p3/preprocess/Acute_Myeloid_Leukemia/code/GSE222616.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Acute_Myeloid_Leukemia"
cohort = "GSE222616"

# Input paths
in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE222616"

# Output paths
out_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/GSE222616.csv"
out_gene_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/gene_data/GSE222616.csv"
out_clinical_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/clinical_data/GSE222616.csv"
json_path = "./output/preprocess/3/Acute_Myeloid_Leukemia/cohort_info.json"

# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data 
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)

# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")

# Print sample characteristics
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
    print(f"Feature: {feature}")
    print(f"Values: {values}\n")
# 1. Gene Expression Data Availability
# Yes - it uses HuGene 1.0 ST Affymetrix arrays for gene expression profiling
is_gene_available = True

# 2.1 Data Availability
# trait (AML status) is constant since all samples are from HL-60 AML cell line
trait_row = None 

# Age is not available for cell line data
age_row = None

# Gender is not available for cell line data
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    # Not needed since trait data is not available
    return None

def convert_age(value):
    # Not needed since age data is not available
    return None

def convert_gender(value):
    # Not needed since gender data is not available 
    return None

# 3. 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)
)

# 4. Clinical Feature Extraction
# Skip since trait_row is None (no clinical data available)
# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)

# Print first 20 row IDs
print("First 20 gene/probe identifiers:")
print(gene_data.index[:20])
# Based on biomedical review: The identifiers appear to be numerical probe IDs from an array platform 
# rather than standardized human gene symbols. These would need to be mapped to gene symbols.
requires_gene_mapping = True
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file)

# Preview gene annotation data
print("Gene annotation columns and example values:")
print(preview_df(gene_annotation))
# Extract gene mapping information
probe_col = "ID"
gene_col = "gene_assignment"
mapping_data = gene_annotation[[probe_col, gene_col]]
mapping_data = mapping_data.dropna()
mapping_data = mapping_data.rename(columns={probe_col: 'ID', gene_col: 'Gene'})
mapping_data['Gene'] = mapping_data['Gene'].apply(extract_human_gene_symbols)

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

# Normalize gene symbols to handle synonyms
gene_data = normalize_gene_symbols_in_index(gene_data)
# 1. Save normalized gene data
gene_data.to_csv(out_gene_data_file)

# Since no clinical data is available, use gene data as final dataset
# Set is_biased=False since we cannot assess bias without trait data
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=False,  # Cannot be biased without trait data
    df=gene_data,
    note="Only gene expression data available, no clinical information found"
)

# Save gene data as final data since no clinical data to link
if is_usable:
    gene_data.to_csv(out_data_file)