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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Large_B-cell_Lymphoma"
cohort = "GSE173263"
# Input paths
in_trait_dir = "../DATA/GEO/Large_B-cell_Lymphoma"
in_cohort_dir = "../DATA/GEO/Large_B-cell_Lymphoma/GSE173263"
# Output paths
out_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/GSE173263.csv"
out_gene_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/gene_data/GSE173263.csv"
out_clinical_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/clinical_data/GSE173263.csv"
json_path = "./output/preprocess/3/Large_B-cell_Lymphoma/cohort_info.json"
# Get file paths for soft and matrix files
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# Get background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file)
# Get unique values for each clinical feature row
clinical_features = get_unique_values_by_row(clinical_data)
# Print background info
print("Background Information:")
print(background_info)
print("\nClinical Features and Sample Values:")
print(json.dumps(clinical_features, indent=2))
# 1. Gene Expression Data Availability
# Based on background info, this is a GEP (Gene Expression Profile) study
is_gene_available = True
# 2. Data Availability and Type Conversion
# 2.1 Data Availability
# Trait (response to R-CHOP) is in row 2
trait_row = 2
# Age not available
age_row = None
# Gender not available
gender_row = None
# 2.2 Data Type Conversion Functions
def convert_trait(value):
if not isinstance(value, str):
return None
value = value.lower().split(": ")[-1].strip()
if "early failure" in value:
return 1
elif "remission" in value:
return 0
return None
def convert_age(value):
return None
def convert_gender(value):
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_df=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 extracted features
preview = preview_df(clinical_features)
# Save clinical features
clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file)
# Print DataFrame info and dimensions to verify data structure
print("DataFrame info:")
print(genetic_data.info())
print("\nDataFrame dimensions:", genetic_data.shape)
# Print an excerpt of the data to inspect row/column structure
print("\nFirst few rows and columns of data:")
print(genetic_data.head().iloc[:, :5])
# Print first 20 row IDs
print("\nFirst 20 gene/probe IDs:")
print(genetic_data.index[:20].tolist())
# Based on the index format (e.g., '11715100_at', '11715101_s_at'), these appear to be Affymetrix probe IDs
# rather than standard human gene symbols. They need to be mapped to HGNC gene symbols.
requires_gene_mapping = True
# Report discovery of missing gene annotation
print("Gene Annotation Analysis:")
print("WARNING: Gene probe-to-symbol mapping information is not available in this SOFT file.")
print("The annotation only contains signature names (e.g. TIS.IO360, APM.IO360) rather than human gene symbols.")
# Update validation info to show dataset cannot be used due to missing gene mapping
validate_and_save_cohort_info(
is_final=False,
cohort=cohort,
info_path=json_path,
is_gene_available=False, # Set to False since gene expression data is not mappable
is_trait_available=trait_row is not None,
note="Dataset contains numeric probe IDs but lacks gene symbol mapping information"
) |