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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "COVID-19"
cohort = "GSE273225"
# Input paths
in_trait_dir = "../DATA/GEO/COVID-19"
in_cohort_dir = "../DATA/GEO/COVID-19/GSE273225"
# Output paths
out_data_file = "./output/preprocess/3/COVID-19/GSE273225.csv"
out_gene_data_file = "./output/preprocess/3/COVID-19/gene_data/GSE273225.csv"
out_clinical_data_file = "./output/preprocess/3/COVID-19/clinical_data/GSE273225.csv"
json_path = "./output/preprocess/3/COVID-19/cohort_info.json"
# Get file paths for SOFT and matrix files
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
# Get background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
# Create dictionary of unique values for each feature
unique_values_dict = get_unique_values_by_row(clinical_data)
# Print the information
print("Dataset Background Information:")
print(background_info)
print("\nSample Characteristics:")
for feature, values in unique_values_dict.items():
print(f"\n{feature}:")
print(values)
# 1. Gene Expression Data Availability
# Based on the Series_overall_design description mentioning nCounter digital gene expression analysis
# with Immunology V2 panel targeting 579 immune system genes
is_gene_available = True
# 2.1 Data Availability
# For COVID-19 trait - data not available in this transplantation study
trait_row = None
# Age data available in row 3
age_row = 3
# Gender data available in row 4
gender_row = 4
# 2.2 Data Type Conversion Functions
def convert_trait(value):
# Not used since trait data not available
return None
def convert_age(value):
# Convert age string to numeric value
try:
# Extract number after "donor age (y): "
age = int(value.split(": ")[1])
return age
except:
return None
def convert_gender(value):
# Convert gender to binary (0=female, 1=male)
try:
gender = value.split(": ")[1].lower()
if gender == "female":
return 0
elif gender == "male":
return 1
else:
return None
except:
return None
# 3. Save initial metadata
is_usable = validate_and_save_cohort_info(
is_final=False,
cohort=cohort,
info_path=json_path,
is_gene_available=is_gene_available,
is_trait_available=False # since trait_row is None
)
# Extract genetic data matrix
genetic_data = get_genetic_data(matrix_file_path)
# Print first few rows with column names to examine data structure
print("Data preview:")
print("\nColumn names:")
print(list(genetic_data.columns)[:5])
print("\nFirst 5 rows:")
print(genetic_data.head())
print("\nShape:", genetic_data.shape)
# Verify this is gene expression data and check identifiers
is_gene_available = True
# Save updated 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)
)
# Save gene expression data
genetic_data.to_csv(out_gene_data_file)
requires_gene_mapping = False
# 1. Normalize gene symbols and save gene data
normalized_gene_data = normalize_gene_symbols_in_index(genetic_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)
# Since trait_row is None (no COVID-19 data), skip data linking and update metadata
is_usable = validate_and_save_cohort_info(
is_final=True,
cohort=cohort,
info_path=json_path,
is_gene_available=is_gene_available,
is_trait_available=False,
is_biased=None,
df=None,
note="Dataset contains gene expression data from lung transplantation study examining rewarming ischemia effects. No COVID-19 trait data available."
) |