p3/preprocess/Sarcoma/code/GSE215265.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Sarcoma"
cohort = "GSE215265"

# Input paths
in_trait_dir = "../DATA/GEO/Sarcoma"
in_cohort_dir = "../DATA/GEO/Sarcoma/GSE215265"

# Output paths
out_data_file = "./output/preprocess/3/Sarcoma/GSE215265.csv"
out_gene_data_file = "./output/preprocess/3/Sarcoma/gene_data/GSE215265.csv"
out_clinical_data_file = "./output/preprocess/3/Sarcoma/clinical_data/GSE215265.csv"
json_path = "./output/preprocess/3/Sarcoma/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
# From background info, this appears to be about ASPSCR1-TFE3 in sarcoma, likely with gene expression
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
# All samples are sarcoma cases - constant feature is not usable
trait_row = None  
age_row = None  # No age data available
gender_row = None  # No gender data available

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    # Default conversion function even though not used since trait is constant
    return None

def convert_age(x):
    # Default conversion function even though not used 
    return None

def convert_gender(x):
    # Default conversion function even though not used
    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])
# Based on identifier pattern "_PM_" and format like "1007_PM_s_at", these are probe IDs from Affymetrix arrays
# They need to be mapped to human gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)

# Based on species information in gene annotation, this dataset contains mouse data
print("Dataset appears to be from mouse rather than human.")
print("Therefore gene annotation and mapping cannot proceed.")

# Set flag indicating gene annotation data is not usable 
gene_annotation = None

# Since we can't properly annotate genes and the goal is studying human genes,
# update the metadata record to indicate gene data is not available
is_gene_available = False
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
)