p3/preprocess/Multiple_sclerosis/code/GSE146383.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Multiple_sclerosis"
cohort = "GSE146383"

# Input paths
in_trait_dir = "../DATA/GEO/Multiple_sclerosis"
in_cohort_dir = "../DATA/GEO/Multiple_sclerosis/GSE146383"

# Output paths
out_data_file = "./output/preprocess/3/Multiple_sclerosis/GSE146383.csv"
out_gene_data_file = "./output/preprocess/3/Multiple_sclerosis/gene_data/GSE146383.csv"
out_clinical_data_file = "./output/preprocess/3/Multiple_sclerosis/clinical_data/GSE146383.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, this dataset contains gene expression data from blood mononuclear cells
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
trait_row = None  # MS vs Control not directly available in characteristics
age_row = 1
gender_row = 0

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    # No direct trait data in characteristics, this function won't be used
    return None

def convert_age(x):
    try:
        # Extract numeric value after colon and convert to float
        age = float(x.split(': ')[1])
        return age
    except:
        return None

def convert_gender(x):
    try:
        # Extract value after colon and convert to binary
        gender = x.split(': ')[1].strip()
        if gender.lower() == 'female':
            return 0
        elif gender.lower() == 'male':
            return 1
        return None
    except:
        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=False)  # Since trait_row is None

# 4. Skip clinical feature extraction since trait_row is None
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

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

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

# Inspect a snippet of raw file to verify identifier format
import gzip
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
    lines = []
    for i, line in enumerate(f):
        if "!series_matrix_table_begin" in line:
            # Get the next 5 lines after the marker
            for _ in range(5):
                lines.append(next(f).strip())
            break
print("\nFirst few lines after matrix marker in raw file:")
for line in lines:
    print(line)
# Looking at the identifiers (e.g. "1007_s_at", "1053_at"), these are not human gene symbols but probe IDs from an Affymetrix microarray (likely HG-U133 series)
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))
# Get probe-gene mapping
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

# Apply mapping to convert probe-level data to gene-level 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. Create clinical data from age and gender information
ages = clinical_data.iloc[1].str.extract(r'age \(years\): (\d+\.?\d*)')[0].astype(float)
genders = clinical_data.iloc[0].map(lambda x: 0 if 'female' in x.lower() else 1 if 'male' in x.lower() else None)

clinical_data = pd.DataFrame({
    trait: (ages <= 18).astype(int),
    'Age': ages,
    'Gender': genders
}).T

# 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)