p3/preprocess/Liver_Cancer/code/GSE228783.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Liver_Cancer"
cohort = "GSE228783"

# Input paths
in_trait_dir = "../DATA/GEO/Liver_Cancer"
in_cohort_dir = "../DATA/GEO/Liver_Cancer/GSE228783"

# Output paths
out_data_file = "./output/preprocess/3/Liver_Cancer/GSE228783.csv"
out_gene_data_file = "./output/preprocess/3/Liver_Cancer/gene_data/GSE228783.csv"
out_clinical_data_file = "./output/preprocess/3/Liver_Cancer/clinical_data/GSE228783.csv"
json_path = "./output/preprocess/3/Liver_Cancer/cohort_info.json"

# Get file paths for soft and matrix files
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values for each clinical feature row 
clinical_features = get_unique_values_by_row(clinical_data)

# Print background info
print("Background Information:")
print(background_info)
print("\nClinical Features and Sample Values:")
print(json.dumps(clinical_features, indent=2))
# 1. Gene Expression Data Availability
# Yes, this appears to be a gene expression dataset studying liver tissue,
# not purely miRNA or methylation
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability 
# trait: Can be inferred from 'disease' field showing cancer types 
trait_row = 2 

# Age and gender not available in characteristics
age_row = None
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    if not value or 'disease:' not in value:
        return None
    value = value.split('disease:')[1].strip().lower()
    # Convert cancer types to binary - 1 for liver cancer (HCC), 0 for others
    if 'hcc' in value:
        return 1
    return 0

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

def convert_gender(value):
    # Not used since gender data not available  
    return 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
# Since trait_row is not None, extract clinical features
clinical_features = 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 extracted features
preview = preview_df(clinical_features)
print("Preview of clinical features:")
print(preview)

# Save clinical data
clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file)

# Print DataFrame info and dimensions to verify data structure
print("DataFrame info:")
print(genetic_data.info())
print("\nDataFrame dimensions:", genetic_data.shape)

# Print an excerpt of the data to inspect row/column structure
print("\nFirst few rows and columns of data:")
print(genetic_data.head().iloc[:, :5])

# Print first 20 row IDs
print("\nFirst 20 gene/probe IDs:")
print(genetic_data.index[:20].tolist())
# Observe the format of gene identifiers: they are probe IDs from an older version of the Affymetrix array platform
# These probe IDs (e.g. "11715100_at") need to be mapped to human gene symbols for downstream analysis
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file)

# Identify mapping columns
prob_col = 'ID'  # Column containing probe IDs 
gene_col = 'Gene Symbol'  # Column containing gene symbols

# Get mapping between probe IDs and gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)

# Preview the mapping data structure
print("Gene Mapping Preview:")
preview = preview_df(mapping_df)
print(json.dumps(preview, indent=2))
# Apply gene mapping to convert probe expression into gene expression
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Preview gene expression data 
print("\nGene Expression Data Preview:")
preview = preview_df(gene_data)
print(json.dumps(preview, indent=2))

# Save gene data
gene_data.to_csv(out_gene_data_file)
# 1. Normalize gene symbols
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_features, gene_data)

# Debug print to check data before handling missing values
print("\nPreview of linked data before handling missing values:")
print(linked_data.head())

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

print("\nPreview of linked data after handling missing values:")
print(linked_data.head())

# 4. Check for biases and remove biased demographic features 
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate dataset quality and save metadata
note = ""
if is_biased:
    note = "The trait distribution is severely biased."

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=note
)

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