p3/preprocess/Liver_Cancer/code/GSE148346.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Liver_Cancer"
cohort = "GSE148346"

# Input paths
in_trait_dir = "../DATA/GEO/Liver_Cancer"
in_cohort_dir = "../DATA/GEO/Liver_Cancer/GSE148346"

# Output paths
out_data_file = "./output/preprocess/3/Liver_Cancer/GSE148346.csv"
out_gene_data_file = "./output/preprocess/3/Liver_Cancer/gene_data/GSE148346.csv"
out_clinical_data_file = "./output/preprocess/3/Liver_Cancer/clinical_data/GSE148346.csv"
json_path = "./output/preprocess/3/Liver_Cancer/cohort_info.json"

# Step 1: Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Step 2: Extract background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Step 3: Get dictionary of unique values for each clinical feature
unique_values_dict = get_unique_values_by_row(clinical_data)

# Step 4: Print background info and sample characteristics
print("Dataset Background Information:")
print("-" * 80)
print(background_info)
print("\nSample Characteristics:")
print("-" * 80)
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# Based on the background information, this appears to be a biopsy study with gene expression analysis
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# For trait, we can use tissue disease state (key 3) which indicates lesional (LS) vs non-lesional (NL) liver tissue
trait_row = 3
def convert_trait(x: str) -> Optional[int]:
    if not isinstance(x, str):
        return None
    value = x.split(': ')[-1]
    if value == 'LS':
        return 1  # Lesional
    elif value == 'NL': 
        return 0  # Non-lesional
    return None

# No age information available
age_row = None
convert_age = None

# No gender information available 
gender_row = None 
convert_gender = None

# 3. Save metadata
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
)

# 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 data
    print(preview_df(selected_clinical))
    
    # Save to file
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_clinical.to_csv(out_clinical_data_file)
# 1. Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# 2. Print first 20 row IDs
print("First 20 gene/probe identifiers:")
print(genetic_data.index[:20])
# Those are Affymetrix probe IDs (_at suffix is characteristic of Affy arrays)
# They need to be mapped to gene symbols for consistency and interpretability
requires_gene_mapping = True
# 1. Extract gene annotation data from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)

# 2. Preview annotation data
print("Column names and first few values in gene annotation data:")
print(preview_df(gene_annotation))
# 1. Get gene mapping dataframe from annotation
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

# 2. Apply mapping to convert probe level data to gene level data
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Preview gene data
print("\nFirst 5 genes and 5 samples of gene expression data:")
print(gene_data.iloc[:5, :5])
# 1. Normalize gene symbols and save 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(selected_clinical, gene_data)

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

# 4. Judge if features are biased
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Save cohort information 
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=trait_biased,
    df=linked_data,
    note="Expression array data of NASH-HCC patients and NASH controls. No age/gender information available."
)

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