p3/preprocess/Multiple_sclerosis/code/GSE131279.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Multiple_sclerosis"
cohort = "GSE131279"

# Input paths
in_trait_dir = "../DATA/GEO/Multiple_sclerosis"
in_cohort_dir = "../DATA/GEO/Multiple_sclerosis/GSE131279"

# Output paths
out_data_file = "./output/preprocess/3/Multiple_sclerosis/GSE131279.csv"
out_gene_data_file = "./output/preprocess/3/Multiple_sclerosis/gene_data/GSE131279.csv"
out_clinical_data_file = "./output/preprocess/3/Multiple_sclerosis/clinical_data/GSE131279.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
# Based on the series title and summary, this is a gene expression study in MS brain tissue
is_gene_available = True

# 2.1 Data Availability
# Feature 6 shows tissue type (Grey Matter Lesion vs Grey Matter) - this indicates MS status
trait_row = 6
# Feature 2 shows age at death
age_row = 2  
# Feature 1 shows sex (F/M)
gender_row = 1

# 2.2 Data Type Conversion Functions
def convert_trait(x: str) -> int:
    """Convert tissue type to binary (0: Normal Grey Matter, 1: Grey Matter Lesion)"""
    if not isinstance(x, str):
        return None
    try:
        value = x.split(": ")[1].strip()
        if value == "Grey Matter":
            return 0
        elif value == "Grey Matter Lesion":
            return 1
        return None
    except:
        return None

def convert_age(x: str) -> float:
    """Convert age at death to float"""
    if not isinstance(x, str):
        return None
    try:
        value = x.split(": ")[1].strip()
        return float(value)
    except:
        return None

def convert_gender(x: str) -> int:
    """Convert sex to binary (0: Female, 1: Male)"""
    if not isinstance(x, str):
        return None
    try:
        value = x.split(": ")[1].strip()
        if value == "F":
            return 0
        elif value == "M":
            return 1
        return None
    except:
        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. 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,
        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)
# 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 the ID format ILMN_*, these are Illumina probe IDs, not gene symbols
# They will need to be mapped to 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 mapping between probe IDs and gene symbols
gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')

# Apply the mapping to convert probe data to gene expression data
gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=gene_mapping)

# Normalize gene symbols to handle synonyms
gene_data = normalize_gene_symbols_in_index(gene_data)

# Save processed gene data
gene_data.to_csv(out_gene_data_file)
# 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 link with genetic data 
clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
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="Study comparing Multiple Sclerosis patients to healthy controls using brain tissue transcriptomics."
)

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