Datasets:

Liu-Hy
/

GenoTEX

	# Path Configuration
	from tools.preprocess import *

	# Processing context
	trait = "Amyotrophic_Lateral_Sclerosis"
	cohort = "GSE61322"

	# Input paths
	in_trait_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis"
	in_cohort_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis/GSE61322"

	# Output paths
	out_data_file = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/GSE61322.csv"
	out_gene_data_file = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/gene_data/GSE61322.csv"
	out_clinical_data_file = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE61322.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
	is_gene_available = True # Microarray data is mentioned in Series_overall_design

	# 2.1 Data Availability
	trait_row = 0 # ALS status can be inferred from diagnosis
	age_row = None # Age not available
	gender_row = None # Gender not available

	# 2.2 Data Type Conversion Functions
	def convert_trait(value):
	"""Convert diagnosis to binary trait (ALS)"""
	if not isinstance(value, str):
	return None
	if ':' in value:
	value = value.split(':')[1].strip().lower()
	if value == 'affected':
	return 1
	elif value == 'carrier':
	return 0
	return None

	def convert_age(value):
	return None # Not used

	def convert_gender(value):
	return None # Not used

	# 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_df = geo_select_clinical_features(clinical_df=clinical_data,
	trait=trait,
	trait_row=trait_row,
	convert_trait=convert_trait)
	print("Preview of processed clinical data:")
	print(preview_df(clinical_df))
	clinical_df.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)
	# The gene identifiers start with "ILMN_", indicating they are Illumina probe IDs
	# These need to be mapped to standard human gene symbols
	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("'Symbol' column: Gene name mapping")
	# 1. Identify mapping columns from gene annotation
	mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')

	# 2 & 3. Apply gene mapping to convert probe data to gene expression data
	gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)

	# Print preview to verify mapping result
	print("Shape of mapped gene expression data:", gene_data.shape)
	print("\nPreview of mapped gene expression data:")
	print(gene_data.head())

	# Save gene expression data
	gene_data.to_csv(out_gene_data_file)
	# 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)}"
	)