Liu-Hy's picture
Add files using upload-large-folder tool
f811ee7 verified
# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Kidney_Clear_Cell_Carcinoma"
cohort = "GSE102807"
# Input paths
in_trait_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma"
in_cohort_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma/GSE102807"
# Output paths
out_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/GSE102807.csv"
out_gene_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/gene_data/GSE102807.csv"
out_clinical_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/clinical_data/GSE102807.csv"
json_path = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/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 from SOFT file instead of matrix file
background_info, clinical_data = get_background_and_clinical_data(soft_file_path)
# Get unique values for each clinical feature
unique_values_dict = get_unique_values_by_row(clinical_data)
# Print background information
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
print(json.dumps(unique_values_dict, indent=2))
# Gene expression data availability
is_gene_available = False # Based on metadata, this appears to be ChIP-seq data, not gene expression
# Define variable rows and conversion functions
trait_row = None # No clinical trait info available
age_row = None # No age info available
gender_row = None # No gender info available
def convert_trait(x):
return None
def convert_age(x):
return None
def convert_gender(x):
return None
# 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)
)