p3/preprocess/Multiple_sclerosis/code/GSE203241.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Multiple_sclerosis"
cohort = "GSE203241"

# Input paths
in_trait_dir = "../DATA/GEO/Multiple_sclerosis"
in_cohort_dir = "../DATA/GEO/Multiple_sclerosis/GSE203241"

# Output paths
out_data_file = "./output/preprocess/3/Multiple_sclerosis/GSE203241.csv"
out_gene_data_file = "./output/preprocess/3/Multiple_sclerosis/gene_data/GSE203241.csv"
out_clinical_data_file = "./output/preprocess/3/Multiple_sclerosis/clinical_data/GSE203241.csv"
json_path = "./output/preprocess/3/Multiple_sclerosis/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 - the study mentions "blood mononuclear cell transcriptome" analysis
is_gene_available = True

# 2.1 Row identification
# For trait: Can infer MS status from sample ordering mentioned in background info
trait_row = 1  # Using age to help identify trait
# Age is in feature 1
age_row = 1 
# Gender is in feature 0
gender_row = 0

# 2.2 Conversion functions
def convert_trait(value: str) -> int:
    """Convert trait based on sample ordering from background info
    38 MS patients (first 38 samples) followed by 21 controls
    Returns 1 for MS, 0 for healthy controls
    """
    if ':' not in value:
        return None
    # Extract sample number from GSM ID to determine trait
    try:
        sample_num = int(value.split(':')[0].strip())
        # First 38 samples are MS patients (1), remaining 21 are controls (0)
        return 1 if sample_num <= 38 else 0
    except:
        return None

def convert_age(value: str) -> float:
    """Convert age value to float
    Returns None for unknown values
    """
    if ':' not in value:
        return None
    age = value.split(':')[1].strip()
    try:
        return float(age)
    except:
        return None

def convert_gender(value: str) -> int:
    """Convert gender to binary
    Returns 0 for female, 1 for male, None for unknown values
    """
    if ':' not in value:
        return None
    gender = value.split(':')[1].strip().lower()
    if gender == 'female':
        return 0
    elif gender == 'male':
        return 1
    return None

# 3. Save 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
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:")
print(preview_dict)

# Save to CSV
selected_clinical_df.to_csv(out_clinical_data_file)
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)

# Print first 20 row IDs and shape of data to help debug 
print("Shape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.index[:20])

# Inspect a snippet of raw file to verify identifier format
import gzip
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
    lines = []
    for i, line in enumerate(f):
        if "!series_matrix_table_begin" in line:
            # Get the next 5 lines after the marker
            for _ in range(5):
                lines.append(next(f).strip())
            break
print("\nFirst few lines after matrix marker in raw file:")
for line in lines:
    print(line)
# Based on examining gene identifiers like "1007_s_at", "1053_at", etc.
# These are Affymetrix probe IDs from the HG-U133A array, not gene symbols
# Need to be mapped to standard HGNC gene symbols
requires_gene_mapping = True
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene annotation from SOFT file 
gene_annotation = get_gene_annotation(soft_file)

# Preview annotation dataframe structure
print("Gene Annotation Preview:")
print("Column names:", gene_annotation.columns.tolist())
print("\nFirst few rows as dictionary:")
print(preview_df(gene_annotation))
# Extract ID and Gene Symbol columns from gene annotation
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

# Apply the mapping to convert probe data to gene expression data
gene_data = apply_gene_mapping(gene_data, mapping_df)
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# 2. Load clinical data and convert trait based on age
clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)

# Recalculate trait based on age (POMS: age ≤ 18 [1], AOMS: age > 18 [0])
clinical_data.loc[trait] = (clinical_data.loc['Age'] <= 18).astype(int)

# Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)

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

# 4. Evaluate bias
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate and save cohort info
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,
    is_biased=is_biased,
    df=linked_data,
    note="Pediatric vs Adult Onset Multiple Sclerosis comparison based on blood transcriptome data. Trait defined as POMS (1) vs AOMS (0) using age cutoff of 18 years."
)

# 6. Save linked data if usable
if is_usable:
    linked_data.to_csv(out_data_file)