p3/preprocess/Acute_Myeloid_Leukemia/code/GSE121431.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Acute_Myeloid_Leukemia"
cohort = "GSE121431"

# Input paths
in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE121431"

# Output paths
out_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/GSE121431.csv"
out_gene_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/gene_data/GSE121431.csv"
out_clinical_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/clinical_data/GSE121431.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
is_gene_available = True  # Based on the background info, this appears to be gene expression data of AML cell lines

# 2. Variable Availability and Data Type Conversion
# For trait: Can infer from Feature 0 (disease state)
trait_row = 0

def convert_trait(value):
    if not isinstance(value, str):
        return None
    value = value.lower().split(': ')[-1]
    if 'acute myeloid leukemia' in value or 'aml' in value:
        return 1
    return None

# For age: Not available as this is cell line data
age_row = None

def convert_age(value):
    return None

# For gender: Not available as this is cell line data 
gender_row = None

def convert_gender(value):
    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
clinical_df = geo_select_clinical_features(
    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_result = preview_df(clinical_df)
print("Preview of clinical data:", preview_result)

# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
clinical_df.to_csv(out_clinical_data_file)
# 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])
# These are probe IDs from Affymetrix arrays that 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))
# 1. & 2. Extract gene mapping from annotation
# ID column matches probe identifiers in gene expression data
# Gene Symbol column contains the corresponding gene symbols
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

# 3. Apply gene mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(gene_data, mapping_data)
# 1. Normalize gene symbols and save normalized gene data
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)

# 3. Handle missing values systematically 
linked_data = handle_missing_values(linked_data, trait)

# 4. Check for biased features and remove them if needed
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate data quality and save metadata
# Note: Dataset contains gene expression data from AML cell lines. The trait "Acute_Myeloid_Leukemia" is defined 
# based on cell subtypes (AMKL vs non-AMKL).
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort, 
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,
    is_biased=is_biased,
    df=linked_data,
    note="Gene expression data from AML cell lines. Trait defined as AMKL vs non-AMKL subtypes."
)

# 6. Save linked data if usable
if is_usable:
    linked_data.to_csv(out_data_file)