p3/preprocess/Multiple_sclerosis/code/GSE135511.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Multiple_sclerosis"
cohort = "GSE135511"

# Input paths
in_trait_dir = "../DATA/GEO/Multiple_sclerosis"
in_cohort_dir = "../DATA/GEO/Multiple_sclerosis/GSE135511"

# Output paths
out_data_file = "./output/preprocess/3/Multiple_sclerosis/GSE135511.csv"
out_gene_data_file = "./output/preprocess/3/Multiple_sclerosis/gene_data/GSE135511.csv"
out_clinical_data_file = "./output/preprocess/3/Multiple_sclerosis/clinical_data/GSE135511.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 dataset title and background info mentioning "gene expression profiling", this contains gene expression data
is_gene_available = True

# 2.1 Data Row Identification
trait_row = 0  # Disease state is recorded in row 0
age_row = None  # Age data not available 
gender_row = None  # Gender data not available

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert disease state to binary (0: Control, 1: MS)"""
    if value is None or ':' not in value:
        return None
    value = value.split(':', 1)[1].strip().lower()
    if 'healthy control' in value:
        return 0
    elif 'multiple sclerosis' in value:
        return 1
    return None

def convert_age(value: str) -> float:
    """Placeholder function since age is not available"""
    return None

def convert_gender(value: str) -> int:
    """Placeholder function since gender is not available"""
    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
    )
    
    # Preview the processed clinical data
    preview = preview_df(clinical_features)
    print("Preview of clinical features:", preview)
    
    # Save clinical features to CSV
    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)
# Observed identifiers like "ILMN_1343291" indicate these are Illumina BeadArray probe IDs,
# not gene symbols, so they need to be mapped to 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))
# 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)

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

# Extract ID-Symbol mapping from gene annotation
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')

# Apply gene mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(gene_data, mapping_df)

# Preview gene data after mapping
print("Gene Data Preview:")
print("Shape:", gene_data.shape)
print("\nFirst few rows:")
print(gene_data.head())
print("\nFirst few gene symbols:")
print(gene_data.index[:20])
# 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)