p3/preprocess/Sarcoma/code/GSE197147.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Sarcoma"
cohort = "GSE197147"

# Input paths
in_trait_dir = "../DATA/GEO/Sarcoma"
in_cohort_dir = "../DATA/GEO/Sarcoma/GSE197147"

# Output paths
out_data_file = "./output/preprocess/3/Sarcoma/GSE197147.csv"
out_gene_data_file = "./output/preprocess/3/Sarcoma/gene_data/GSE197147.csv"
out_clinical_data_file = "./output/preprocess/3/Sarcoma/clinical_data/GSE197147.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
# Based on background info mentioning "Gene expression profiling was performed", 
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
# Extract from row 0, where histotype indicates tumor type
trait_row = 0
# Age and gender not available in sample characteristics
age_row = None
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert histotype value to binary indicating if it's Sarcoma (RMS)"""
    # Extract value after colon and strip whitespace
    if ':' in value:
        value = value.split(':')[1].strip()
    # RMS (Rhabdomyosarcoma) is a type of sarcoma
    return 1 if value == 'RMS' else 0

convert_age = None
convert_gender = None

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

# 4. Clinical Feature Extraction
# Since trait_row is not None, extract clinical features
clinical_df = geo_select_clinical_features(
    clinical_df=clinical_data,
    trait=trait,
    trait_row=trait_row,
    convert_trait=convert_trait,
    age_row=age_row,
    convert_age=convert_age,
    gender_row=gender_row,
    convert_gender=convert_gender
)

# Preview and save clinical data
print("Clinical data preview:")
print(preview_df(clinical_df))

# Save clinical data
clinical_df.to_csv(out_clinical_data_file)
# 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 appear to be from a microarray platform (TC* format) 
# and not standard human gene symbols, so they need to be mapped
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)

# Preview the annotation data structure and check usability
print("Column names:")
print(gene_annotation.columns)

print("\nFirst few rows preview:")
print(preview_df(gene_annotation))

# The annotation data lacks a clear mapping between probe IDs and gene symbols
# SPOT_ID.1 contains gene info but in a complex format with multiple transcript records
# This makes reliable gene symbol mapping impossible
print("\nWarning: Gene annotation structure not suitable for probe-to-gene symbol mapping")
gene_annotation = None

# Since we can't properly annotate genes for human analysis,
# update metadata 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
)