p3/preprocess/Multiple_sclerosis/code/GSE141804.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Multiple_sclerosis"
cohort = "GSE141804"

# Input paths
in_trait_dir = "../DATA/GEO/Multiple_sclerosis"
in_cohort_dir = "../DATA/GEO/Multiple_sclerosis/GSE141804"

# Output paths
out_data_file = "./output/preprocess/3/Multiple_sclerosis/GSE141804.csv"
out_gene_data_file = "./output/preprocess/3/Multiple_sclerosis/gene_data/GSE141804.csv"
out_clinical_data_file = "./output/preprocess/3/Multiple_sclerosis/clinical_data/GSE141804.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
# From the series summary and background, this dataset has blood mononuclear cell transcriptome data
is_gene_available = True

# 2.1 Data Row Identification
# From the sample characteristics:
gender_row = 0  # Gender data in Feature 0
age_row = 1    # Age data in Feature 1
# Trait (MS) data is not directly given in the sample characteristics
trait_row = None

# 2.2 Data Type Conversion Functions
def convert_gender(x):
    if x is None:
        return None
    value = x.split(': ')[1].strip().lower()
    if value == 'female':
        return 0
    elif value == 'male':
        return 1
    return None

def convert_age(x):
    if x is None:
        return None
    try:
        return float(x.split(': ')[1])
    except:
        return None

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

# 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)
# These identifiers appear to be Affymetrix probe IDs (e.g. "1007_s_at")
# rather than standard human gene symbols. They will need to be mapped.
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))
# 1. The 'ID' column in annotation matches the probe identifiers in expression data
# The 'Gene Symbol' column contains the target gene symbols
prob_id = 'ID'
gene_symbol = 'Gene Symbol'

# 2. Get mapping between probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_annotation, prob_id, gene_symbol) 

# 3. Convert probe-level data to gene-level expression data
gene_data = apply_gene_mapping(gene_data, mapping_data)

# Print gene data shape and preview to verify mapping worked
print("\nShape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few rows of mapped gene data:")
print(gene_data.head())
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# 2. Create a minimal linked dataset since we lack trait information
linked_data = gene_data.T  # Just use gene expression data
is_biased = True  # Mark as biased since we lack the essential trait information 

# 3. Validate and save cohort info - mark as unusable due to missing trait information
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,
    is_biased=is_biased,
    df=linked_data,
    note="Could not identify trait information in sample characteristics."
)