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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Lung_Cancer"
cohort = "GSE244647"
# Input paths
in_trait_dir = "../DATA/GEO/Lung_Cancer"
in_cohort_dir = "../DATA/GEO/Lung_Cancer/GSE244647"
# Output paths
out_data_file = "./output/preprocess/3/Lung_Cancer/GSE244647.csv"
out_gene_data_file = "./output/preprocess/3/Lung_Cancer/gene_data/GSE244647.csv"
out_clinical_data_file = "./output/preprocess/3/Lung_Cancer/clinical_data/GSE244647.csv"
json_path = "./output/preprocess/3/Lung_Cancer/cohort_info.json"
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# Extract background info and clinical data using specified prefixes
background_info, clinical_data = get_background_and_clinical_data(
matrix_file,
prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1']
)
# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)
# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")
# Print sample characteristics
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
print(f"Feature: {feature}")
print(f"Values: {values}\n")
# Gene expression data availability
is_gene_available = True # Based on dataset title mentioning NSCLC and HNSCC which indicates gene expression data
# Variable row identification
trait_row = 1 # 'condition: tumour presence/tumour free' indicates cancer status
age_row = 5 # 'age: XX' contains age information
gender_row = 4 # 'Sex: Male/Female' contains gender information
# Conversion functions
def convert_trait(value: str) -> int:
if not value or ':' not in value:
return None
value = value.split(':')[1].strip().lower()
if 'tumour presence' in value:
return 1
elif 'tumour free' in value:
return 0
return None
def convert_age(value: str) -> float:
if not value or ':' not in value:
return None
try:
return float(value.split(':')[1].strip())
except:
return None
def convert_gender(value: str) -> int:
if not value or ':' not in value:
return None
value = value.split(':')[1].strip().lower()
if value == 'female':
return 0
elif value == 'male':
return 1
return None
# 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
)
# Extract clinical features since trait_row is available
selected_clinical_df = 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 clinical data
print(preview_df(selected_clinical_df))
# Save clinical features
selected_clinical_df.to_csv(out_clinical_data_file)
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)
# Print first 20 row IDs and shape of data to help debug
print("Shape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.index[:20])
# Inspect a snippet of raw file to verify identifier format
import gzip
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
lines = []
for i, line in enumerate(f):
if "!series_matrix_table_begin" in line:
# Get the next 5 lines after the marker
for _ in range(5):
lines.append(next(f).strip())
break
print("\nFirst few lines after matrix marker in raw file:")
for line in lines:
print(line)
# Based on the format TC0100006437.hg.1 which appears to be probe IDs from a microarray platform
# rather than standard human gene symbols, gene mapping will be required
requires_gene_mapping = True
# Detect miRNA dataset and handle appropriately
is_gene_available = False
validate_and_save_cohort_info(
is_final=False,
cohort=cohort,
info_path=json_path,
is_gene_available=is_gene_available,
is_trait_available=True, # We already know trait data exists from Step 2
note="Dataset contains miRNA measurements instead of gene expression data"
)
print("WARNING: This dataset contains miRNA measurements and is not suitable for gene expression analysis.")
print("Preprocessing pipeline will be terminated for this dataset.") |