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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Obstructive_sleep_apnea"
cohort = "GSE135917"
# Input paths
in_trait_dir = "../DATA/GEO/Obstructive_sleep_apnea"
in_cohort_dir = "../DATA/GEO/Obstructive_sleep_apnea/GSE135917"
# Output paths
out_data_file = "./output/preprocess/3/Obstructive_sleep_apnea/GSE135917.csv"
out_gene_data_file = "./output/preprocess/3/Obstructive_sleep_apnea/gene_data/GSE135917.csv"
out_clinical_data_file = "./output/preprocess/3/Obstructive_sleep_apnea/clinical_data/GSE135917.csv"
json_path = "./output/preprocess/3/Obstructive_sleep_apnea/cohort_info.json"
# Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
# Get background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
# Get dictionary of unique values per row
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
print(f"\n{row}:")
print(values)
# 1. Gene Expression Data Availability
# Yes - Background info mentions gene expression microarray data
is_gene_available = True
# 2.1 Data Row Identification
# Group 2 baseline vs after design - use same data structure
# Note: OSA state distinguishes cases/controls
trait_row = None # No direct OSA status labeling in characteristics
age_row = 0 # Age information in row 0
gender_row = 1 # Sex information in row 1
# 2.2 Data Type Conversion Functions
def convert_trait(x):
# Not implemented since trait_row is None
return None
def convert_age(x):
# Extract numeric value after colon
try:
age = float(x.split(': ')[1])
return age
except:
return None
def convert_gender(x):
# Convert F->0, M->1 after colon
try:
gender = x.split(': ')[1]
if gender == 'F':
return 0
elif gender == 'M':
return 1
return None
except:
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. Skip clinical feature extraction since trait_row is None
# Get gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)
# Examine data structure
print("Data structure and head:")
print(genetic_data.head())
print("\nShape:", genetic_data.shape)
print("\nFirst 20 row IDs (gene/probe identifiers):")
print(list(genetic_data.index)[:20])
# Get a few column names to verify sample IDs
print("\nFirst 5 column names:")
print(list(genetic_data.columns)[:5])
# Review gene identifiers
# The row IDs appear to be numeric probe identifiers (e.g. 7892501) rather than human gene symbols
# Therefore, we need to map these probe IDs to actual gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)
# Display column names and preview data
print("Column names:")
print(gene_annotation.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_annotation))
# Identify probe ID and gene symbol columns
# 'ID' matches probe IDs in expression data
# 'gene_assignment' contains gene symbol info
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')
# Apply gene mapping to convert probe data to gene data
gene_data = apply_gene_mapping(genetic_data, mapping_df)
# Print first few rows of resulting gene data to verify
print("\nFirst few rows of mapped gene expression data:")
print(gene_data.head())
print("\nShape of gene data:", gene_data.shape)
# Early validation of data availability
is_usable = validate_and_save_cohort_info(
is_final=False,
cohort=cohort,
info_path=json_path,
is_gene_available=True,
is_trait_available=False # No trait data available
) |