p3/preprocess/Acute_Myeloid_Leukemia/code/GSE99612.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Acute_Myeloid_Leukemia"
cohort = "GSE99612"

# Input paths
in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE99612"

# Output paths
out_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/GSE99612.csv"
out_gene_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/gene_data/GSE99612.csv"
out_clinical_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/clinical_data/GSE99612.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 - Not a miRNA or methylation study
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# This is cell line data, not human subject data
trait_row = None

def convert_trait(x):
    return None

age_row = None
def convert_age(x):
    return None

gender_row = None
def convert_gender(x):
    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. Skip clinical feature extraction since trait_row is None
# 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])
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))
# 1. Identify relevant columns for gene mapping
# 'ID' in gene annotation matches identifiers in gene expression data
# 'gene_assignment' contains gene symbol information

# 2. Extract gene mapping dataframe
gene_mapping = get_gene_mapping(gene_annotation, 'ID', 'gene_assignment')

# 3. Apply gene mapping to convert probe data to gene expression data 
gene_data = apply_gene_mapping(gene_data, gene_mapping)
# 1. Normalize gene symbols and save gene data
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. Create minimal linked data structure 
linked_data = pd.DataFrame(index=gene_data.columns)

# 3-4. Skip missing value handling since data is not usable
# Mark as biased since we have no trait data
is_biased = True

# 5. Final validation and save metadata
validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,
    is_biased=is_biased,
    df=linked_data,
    note="This is a cell line experiment, not a human subject study. Contains no trait data."
)

# 6. Skip saving linked data since it's not usable