Datasets:

Liu-Hy
/

GenoTEX

	# Path Configuration
	from tools.preprocess import *

	# Processing context
	trait = "Amyotrophic_Lateral_Sclerosis"
	cohort = "GSE118336"

	# Input paths
	in_trait_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis"
	in_cohort_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis/GSE118336"

	# Output paths
	out_data_file = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/GSE118336.csv"
	out_gene_data_file = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/gene_data/GSE118336.csv"
	out_clinical_data_file = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE118336.csv"
	json_path = "./output/preprocess/3/Amyotrophic_Lateral_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
	# Dataset uses HTA2.0 human transcriptome array, which measures gene expression
	is_gene_available = True

	# 2.1 Data Availability
	# Genotype field (Feature 1) indicates disease status - control vs mutation carriers
	trait_row = 1
	# Age and gender not available
	age_row = None
	gender_row = None

	# 2.2 Data Type Conversion Functions
	def convert_trait(x):
	"""Convert genotype to binary trait - 0 for control, 1 for mutation carriers"""
	if not isinstance(x, str):
	return None
	value = x.split(": ")[-1].strip()
	if value == "FUSWT/WT":
	return 0 # Control
	elif value in ["FUSWT/H517D", "FUSH517D/H517D"]:
	return 1 # Mutation carriers
	return None

	convert_age = None
	convert_gender = 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
	clinical_features = geo_select_clinical_features(clinical_data,
	trait=trait,
	trait_row=trait_row,
	convert_trait=convert_trait)

	print("Preview of clinical features:")
	print(preview_df(clinical_features))

	# Save clinical features
	os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
	clinical_features.to_csv(out_clinical_data_file)
	# 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)
	requires_gene_mapping = True
	# Extract gene annotation from SOFT file and get meaningful data
	gene_annotation = get_gene_annotation(soft_file)

	# Preview gene annotation data
	print("Gene annotation shape:", gene_annotation.shape)
	print("\nGene annotation preview:")
	print(preview_df(gene_annotation))

	print("\nNumber of non-null values in each column:")
	print(gene_annotation.count())

	print("\nNote: Gene mapping will use:")
	print("'ID' column: Probe identifiers")
	print("'gene_assignment' column: Contains gene symbols")
	print("\nExample gene_assignment value:")
	print(gene_annotation['gene_assignment'].iloc[0])
	# Create mapping between probe IDs and gene symbols
	mapping_data = get_gene_mapping(gene_annotation, 'ID', 'gene_assignment')

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

	# Normalize gene symbols to standard format
	gene_data = normalize_gene_symbols_in_index(gene_data)

	# Preview results
	print("Shape of gene expression data after mapping:", gene_data.shape)
	print("\nFirst few rows of mapped gene data:")
	print(gene_data.head())
	print("\nRandomly check some gene indices to verify they are gene symbols:")
	print("\nRandom sample of 10 gene indices:")
	print(gene_data.index[::len(gene_data.index)//10][:10])
	# 1. Normalize gene symbols
	gene_data = normalize_gene_symbols_in_index(gene_data)

	# Save normalized gene data
	gene_data.to_csv(out_gene_data_file)

	# 2. Link clinical and genetic data
	try:
	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. Determine if features are biased
	is_trait_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_trait_biased,
	df=linked_data,
	note="Gene expression data successfully mapped and linked with clinical features"
	)

	# 6. Save linked data only if usable AND trait is not biased
	if is_usable and not is_trait_biased:
	linked_data.to_csv(out_data_file)

	except Exception as e:
	print(f"Error in data linking and processing: {str(e)}")
	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=True,
	df=pd.DataFrame(),
	note=f"Data processing failed: {str(e)}"
	)