p3/preprocess/Liver_Cancer/code/GSE228782.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Liver_Cancer"
cohort = "GSE228782"

# Input paths
in_trait_dir = "../DATA/GEO/Liver_Cancer"
in_cohort_dir = "../DATA/GEO/Liver_Cancer/GSE228782"

# Output paths
out_data_file = "./output/preprocess/3/Liver_Cancer/GSE228782.csv"
out_gene_data_file = "./output/preprocess/3/Liver_Cancer/gene_data/GSE228782.csv"
out_clinical_data_file = "./output/preprocess/3/Liver_Cancer/clinical_data/GSE228782.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
# Based on background info mentioning RNA profiles and Affymetrix microarrays
is_gene_available = True

# 2.1 Data Availability
# From sample characteristics:
# Key 2 contains disease info - can be used to determine trait (cancer vs normal)
trait_row = 2
# No age data available
age_row = None  
# No gender data available
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> Optional[int]:
    """Convert disease status to binary (1 for cancer, 0 for non-cancer)"""
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip().upper()
    if value in ['CCC', 'CRC MET', 'HCC']:  # These are cancer types
        return 1
    elif value == 'OTHER':  # Cannot determine cancer status
        return None
    return 0

def convert_age(value: str) -> Optional[float]:
    """Placeholder function since age is not available"""
    return None

def convert_gender(value: str) -> Optional[int]:
    """Placeholder function since gender is not available"""
    return None

# 3. Save metadata - initial filtering
_ = 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. Extract clinical features since trait_row is not None
clinical_df = 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
preview = preview_df(clinical_df)
print(f"Clinical data preview: {preview}")

# Save clinical data
clinical_df.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())
# The identifiers like "11715100_at" are Affymetrix probe IDs, not human gene symbols
# They need to be mapped to gene symbols for biological interpretation
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))
# Map probe IDs to gene symbols and convert probe-level measurements to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Print the gene data structure to verify mapping success
print("\nGene Expression Data Preview:")
preview = preview_df(gene_data)
print(json.dumps(preview, indent=2))
# 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_df, 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)