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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Amyotrophic_Lateral_Sclerosis"
cohort = "GSE95810"
# Input paths
in_trait_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis"
in_cohort_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis/GSE95810"
# Output paths
out_data_file = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/GSE95810.csv"
out_gene_data_file = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/gene_data/GSE95810.csv"
out_clinical_data_file = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE95810.csv"
json_path = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/cohort_info.json"
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# Extract background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file)
# 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 Availability
# Based on the background information, this is an assay using iPS derived neurons as biosensors
# to measure gene expression in response to plasma exposure, so gene expression data is available
is_gene_available = True
# 2.1 Data Availability
# trait_row: None since this dataset is about Alzheimer's Disease, not ALS
# age_row: Not available in sample characteristics
# gender_row: Not available in sample characteristics
trait_row = None
age_row = None
gender_row = None
# 2.2 Data Type Conversion Functions
def convert_trait(x):
return None # Not used since trait_row is None
def convert_age(x):
return None # Not used since age_row is None
def convert_gender(x):
return None # Not used since gender_row is None
# 3. Save Metadata
# Validate and save cohort info (initial filtering)
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. Clinical Feature Extraction
# Skip since trait_row is None, indicating clinical data is not available for our trait of interest
# 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)
requires_gene_mapping = False
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
# Save normalized gene data
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
# 2-6. Since clinical data is not available for our trait, we skip data linking
# and mark the dataset as not usable for our study
is_usable = validate_and_save_cohort_info(
is_final=True,
cohort=cohort,
info_path=json_path,
is_gene_available=True,
is_trait_available=False,
is_biased=True,
df=gene_data,
note="Dataset contains gene expression data but is about Alzheimer's Disease, not ALS"
) |