p3/preprocess/Chronic_Fatigue_Syndrome/code/GSE67311.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Chronic_Fatigue_Syndrome"
cohort = "GSE67311"

# Input paths
in_trait_dir = "../DATA/GEO/Chronic_Fatigue_Syndrome"
in_cohort_dir = "../DATA/GEO/Chronic_Fatigue_Syndrome/GSE67311"

# Output paths
out_data_file = "./output/preprocess/3/Chronic_Fatigue_Syndrome/GSE67311.csv"
out_gene_data_file = "./output/preprocess/3/Chronic_Fatigue_Syndrome/gene_data/GSE67311.csv"
out_clinical_data_file = "./output/preprocess/3/Chronic_Fatigue_Syndrome/clinical_data/GSE67311.csv"
json_path = "./output/preprocess/3/Chronic_Fatigue_Syndrome/cohort_info.json"

# Get paths to the SOFT and matrix files
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values for each feature (row) in clinical data 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("=== Dataset Background Information ===")
print(background_info)
print("\n=== Sample Characteristics ===")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# Dataset uses Affymetrix Human Gene arrays, indicating gene expression data is available
is_gene_available = True

# 2. Variable Availability and Data Type
# 2.1 Row identification
trait_row = 8  # Chronic fatigue syndrome status in row 8
age_row = None  # Age not available 
gender_row = None  # Gender not available

# 2.2 Data type conversion functions
def convert_trait(value: str) -> int:
    """Convert CFS status to binary (0: No CFS, 1: Has CFS)"""
    if pd.isna(value):
        return None
    value = value.lower().split(': ')[-1]
    if value == 'yes':
        return 1
    elif value == 'no':
        return 0
    return None

def convert_age(value: str) -> float:
    """Convert age to float - not used as age unavailable"""
    return None

def convert_gender(value: str) -> int:
    """Convert gender to binary - not used as gender unavailable"""
    return None

# 3. Save initial metadata
validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=trait_row is not None
)

# 4. Clinical Feature Extraction
# Since trait_row is not None, extract clinical features
clinical_df = geo_select_clinical_features(
    clinical_df=clinical_data,
    trait=trait,
    trait_row=trait_row,
    convert_trait=convert_trait,
    age_row=age_row,
    convert_age=convert_age,
    gender_row=gender_row,
    convert_gender=convert_gender
)

# Preview the extracted features
print("Preview of clinical features:")
print(preview_df(clinical_df))

# Save clinical features
clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_df = get_genetic_data(matrix_file)

# Print DataFrame shape and first 20 row IDs
print("DataFrame shape:", genetic_df.shape)
print("\nFirst 20 row IDs:")
print(genetic_df.index[:20])

print("\nPreview of first few rows and columns:")
print(genetic_df.head().iloc[:, :5])
# Gene probe identifier pattern suggests they are not gene symbols
# The identifiers are numeric values in the format 7XXXXXX, which appear to be Illumina probe IDs
# We will need to perform identifier mapping
requires_gene_mapping = True
# Extract gene annotation data, excluding control probe lines
gene_metadata = get_gene_annotation(soft_file) 

# Preview filtered annotation data
print("Column names:")
print(gene_metadata.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_metadata))
# 1. Identify columns for mapping
# 'ID' column in gene_metadata contains probe IDs matching genetic_df index
# 'gene_assignment' column contains gene symbols in the format "NM_XXX // GENESYMBOL // description"

# 2. Get mapping dataframe by extracting probe IDs and gene symbols
# Use text extraction to get gene symbols from gene_assignment strings
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='gene_assignment')

# 3. Apply mapping to convert probe data to gene expression data
gene_data = apply_gene_mapping(genetic_df, mapping_df)

# Print info about the mapping result
print(f"Original probe data shape: {genetic_df.shape}")
print(f"Gene expression data shape: {gene_data.shape}")
print("\nPreview of gene expression data:")
print(gene_data.head().iloc[:, :5])
# 1. Normalize gene symbols and save
gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data  
linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)

# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)

# 4. Check for biased features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and metadata saving
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path, 
    is_gene_available=is_gene_available,
    is_trait_available=True,
    is_biased=trait_biased,
    df=linked_data,
    note="Dataset contains gene expression data from blood samples used to study chronic fatigue syndrome"
)

# 6. Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)