Datasets:

Liu-Hy
/

GenoTEX

	# Path Configuration
	from tools.preprocess import *

	# Processing context
	trait = "Anxiety_disorder"
	cohort = "GSE61672"

	# Input paths
	in_trait_dir = "../DATA/GEO/Anxiety_disorder"
	in_cohort_dir = "../DATA/GEO/Anxiety_disorder/GSE61672"

	# Output paths
	out_data_file = "./output/preprocess/3/Anxiety_disorder/GSE61672.csv"
	out_gene_data_file = "./output/preprocess/3/Anxiety_disorder/gene_data/GSE61672.csv"
	out_clinical_data_file = "./output/preprocess/3/Anxiety_disorder/clinical_data/GSE61672.csv"
	json_path = "./output/preprocess/3/Anxiety_disorder/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 dataset contains blood gene expression profiles
	is_gene_available = True

	# 2.1 Data Availability
	# Anxiety case/control data is in Feature 4/5
	trait_row = 4
	# Age data is in Feature 0
	age_row = 0
	# Gender data is in Feature 1
	gender_row = 1

	# 2.2 Data Type Conversion Functions
	def convert_trait(x):
	if pd.isna(x):
	return None
	if "anxiety case/control:" in x.lower():
	value = x.split(":")[-1].strip()
	if value == "case":
	return 1
	elif value == "control":
	return 0
	return None

	def convert_age(x):
	if pd.isna(x):
	return None
	if "age:" in x:
	try:
	return float(x.split(":")[1].strip())
	except:
	return None
	return None

	def convert_gender(x):
	if pd.isna(x):
	return None
	if "Sex:" in x:
	value = x.split(":")[1].strip()
	if value == "F":
	return 0
	elif value == "M":
	return 1
	return None

	# 3. Save 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. Clinical Feature Extraction
	if trait_row is not None:
	clinical_features = geo_select_clinical_features(
	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 extracted features
	preview = preview_df(clinical_features)
	print("Preview of extracted clinical features:")
	print(preview)

	# 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
	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. Observe that gene expression data uses 'ILMN_*' identifiers, which match the 'ID' column in annotation
	# Gene symbols are in the 'Symbol' column of annotation

	# 2. Get mapping between probe IDs and gene symbols
	mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')

	# 3. Map probe-level measurements to gene-level measurements
	gene_data = apply_gene_mapping(gene_data, mapping_data)

	# Save gene expression data before normalization
	os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
	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)}"
	)