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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Essential_Thrombocythemia"
cohort = "GSE65161"
# Input paths
in_trait_dir = "../DATA/GEO/Essential_Thrombocythemia"
in_cohort_dir = "../DATA/GEO/Essential_Thrombocythemia/GSE65161"
# Output paths
out_data_file = "./output/preprocess/3/Essential_Thrombocythemia/GSE65161.csv"
out_gene_data_file = "./output/preprocess/3/Essential_Thrombocythemia/gene_data/GSE65161.csv"
out_clinical_data_file = "./output/preprocess/3/Essential_Thrombocythemia/clinical_data/GSE65161.csv"
json_path = "./output/preprocess/3/Essential_Thrombocythemia/cohort_info.json"
# Get relevant file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
# Extract background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
# Get dictionary of unique values per row in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data)
# Print background info
print("Background Information:")
print("-" * 50)
print(background_info)
print("\n")
# Print clinical data unique values
print("Sample Characteristics:")
print("-" * 50)
for row, values in unique_values_dict.items():
print(f"{row}:")
print(f" {values}")
print()
# 1. Gene Expression Data Availability
is_gene_available = False # Based on the background info, this dataset seems to focus on molecular studies of cell lines rather than gene expression
# 2.1 Data Availability
trait_row = None # No suitable trait data (Essential Thrombocythemia) can be found - these are cell lines
age_row = None # No age data available for cell lines
gender_row = None # No gender data available for cell lines
# 2.2 Data Type Conversion Functions
def convert_trait(x):
# Not needed since trait data is unavailable
return None
def convert_age(x):
# Not needed since age data is unavailable
return None
def convert_gender(x):
# Not needed since gender data is unavailable
return None
# 3. Save Initial Metadata
validate_and_save_cohort_info(
is_final=False,
cohort=cohort,
info_path=json_path,
is_gene_available=is_gene_available,
is_trait_available=False # trait_row is None
)
# 4. Clinical Feature Extraction
# Skip this step since trait_row is None |