p3/preprocess/Liver_Cancer/code/GSE178201.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Liver_Cancer"
cohort = "GSE178201"

# Input paths
in_trait_dir = "../DATA/GEO/Liver_Cancer"
in_cohort_dir = "../DATA/GEO/Liver_Cancer/GSE178201"

# Output paths
out_data_file = "./output/preprocess/3/Liver_Cancer/GSE178201.csv"
out_gene_data_file = "./output/preprocess/3/Liver_Cancer/gene_data/GSE178201.csv"
out_clinical_data_file = "./output/preprocess/3/Liver_Cancer/clinical_data/GSE178201.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, this dataset does contain gene expression data (L1000 platform)
is_gene_available = True

# 2.1 Data Availability
# Looking at sample characteristics, there is no trait (cancer status), age or gender info
# These are cell line experiments, not patient samples
trait_row = None
age_row = None  
gender_row = None

# 2.2 Data Type Conversion Functions
# Not needed since we have no clinical data, but defining empty functions to satisfy interface
def convert_trait(x):
    return None

def convert_age(x):
    return None

def convert_gender(x):  
    return None

# 3. Save Metadata
# Initial filtering - trait data not available (cell lines)
_ = validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=False
)

# 4. Clinical Feature Extraction
# Skip since trait_row is None (no clinical data available)
# 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 row index values appear to be Entrez Gene IDs
# These are numerical identifiers that need to be mapped to human gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file)

# Preview the annotation data structure
print("Gene Annotation Preview:")
print("\nColumns:", gene_annotation.columns.tolist())
preview = preview_df(gene_annotation)
print(json.dumps(preview, indent=2))

# Get mapping between probe IDs and gene symbols 
prob_col = 'ID'  # Column containing probe IDs
gene_col = 'pr_gene_symbol'  # Column containing gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)

# Preview the mapping
print("\nGene Mapping Preview:")
mapping_preview = preview_df(mapping_df)
print(json.dumps(mapping_preview, indent=2))
# Apply gene mapping to convert probe IDs to gene symbols
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Print DataFrame info and preview to verify mapping result
print("Gene Expression Data After Mapping:")
print("\nDataFrame info:")
print(gene_data.info())
print("\nDataFrame dimensions:", gene_data.shape)
print("\nFirst few rows and columns:")
print(gene_data.head().iloc[:, :5])
print("\nFirst 20 gene symbols:")
print(gene_data.index[:20].tolist())
# 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)

# Create an empty DataFrame for mock validation
mock_df = pd.DataFrame({
    trait: [0,1],  # Mock trait values
    'GENE1': [0,0]  # Mock gene values  
}) 

# Mark dataset as not usable in final validation due to lack of trait data
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort, 
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,
    is_biased=True,  # Consider lack of trait data as biased
    df=mock_df,
    note="Cell line data without clinical trait information - not suitable for trait association analysis"
)

# No linked data to save since data is not usable