p3/preprocess/Retinoblastoma/code/GSE26805.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Retinoblastoma"
cohort = "GSE26805"

# Input paths
in_trait_dir = "../DATA/GEO/Retinoblastoma"
in_cohort_dir = "../DATA/GEO/Retinoblastoma/GSE26805"

# Output paths
out_data_file = "./output/preprocess/3/Retinoblastoma/GSE26805.csv"
out_gene_data_file = "./output/preprocess/3/Retinoblastoma/gene_data/GSE26805.csv"
out_clinical_data_file = "./output/preprocess/3/Retinoblastoma/clinical_data/GSE26805.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)
# 1. Gene Expression Data Availability
# Based on background info, this is gene expression data from ovarian cell lines
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# Looking at sample characteristics, this dataset only includes cell lines without 
# trait/age/gender info for individual samples
trait_row = None
age_row = None 
gender_row = None

# Converting functions (not used but defined for completeness)
def convert_trait(x):
    return None

def convert_age(x):
    return None

def convert_gender(x):
    return None

# 3. Save metadata about dataset usability
# Call validate_and_save_cohort_info with is_final=False for 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 no clinical data 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])
# Based on gene identifiers format (A_23_P...), these are Agilent microarray probe IDs
# They need to be mapped to gene symbols for biological interpretation
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 mapping between probe IDs and gene symbols from annotation data
# 'ID' is the probe identifier column, 'GENE_SYMBOL' has gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')

# Convert probe-level measurements to gene-level expression
gene_data = apply_gene_mapping(genetic_data, mapping_df)
# 1. Normalize gene symbols 
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# Validate data availability without final validation since trait data is missing
validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True, 
    is_trait_available=False
)