p3/preprocess/Retinoblastoma/code/GSE63529.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Retinoblastoma"
cohort = "GSE63529"

# Input paths
in_trait_dir = "../DATA/GEO/Retinoblastoma"
in_cohort_dir = "../DATA/GEO/Retinoblastoma/GSE63529"

# Output paths
out_data_file = "./output/preprocess/3/Retinoblastoma/GSE63529.csv"
out_gene_data_file = "./output/preprocess/3/Retinoblastoma/gene_data/GSE63529.csv"
out_clinical_data_file = "./output/preprocess/3/Retinoblastoma/clinical_data/GSE63529.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
is_gene_available = True  # The Series_summary and design indicate a gene expression study

# 2.1 Data Availability 
# This dataset studies ovarian cancer drug resistance, not retinoblastoma
trait_row = None  # No appropriate retinoblastoma trait data
age_row = None  # Age information is not provided
gender_row = None  # Gender information is not provided

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    # Not needed since trait data is unavailable
    return None

def convert_age(x):
    # Not needed since age data is unavailable
    return None

def convert_gender(x):
    # Not needed since gender data is unavailable
    return None

# 3. Save Metadata
is_trait_available = trait_row is not None
validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=is_trait_available
)

# 4. Clinical Feature Extraction
# Skip since trait_row is None
# 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])
# The identifiers starting with "ILMN_" are Illumina probe IDs used in microarrays.
# They need to be mapped to human gene symbols for consistent analysis.
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))
# 1. Column names identified:
# 'ID' in gene annotation corresponds to the probe IDs (ILMN_*) in gene expression data
# 'Symbol' contains the gene symbols to map to

# 2. Get gene mapping dataframe with probe ID and gene symbol columns
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')

# 3. Convert probe-level measurements to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Print info about the conversion
print("Original probe data shape:", genetic_data.shape)
print("Gene mapping data shape:", mapping_data.shape)
print("Final gene expression data shape:", gene_data.shape)

print("\nPreview of gene expression data:")
print(preview_df(gene_data))
# 1. Normalize gene symbols and save
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)

# 2-4. Skip clinical data linking and bias checking since trait data is unavailable
linked_data = gene_data  # Use gene data as linked data since no clinical data available
trait_biased = True  # No retinoblastoma data makes it maximally biased for this trait

# 5. Final validation - mark as unusable due to lack of retinoblastoma trait data
note = "Dataset contains gene expression data from ovarian cancer drug resistance study, not retinoblastoma."
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=trait_biased,
    df=linked_data,
    note=note
)

# 6. Skip saving linked data since dataset is unusable