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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Large_B-cell_Lymphoma"
cohort = "GSE145848"
# Input paths
in_trait_dir = "../DATA/GEO/Large_B-cell_Lymphoma"
in_cohort_dir = "../DATA/GEO/Large_B-cell_Lymphoma/GSE145848"
# Output paths
out_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/GSE145848.csv"
out_gene_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/gene_data/GSE145848.csv"
out_clinical_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/clinical_data/GSE145848.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
# Title mentions "transcription programs" and B cell cancers,
# suggesting gene expression data will be part of the series
is_gene_available = True
# 2.1 Data Availability
# From clinical features dictionary:
# - trait (healthy vs CLL) is available in row 1
# - age is not available
# - gender is not available
trait_row = 1
age_row = None
gender_row = None
# 2.2 Data Type Conversion Functions
def convert_trait(value):
if not value or ':' not in value:
return None
value = value.split(':')[1].strip().lower()
# Convert to binary: 0 for healthy, 1 for disease
if 'healthy' in value:
return 0
elif 'chronic lymphocytic leukemia' in value:
return 1
return None
convert_age = None
convert_gender = None
# 3. Save Metadata
# Initial filtering - only checking data availability
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
# Since trait_row is not None, we proceed with clinical feature extraction
clinical_df = geo_select_clinical_features(clinical_data,
trait=trait,
trait_row=trait_row,
convert_trait=convert_trait)
# Preview the processed clinical data
preview = preview_df(clinical_df)
print("Clinical data preview:", preview)
# Save clinical data
clinical_df.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())
# The row indices appear to be probe identifiers from a microarray platform
# (16657436, etc) rather than human gene symbols.
# These need to be mapped to standard gene symbols for analysis.
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"
) |