p3/preprocess/Chronic_Fatigue_Syndrome/code/GSE39684.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Chronic_Fatigue_Syndrome"
cohort = "GSE39684"

# Input paths
in_trait_dir = "../DATA/GEO/Chronic_Fatigue_Syndrome"
in_cohort_dir = "../DATA/GEO/Chronic_Fatigue_Syndrome/GSE39684"

# Output paths
out_data_file = "./output/preprocess/3/Chronic_Fatigue_Syndrome/GSE39684.csv"
out_gene_data_file = "./output/preprocess/3/Chronic_Fatigue_Syndrome/gene_data/GSE39684.csv"
out_clinical_data_file = "./output/preprocess/3/Chronic_Fatigue_Syndrome/clinical_data/GSE39684.csv"
json_path = "./output/preprocess/3/Chronic_Fatigue_Syndrome/cohort_info.json"

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

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

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

# Print background info
print("=== Dataset Background Information ===")
print(background_info)
print("\n=== Sample Characteristics ===")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# Yes, this is microarray data from prostate tissue samples analyzing genes
is_gene_available = True

# 2. Clinical Feature Analysis
# 2.1 Data Availability
# From cohort in sample char row 1, we can infer cohort year represents CFS cases vs controls
trait_row = 1
# No age information available
age_row = None  
# No gender information available 
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(val):
    if not isinstance(val, str):
        return None
    # Cohort 2006 = CFS cases, 2012 = controls
    if "cohort:" in val:
        year = val.split(":")[1].strip()
        if year == "2006":
            return 1  # Cases
        elif year == "2012": 
            return 0  # Controls
    return None

def convert_age(val):
    # No age data
    return None

def convert_gender(val):
    # No gender data
    return None

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

# 4. Clinical Feature Extraction
if trait_row is not None:
    clinical_features = geo_select_clinical_features(
        clinical_df=clinical_data,
        trait=trait,
        trait_row=trait_row,
        convert_trait=convert_trait,
        age_row=age_row,
        convert_age=convert_age,
        gender_row=gender_row, 
        convert_gender=convert_gender
    )
    
    print("Preview of extracted clinical features:")
    print(preview_df(clinical_features))
    
    # Save clinical features
    clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_df = get_genetic_data(matrix_file)

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

print("\nPreview of first few rows and columns:")
print(genetic_df.head().iloc[:, :5])
# These identifiers appear to be custom probe IDs from a microarray platform (XXX-V3-70mer format)
# and will need to be mapped to official gene symbols
requires_gene_mapping = True
# Extract gene annotation data, excluding control probe lines
gene_metadata = get_gene_annotation(soft_file)

# Preview filtered annotation data
print("Column names:")
print(gene_metadata.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_metadata))

print("\nIMPORTANT NOTE: After reviewing the gene annotation data,")
print("it is clear this dataset contains viral gene expression data (Parvovirus, Retrovirus etc.)")
print("rather than human gene expression data. Therefore this dataset is not suitable for human trait analysis.")

# Invalidate our previous assessment
is_gene_available = False

# Re-run validation with updated gene availability
validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=trait_row is not None
)