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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Glioblastoma"
cohort = "GSE175700"
# Input paths
in_trait_dir = "../DATA/GEO/Glioblastoma"
in_cohort_dir = "../DATA/GEO/Glioblastoma/GSE175700"
# Output paths
out_data_file = "./output/preprocess/3/Glioblastoma/GSE175700.csv"
out_gene_data_file = "./output/preprocess/3/Glioblastoma/gene_data/GSE175700.csv"
out_clinical_data_file = "./output/preprocess/3/Glioblastoma/clinical_data/GSE175700.csv"
json_path = "./output/preprocess/3/Glioblastoma/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")
# Gene data availability
# Yes - the dataset contains microarray data for gene expression analysis
is_gene_available = True
# Clinical feature keys and conversion functions
trait_row = None # All samples are U87 cell lines, no trait variation
age_row = None # Age not available for cell line data
gender_row = None # All samples are male cell line, no gender variation
# Since no clinical features are available with variation, we don't need conversion functions
def convert_trait(x):
return None
def convert_age(x):
return None
def convert_gender(x):
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=False) # trait_row is None so trait data unavailable
# Skip clinical feature extraction since no trait data available
# 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)
# Looking at the identifiers starting with "AFFX-", these are Affymetrix probe IDs
# They need to be mapped to standard human gene symbols
requires_gene_mapping = True
# Get file paths using library function
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file)
# Preview gene annotation data
print("Gene annotation columns and example values:")
print(preview_df(gene_annotation))
# Get the mapping between probe IDs and gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')
# Apply the mapping to convert probe-level to gene-level expression
gene_data = apply_gene_mapping(gene_data, mapping_df)
# Print dimensions to verify mapping result
print("\nShape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few rows after mapping:")
print(gene_data.head())
# 1. Normalize gene symbols and save normalized gene data
gene_data.index = gene_data.index.str.replace('-mRNA', '')
gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
# Update final validation info with gene_data as df and is_biased=True
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, # No trait variation means completely biased
df=gene_data, # Pass gene_data as df
note="Cell line study with no trait variation - all samples are U87 cell line"
) |