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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Retinoblastoma"
cohort = "GSE68950"
# Input paths
in_trait_dir = "../DATA/GEO/Retinoblastoma"
in_cohort_dir = "../DATA/GEO/Retinoblastoma/GSE68950"
# Output paths
out_data_file = "./output/preprocess/3/Retinoblastoma/GSE68950.csv"
out_gene_data_file = "./output/preprocess/3/Retinoblastoma/gene_data/GSE68950.csv"
out_clinical_data_file = "./output/preprocess/3/Retinoblastoma/clinical_data/GSE68950.csv"
json_path = "./output/preprocess/3/Retinoblastoma/cohort_info.json"
# Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
# Get background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
# Get dictionary of unique values per row
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
print(f"\n{row}:")
print(values)
# Check gene expression data availability (yes, this is Affymetrix gene expression data)
is_gene_available = True
# After reviewing disease states, there are no retinoblastoma cases
trait_row = None # No retinoblastoma cases in the dataset
age_row = None # No age information available
gender_row = None # No gender information available
# Define conversion functions
def convert_trait(value: str) -> int:
"""Convert disease state to binary: 1 for Retinoblastoma, 0 for others"""
if not value or ':' not in value:
return None
disease = value.split(':', 1)[1].strip().lower()
if 'retinoblastoma' in disease:
return 1
return 0
def convert_age(value: str) -> float:
"""Placeholder function since age data is not available"""
return None
def convert_gender(value: str) -> int:
"""Placeholder function since gender data is not available"""
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)
# Skip clinical feature extraction since trait data is not available
# Get gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)
# Examine data structure
print("Data structure and head:")
print(genetic_data.head())
print("\nShape:", genetic_data.shape)
print("\nFirst 20 row IDs (gene/probe identifiers):")
print(list(genetic_data.index)[:20])
# Get a few column names to verify sample IDs
print("\nFirst 5 column names:")
print(list(genetic_data.columns)[:5])
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)
# Display column names and preview data
print("Column names:")
print(gene_annotation.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_annotation))
# Get gene mapping information from annotation data
# The ID column in gene_annotation matches the probe IDs in genetic_data
# The Gene Symbol column contains corresponding gene symbols
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
# Apply the mapping to convert probe data to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_data)
# Save gene data
gene_data.to_csv(out_gene_data_file)
import pandas as pd
# Create empty DataFrame for validation
empty_df = pd.DataFrame()
# Final validation and information saving
note = "Dataset lacks retinoblastoma trait information, cannot be used for analysis."
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=empty_df,
note=note
) |