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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Large_B-cell_Lymphoma"
cohort = "GSE243973"
# Input paths
in_trait_dir = "../DATA/GEO/Large_B-cell_Lymphoma"
in_cohort_dir = "../DATA/GEO/Large_B-cell_Lymphoma/GSE243973"
# Output paths
out_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/GSE243973.csv"
out_gene_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/gene_data/GSE243973.csv"
out_clinical_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/clinical_data/GSE243973.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
# Yes - Series summary mentions transcriptomic profiling
is_gene_available = True
# 2.1 Feature Key Identification
# Trait - Row 0 contains disease state info
trait_row = 0
# Age - Not available in characteristics
age_row = None
# Gender - Not available in characteristics
gender_row = None
# 2.2 Data Type Conversion Functions
def convert_trait(x: str) -> int:
"""Convert disease status to binary: 1 for LBCL, 0 for control"""
if pd.isna(x):
return None
value = x.split(': ')[1].lower() if ': ' in x else x.lower()
if 'large b-cell lymphoma' in value:
return 1
elif 'healthy control' in value:
return 0
return None
def convert_age(x: str) -> float:
"""Not used but defined for completeness"""
return None
def convert_gender(x: str) -> int:
"""Not used but defined for completeness"""
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. Extract Clinical Features
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 the data
preview = preview_df(clinical_features)
print("Clinical features preview:", preview)
# Save to CSV
clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file)
# Print first 20 row IDs
print("First 20 gene/probe IDs:")
print(genetic_data.index[:20].tolist())
# These appear to be standard human gene symbols (HGNC format)
# e.g. ABCF1, ACACA, ADAR are well-known human gene symbols
# No mapping needed as they are already in the correct format
requires_gene_mapping = False
# 1. Normalize gene symbols
genetic_data = normalize_gene_symbols_in_index(genetic_data)
genetic_data.to_csv(out_gene_data_file)
# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_features, genetic_data)
# 3. Handle missing values
linked_data = handle_missing_values(df=linked_data, trait_col=trait)
# 4. Check for biases and remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Validate dataset quality and save metadata
note = ""
if is_biased:
note = "The trait distribution is severely biased."
is_usable = validate_and_save_cohort_info(
is_final=True,
cohort=cohort,
info_path=json_path,
is_gene_available=True,
is_trait_available=True,
is_biased=is_biased,
df=linked_data,
note=note
)
# 6. Save linked data if usable
if is_usable:
linked_data.to_csv(out_data_file) |