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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Breast_Cancer"
cohort = "GSE153316"
# Input paths
in_trait_dir = "../DATA/GEO/Breast_Cancer"
in_cohort_dir = "../DATA/GEO/Breast_Cancer/GSE153316"
# Output paths
out_data_file = "./output/preprocess/3/Breast_Cancer/GSE153316.csv"
out_gene_data_file = "./output/preprocess/3/Breast_Cancer/gene_data/GSE153316.csv"
out_clinical_data_file = "./output/preprocess/3/Breast_Cancer/clinical_data/GSE153316.csv"
json_path = "./output/preprocess/3/Breast_Cancer/cohort_info.json"
# Get file paths for SOFT and matrix files
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
# Get background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
# Create dictionary of unique values for each feature
unique_values_dict = get_unique_values_by_row(clinical_data)
# Print the information
print("Dataset Background Information:")
print(background_info)
print("\nSample Characteristics:")
for feature, values in unique_values_dict.items():
print(f"\n{feature}:")
print(values)
# 1. Gene Expression Data Availability
# Based on series title/summary mentioning "gene expression profiles", this dataset contains gene expression data
is_gene_available = True
# 2. Data Availability and Type Conversion
# Trait (Breast Cancer): All samples are from breast cancer patients (see subject status)
# Since all are cancer patients, there is no variance in trait values
trait_row = None
# Age data is available in row 2
age_row = 2
def convert_age(value):
if not value or ':' not in value:
return None
age = value.split(':')[1].strip()
try:
return float(age)
except:
return None
# Gender: Not explicitly stated but all patients underwent mastectomy,
# which is a surgery primarily for female breast cancer patients
gender_row = None
# We found that trait data is not available (trait_row is None), indicating this dataset is not usable
# Save this information
validate_and_save_cohort_info(
is_final=False,
cohort=cohort,
info_path=json_path,
is_gene_available=is_gene_available,
is_trait_available=False
)
# Since trait_row is None, we skip the clinical feature extraction step
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)
# Print first 20 row IDs
print("First 20 gene/probe IDs:")
print(list(genetic_data.index[:20]))
# These identifiers are from Affymetrix array probes starting with "AFFX-" prefix,
# not standard human gene symbols. They need to be mapped to gene symbols.
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file_path)
# Preview column names and first few values
preview = preview_df(gene_metadata)
print("\nGene annotation columns and sample values:")
print(preview)
# Extract available platform IDs to see what annotation is present
platform_info, _ = filter_content_by_prefix(
soft_file_path,
prefixes_a=["!Platform_title"],
source_type='file',
return_df_a=False
)
print("\nPlatform Information:")
print(platform_info)
# Since we found a mismatch between probe IDs in expression data ("AFFX-" format)
# and annotation data, we need to record this as a data quality issue
validate_and_save_cohort_info(
is_final=False,
cohort=cohort,
info_path=json_path,
is_gene_available=False,
is_trait_available=False,
note="Gene annotation data does not match probe IDs in expression data"
) |