p3/preprocess/Acute_Myeloid_Leukemia/code/GSE222169.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Acute_Myeloid_Leukemia"
cohort = "GSE222169"

# Input paths
in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE222169"

# Output paths
out_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/GSE222169.csv"
out_gene_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/gene_data/GSE222169.csv"
out_clinical_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/clinical_data/GSE222169.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  # Given information about leukemia cell lines suggests this is likely gene expression data

# 2. Variable Availability and Data Type Conversion
# 2.1 Row identification
trait_row = 0  # Contains AML information in 'cell line' and 'tissue source' fields
age_row = None  # Age information not available
gender_row = None  # Gender information not available

# 2.2 Conversion Functions
def convert_trait(value: str) -> int:
    """Convert trait values to binary: 1 for AML cases"""
    if pd.isna(value):
        return None
    value = value.split(': ')[-1].lower()
    # Both cell lines and patient samples are AML cases
    if 'aml' in value or 'molm-14' in value or 'oci-aml2' in value:
        return 1
    return None

def convert_age(value: str) -> float:
    """Placeholder function - age data not available"""
    return None

def convert_gender(value: str) -> int:
    """Placeholder function - gender data 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
if trait_row is not None:
    selected_clinical = 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 the processed clinical data
    print("Preview of processed clinical data:")
    print(preview_df(selected_clinical))
    
    # Save to CSV
    selected_clinical.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])
# The identifiers appear to be transcript cluster IDs from Affymetrix Clariom D arrays
# They need to be mapped to human 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))
# Looking at the example values in SPOT_ID.1, we can find gene symbols within parentheses
# followed by ']', like '(OR4F5)', '(SAMD11)', etc.
# Create a custom function to extract gene symbols from complex text descriptions
def extract_gene_symbols_from_desc(text):
    """Extract gene symbols from complex annotation text that follows format:
    ... (SYMBOL) [gene_biotype ...
    """
    if pd.isna(text):
        return []
    # Split on '//' to get separate entries and look for gene symbol pattern
    # Gene symbols typically appear in parentheses before [gene_biotype 
    symbols = []
    entries = text.split('//')
    for entry in entries:
        # Look for text in parentheses followed by [gene_biotype
        match = re.search(r'\(([^)]+)\)\s*\[gene_biotype', entry)
        if match:
            symbol = match.group(1)
            # Some entries have additional text like "(Drosophila)" - remove that
            symbol = re.sub(r'\s*\([^)]+\)$', '', symbol)
            symbols.append(symbol)
    return list(set(symbols))  # Remove duplicates

# Add a column with extracted gene symbols
gene_annotation['Gene'] = gene_annotation['SPOT_ID.1'].apply(extract_gene_symbols_from_desc)

# Get mapping between IDs and gene symbols
mapping_data = get_gene_mapping(gene_annotation, 'ID', 'Gene')

# Apply gene mapping to convert probe-level data to gene-level expression 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)

# Get clinical data from previous step
selected_clinical = geo_select_clinical_features(
    clinical_df=clinical_data,
    trait=trait,
    trait_row=0,  # Using first row containing cell line info
    convert_trait=convert_trait,  # Using previously defined convert_trait function
    age_row=None,
    convert_age=None, 
    gender_row=None,
    convert_gender=None
)

# 2. Link clinical and genetic data 
linked_data = geo_link_clinical_genetic_data(selected_clinical, 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
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 comparing different AML cell lines and treatments."
)

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