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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Hepatitis"
cohort = "GSE125860"
# Input paths
in_trait_dir = "../DATA/GEO/Hepatitis"
in_cohort_dir = "../DATA/GEO/Hepatitis/GSE125860"
# Output paths
out_data_file = "./output/preprocess/3/Hepatitis/GSE125860.csv"
out_gene_data_file = "./output/preprocess/3/Hepatitis/gene_data/GSE125860.csv"
out_clinical_data_file = "./output/preprocess/3/Hepatitis/clinical_data/GSE125860.csv"
json_path = "./output/preprocess/3/Hepatitis/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")
# 1. Gene Expression Data
is_gene_available = True # Affymetrix arrays indicate gene expression data
# 2.1 Data Row Identification
trait_row = 7 # hepatitis b concentration post-vaccination indicates disease status
age_row = 17 # age information
gender_row = 18 # gender information
# 2.2 Data Type Conversion Functions
def convert_trait(x):
try:
# Extract value after colon and strip whitespace
val = x.split(':', 1)[1].strip()
if val == 'NA':
return None
# Convert to float and binarize based on threshold
val = float(val.replace('<', '').replace('mIU/mL', '').strip())
return 1 if val >= 10 else 0 # Common threshold for HBV protection
except:
return None
def convert_age(x):
try:
return int(x.split(':', 1)[1].strip())
except:
return None
def convert_gender(x):
try:
gender = x.split(':', 1)[1].strip()
if gender == 'F':
return 0
elif gender == 'M':
return 1
return None
except:
return None
# 3. Save Initial Metadata
is_trait_available = trait_row is not None
validate_and_save_cohort_info(False, cohort, json_path, is_gene_available, is_trait_available)
# 4. Clinical Feature Extraction
if trait_row is not None:
selected_clinical = geo_select_clinical_features(
clinical_data,
trait,
trait_row,
convert_trait,
age_row,
convert_age,
gender_row,
convert_gender
)
# Preview the data
preview = preview_df(selected_clinical)
# Save to CSV
selected_clinical.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 gene identifiers in the data ('AFFX-' prefix suggests Affymetrix probe IDs),
# these are probe IDs that need to be mapped to gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)
# Preview the annotation data
print("Column names:", gene_metadata.columns.tolist())
print("\nFirst few rows preview:")
print(preview_df(gene_metadata))
# Since gene mapping information is missing in the SOFT file
# Print message and save original probe-level data directly
print("Warning: Gene mapping information is not available in the SOFT file.")
print("Saving probe-level expression data without gene mapping.")
# Save probe-level expression data
gene_data.to_csv(out_gene_data_file) |