658,GSM8611659,GSM8611660,GSM8611661,GSM8611662,GSM8611663,GSM8611664,GSM8611665,GSM8611666,GSM8611667,GSM8611668,GSM8611669,GSM8611670,GSM8611671,GSM8611672,GSM8611673,GSM8611674,GSM8611675,GSM8611676,GSM8611677,GSM8611678,GSM8611679,GSM8611680,GSM8611681,GSM8611682

p3/preprocess/Obsessive-Compulsive_Disorder/clinical_data/GSE60190.csv

@@ -0,0 +1,4 @@
+,GSM1467273,GSM1467274,GSM1467275,GSM1467276,GSM1467277,GSM1467278,GSM1467279,GSM1467280,GSM1467281,GSM1467282,GSM1467283,GSM1467284,GSM1467285,GSM1467286,GSM1467287,GSM1467288,GSM1467289,GSM1467290,GSM1467291,GSM1467292,GSM1467293,GSM1467294,GSM1467295,GSM1467296,GSM1467297,GSM1467298,GSM1467299,GSM1467300,GSM1467301,GSM1467302,GSM1467303,GSM1467304,GSM1467305,GSM1467306,GSM1467307,GSM1467308,GSM1467309,GSM1467310,GSM1467311,GSM1467312,GSM1467313,GSM1467314,GSM1467315,GSM1467316,GSM1467317,GSM1467318,GSM1467319,GSM1467320,GSM1467321,GSM1467322,GSM1467323,GSM1467324,GSM1467325,GSM1467326,GSM1467327,GSM1467328,GSM1467329,GSM1467330,GSM1467331,GSM1467332,GSM1467333,GSM1467334,GSM1467335,GSM1467336,GSM1467337,GSM1467338,GSM1467339,GSM1467340,GSM1467341,GSM1467342,GSM1467343,GSM1467344,GSM1467345,GSM1467346,GSM1467347,GSM1467348,GSM1467349,GSM1467350,GSM1467351,GSM1467352,GSM1467353,GSM1467354,GSM1467355,GSM1467356,GSM1467357,GSM1467358,GSM1467359,GSM1467360,GSM1467361,GSM1467362,GSM1467363,GSM1467364,GSM1467365,GSM1467366,GSM1467367,GSM1467368,GSM1467369,GSM1467370,GSM1467371,GSM1467372,GSM1467373,GSM1467374,GSM1467375,GSM1467376,GSM1467377,GSM1467378,GSM1467379,GSM1467380,GSM1467381,GSM1467382,GSM1467383,GSM1467384,GSM1467385,GSM1467386,GSM1467387,GSM1467388,GSM1467389,GSM1467390,GSM1467391,GSM1467392,GSM1467393,GSM1467394,GSM1467395,GSM1467396,GSM1467397,GSM1467398,GSM1467399,GSM1467400,GSM1467401,GSM1467402,GSM1467403,GSM1467404,GSM1467405
+Obsessive-Compulsive_Disorder,,0.0,,0.0,0.0,,0.0,0.0,0.0,0.0,0.0,0.0,,,0.0,,0.0,0.0,0.0,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,,0.0,0.0,0.0,,1.0,,,0.0,0.0,0.0,0.0,,,0.0,,1.0,,,0.0,,0.0,0.0,,0.0,,,1.0,,1.0,0.0,0.0,0.0,0.0,0.0,0.0,,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,,0.0,0.0,0.0,0.0,0.0,,0.0,0.0,0.0
+Age,50.421917,27.49863,30.627397,61.167123,32.69589,39.213698,58.605479,49.2,41.041095,51.750684,50.89863,26.745205,29.104109,39.301369,48.978082,57.884931,28.364383,24.041095,19.268493,27.230136,46.605479,23.443835,51.038356,39.663013,46.109589,77.989041,46.967123,63.241095,62.306849,83.641095,42.838356,51.386301,66.715068,51.939726,34.339726,50.109589,18.758904,16.649315,16.353424,42.065753,16.726027,34.465753,34.254794,47.484931,43.756164,49.210958,57.482191,46.561643,49.561643,28.589041,38.410958,30.032876,56.09041,46.915068,49.021917,71.109589,17.235616,16.583561,16.934246,16.8,18.117808,18.660273,16.69589,75.572602,59.260273,55.545205,41.778082,57.454794,45.284931,56.304109,39.654794,55.945205,38.232876,58.109589,40.021917,50.504109,36.550684,45.117808,83.545205,18.786301,48.567123,38.331506,48.101369,18.39452,60.843835,61.372602,52.038356,59.254794,41.567123,50.358904,31.558904,45.701369,44.731506,34.39726,31.613698,54.846575,84.057534,66.79452,53.323287,30.043835,55.435616,45.676712,54.334246,63.558904,45.224657,23.69589,67.865753,16.753424,18.424657,17.09041,16.183561,33.260273,54.424657,45.378082,52.523287,35.273972,22.630136,20.863013,26.531506,24.627397,53.978082,34.961643,18.731506,30.726027,63.471232,54.808219,57.512328,57.610958,44.958904,35.684931,63.0,38.780821,45.978082
+Gender,0.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0

p3/preprocess/Obsessive-Compulsive_Disorder/clinical_data/GSE78104.csv

@@ -0,0 +1,4 @@
+,GSM2067403,GSM2067404,GSM2067405,GSM2067406,GSM2067407,GSM2067408,GSM2067409,GSM2067410,GSM2067411,GSM2067412,GSM2067413,GSM2067414,GSM2067415,GSM2067416,GSM2067417,GSM2067418,GSM2067419,GSM2067420,GSM2067421,GSM2067422,GSM2067423,GSM2067424,GSM2067425,GSM2067426,GSM2067427,GSM2067428,GSM2067429,GSM2067430,GSM2067431,GSM2067432,GSM2067433,GSM2067434,GSM2067435,GSM2067436,GSM2067437,GSM2067438,GSM2067439,GSM2067440,GSM2067441,GSM2067442,GSM2067443,GSM2067444,GSM2067445,GSM2067446,GSM2067447,GSM2067448,GSM2067449,GSM2067450,GSM2067451,GSM2067452,GSM2067453,GSM2067454,GSM2067455,GSM2067456,GSM2067457,GSM2067458,GSM2067459,GSM2067460,GSM2067461,GSM2067462
+Obsessive-Compulsive_Disorder,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+Age,25.0,23.0,18.0,26.0,27.0,19.0,22.0,27.0,18.0,25.0,16.0,35.0,16.0,16.0,32.0,18.0,15.0,43.0,36.0,17.0,45.0,40.0,35.0,28.0,27.0,31.0,23.0,35.0,60.0,59.0,24.0,23.0,18.0,27.0,27.0,20.0,21.0,27.0,20.0,24.0,18.0,35.0,17.0,18.0,32.0,18.0,18.0,44.0,37.0,17.0,43.0,40.0,32.0,28.0,27.0,30.0,24.0,35.0,56.0,56.0
+Gender,1.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0

p3/preprocess/Obsessive-Compulsive_Disorder/code/GSE60190.py

@@ -0,0 +1,182 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Obsessive-Compulsive_Disorder"
+cohort = "GSE60190"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Obsessive-Compulsive_Disorder"
+in_cohort_dir = "../DATA/GEO/Obsessive-Compulsive_Disorder/GSE60190"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Obsessive-Compulsive_Disorder/GSE60190.csv"
+out_gene_data_file = "./output/preprocess/3/Obsessive-Compulsive_Disorder/gene_data/GSE60190.csv"
+out_clinical_data_file = "./output/preprocess/3/Obsessive-Compulsive_Disorder/clinical_data/GSE60190.csv"
+json_path = "./output/preprocess/3/Obsessive-Compulsive_Disorder/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# Yes, this is gene expression data from Illumina HumanHT-12 microarray as stated in Series_overall_design
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Data row identification
+# For trait (OCD): Use row 3 'dx' (diagnosis) which has OCD vs Control
+trait_row = 3
+
+# For age: Available in row 5
+age_row = 5
+
+# For gender: Available in row 7 'Sex'
+gender_row = 7
+
+# 2.2 Data type conversion functions
+def convert_trait(x):
+    """Convert diagnosis to binary: OCD=1, Control=0"""
+    if pd.isna(x):
+        return None
+    value = x.split(': ')[1] if ': ' in x else x
+    if value == 'OCD':
+        return 1
+    elif value == 'Control':
+        return 0
+    else:
+        return None
+
+def convert_age(x):
+    """Convert age to continuous numeric value"""
+    if pd.isna(x):
+        return None
+    value = x.split(': ')[1] if ': ' in x else x
+    try:
+        return float(value)
+    except:
+        return None
+
+def convert_gender(x):
+    """Convert gender to binary: M=1, F=0"""
+    if pd.isna(x):
+        return None
+    value = x.split(': ')[1] if ': ' in x else x
+    if value == 'M':
+        return 1
+    elif value == 'F':
+        return 0
+    else:
+        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. Clinical Feature Extraction
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the data
+    print("Preview of clinical features:")
+    print(preview_df(selected_clinical_df))
+    
+    # Save to CSV
+    selected_clinical_df.to_csv(out_clinical_data_file)
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# Identifiers starting with ILMN_ indicate these are Illumina probe IDs, not gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Display column names and preview data
+print("Column names:")
+print(gene_annotation.columns)
+
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_annotation))
+# 1. ID column stores Illumina probe IDs, Symbol column stores gene symbols
+# 2. Extract mapping between probe IDs and gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# 3. Apply gene mapping to convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Print head of resulting gene expression data
+print("\nFirst few rows of gene expression data:")
+print(gene_data.head())
+
+print("\nShape:", gene_data.shape)
+# Reload clinical data that was processed earlier
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 1. Normalize gene symbols
+genetic_data = normalize_gene_symbols_in_index(gene_data)
+genetic_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)
+
+# 3. Handle missing values systematically  
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias in trait and demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and information saving
+note = "Contains gene expression data with metabolic rate (inferred from multicentric occurrence-free survival days) measurements"
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort, 
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note=note
+)
+
+# 6. Save linked data only 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/Obsessive-Compulsive_Disorder/code/GSE78104.py

@@ -0,0 +1,180 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Obsessive-Compulsive_Disorder"
+cohort = "GSE78104"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Obsessive-Compulsive_Disorder"
+in_cohort_dir = "../DATA/GEO/Obsessive-Compulsive_Disorder/GSE78104"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Obsessive-Compulsive_Disorder/GSE78104.csv"
+out_gene_data_file = "./output/preprocess/3/Obsessive-Compulsive_Disorder/gene_data/GSE78104.csv"
+out_clinical_data_file = "./output/preprocess/3/Obsessive-Compulsive_Disorder/clinical_data/GSE78104.csv"
+json_path = "./output/preprocess/3/Obsessive-Compulsive_Disorder/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# Yes, based on the Series title and design, this dataset contains mRNA expression data
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+trait_row = 1  # disease state row contains trait info
+age_row = 3    # age information available 
+gender_row = 2 # gender information available
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    # Binary: Convert OCD vs control
+    if not isinstance(x, str):
+        return None
+    value = x.split(': ')[-1].lower()
+    if 'obsessive-compulsive disorder' in value:
+        return 1
+    elif 'normal control' in value:
+        return 0
+    return None
+
+def convert_age(x):
+    # Continuous: Extract age number
+    if not isinstance(x, str):
+        return None
+    value = x.split(': ')[-1].lower()
+    if 'y' in value:
+        try:
+            return int(value.replace('y',''))
+        except:
+            return None
+    return None
+
+def convert_gender(x):
+    # Binary: Female=0, Male=1
+    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
+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
+    print("Preview of selected clinical features:")
+    print(preview_df(selected_clinical))
+    
+    # Save clinical data
+    selected_clinical.to_csv(out_clinical_data_file)
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# Looking at the identifiers in the gene expression data, they appear to be Agilent probe IDs 
+# (starting with "A_19_") and control probes, not standard human gene symbols.
+# Therefore we need to map them to gene symbols.
+
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Display column names and preview data
+print("Column names:")
+print(gene_annotation.columns)
+
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_annotation))
+# 1. From observation:
+# gene expression data uses probe IDs like "A_19_P00315459"
+# gene annotation data has these IDs in the "ID" column
+# gene symbols are in "GeneSymbol" column
+
+# 2. Get gene mapping from annotation data
+prob_col = 'ID'
+gene_col = 'GeneSymbol' 
+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(genetic_data, mapping_df)
+
+# Preview the result
+print("Gene expression data shape:", gene_data.shape)
+print("\nFirst few genes and their expression values:")
+print(gene_data.head())
+# Reload clinical data that was processed earlier
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 1. Normalize gene symbols
+genetic_data = normalize_gene_symbols_in_index(gene_data)
+genetic_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)
+
+# 3. Handle missing values systematically  
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias in trait and demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and information saving
+note = "Contains gene expression data with metabolic rate (inferred from multicentric occurrence-free survival days) measurements"
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort, 
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note=note
+)
+
+# 6. Save linked data only 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/Obsessive-Compulsive_Disorder/code/TCGA.py

@@ -0,0 +1,28 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Obsessive-Compulsive_Disorder"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Obsessive-Compulsive_Disorder/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Obsessive-Compulsive_Disorder/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Obsessive-Compulsive_Disorder/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Obsessive-Compulsive_Disorder/cohort_info.json"
+
+# Check for OCD-related directory in TCGA
+# Since TCGA is focused on cancer, no directory is related to OCD
+is_gene_available = False
+is_trait_available = False
+
+# Mark task as completed and save info about data unavailability
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort="TCGA", 
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)

p3/preprocess/Obsessive-Compulsive_Disorder/cohort_info.json

@@ -0,0 +1 @@
+{"GSE78104": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 60, "note": "Contains gene expression data with metabolic rate (inferred from multicentric occurrence-free survival days) measurements"}, "GSE60190": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 108, "note": "Contains gene expression data with metabolic rate (inferred from multicentric occurrence-free survival days) measurements"}, "TCGA": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}

p3/preprocess/Obstructive_sleep_apnea/clinical_data/GSE133601.csv

@@ -0,0 +1,2 @@
+,GSM3912810,GSM3912811,GSM3912812,GSM3912813,GSM3912814,GSM3912815,GSM3912816,GSM3912817,GSM3912818,GSM3912819,GSM3912820,GSM3912821,GSM3912822,GSM3912823,GSM3912824,GSM3912825,GSM3912826,GSM3912827,GSM3912828,GSM3912829,GSM3912830,GSM3912831,GSM3912832,GSM3912833,GSM3912834,GSM3912835,GSM3912836,GSM3912837,GSM3912838,GSM3912839
+Obstructive_sleep_apnea,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0

p3/preprocess/Obstructive_sleep_apnea/clinical_data/GSE49800.csv

@@ -0,0 +1,2 @@
+,GSM1207208,GSM1207209,GSM1207210,GSM1207211,GSM1207212,GSM1207213,GSM1207214,GSM1207215,GSM1207216,GSM1207217,GSM1207218,GSM1207219,GSM1207220,GSM1207221,GSM1207222,GSM1207223,GSM1207224,GSM1207225,GSM1207226,GSM1207227,GSM1207228,GSM1207229,GSM1207230,GSM1207231,GSM1207232,GSM1207233,GSM1207234,GSM1207235,GSM1207236,GSM1207237,GSM1207238,GSM1207239,GSM1207240,GSM1207241,GSM1207242,GSM1207243
+Obstructive_sleep_apnea,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0

p3/preprocess/Obstructive_sleep_apnea/clinical_data/GSE75097.csv

@@ -0,0 +1,4 @@
+,GSM1942590,GSM1942591,GSM1942592,GSM1942593,GSM1942594,GSM1942595,GSM1942596,GSM1942597,GSM1942598,GSM1942599,GSM1942600,GSM1942601,GSM1942602,GSM1942603,GSM1942604,GSM1942605,GSM1942606,GSM1942607,GSM1942608,GSM1942609,GSM1942610,GSM1942611,GSM1942612,GSM1942613,GSM1942614,GSM1942615,GSM1942616,GSM1942617,GSM1942618,GSM1942619,GSM1942620,GSM1942621,GSM1942622,GSM1942623,GSM1942624,GSM1942625,GSM1942626,GSM1942627,GSM1942628,GSM1942629,GSM1942630,GSM1942631,GSM1942632,GSM1942633,GSM1942634,GSM1942635,GSM1942636,GSM1942637
+Obstructive_sleep_apnea,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0
+Age,54.0,31.0,44.0,60.0,21.0,50.0,52.0,58.0,42.0,34.0,58.0,37.0,60.0,59.0,27.0,57.0,68.0,53.0,58.0,52.0,36.0,38.0,50.0,44.0,58.0,54.0,43.0,59.0,44.0,46.0,36.0,59.0,49.0,59.0,68.0,61.0,38.0,45.0,35.0,57.0,42.0,44.0,47.0,50.0,54.0,50.0,47.0,38.0
+Gender,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0