p3/preprocess/Sarcoma/code/GSE159848.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Sarcoma"
cohort = "GSE159848"

# Input paths
in_trait_dir = "../DATA/GEO/Sarcoma"
in_cohort_dir = "../DATA/GEO/Sarcoma/GSE159848"

# Output paths
out_data_file = "./output/preprocess/3/Sarcoma/GSE159848.csv"
out_gene_data_file = "./output/preprocess/3/Sarcoma/gene_data/GSE159848.csv"
out_clinical_data_file = "./output/preprocess/3/Sarcoma/clinical_data/GSE159848.csv"
json_path = "./output/preprocess/3/Sarcoma/cohort_info.json"

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

# Get background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")

# Get dictionary of unique values per row 
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
    print(f"\n{row}:")
    print(values)
# 1. Gene Expression Data Availability
# The dataset uses "Agilent-014850 Whole Human Genome Microarray", so it contains gene expression data
is_gene_available = True

# 2. Variable Availability and Row Identification
trait_row = 3  # metastasis data is available in row 3
age_row = 1    # age data is available in row 1 
gender_row = 0  # gender data is available in row 0

# Define conversion functions
def convert_trait(value: str) -> Optional[float]:
    """Convert metastasis status to binary: 0 = no metastasis, 1 = has metastasis"""
    if not value or 'metastasis:' not in value:
        return None
    try:
        return float(value.split(': ')[1])
    except:
        return None

def convert_age(value: str) -> Optional[float]:
    """Convert age to continuous values"""
    if not value or 'age:' not in value:
        return None
    try:
        return float(value.split(': ')[1])
    except:
        return None

def convert_gender(value: str) -> Optional[float]:
    """Convert gender to binary: 0 = female, 1 = male"""
    if not value or 'Sex:' not in value:
        return None
    gender = value.split(': ')[1].strip().upper()
    if gender == 'F':
        return 0.0
    elif gender == 'M':
        return 1.0
    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. Extract clinical features 
selected_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 data
print(preview_df(selected_clinical_df))

# Save clinical data
selected_clinical_df.to_csv(out_clinical_data_file)
# Get gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Examine data structure
print("Data structure and head:")
print(genetic_data.head())

print("\nShape:", genetic_data.shape)

print("\nFirst 20 row IDs (gene/probe identifiers):")
print(list(genetic_data.index)[:20])

# Get a few column names to verify sample IDs
print("\nFirst 5 column names:")
print(list(genetic_data.columns)[:5])
# The identifiers start with 'A_23_P', which appear to be Agilent probe IDs rather than standard human gene symbols
# These need to be mapped to their corresponding gene symbols for analysis
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)

# Preview column names and values from annotation dataframe
print("Gene annotation DataFrame preview:")
print(preview_df(gene_annotation))
# 1. From the preview, we can see that 'ID' contains probe identifiers matching gene expression data,
# and 'GENE_SYMBOL' contains the gene symbols
probe_col = 'ID'
gene_col = 'GENE_SYMBOL'

# 2. Get mapping between probe IDs and gene symbols
gene_mapping = get_gene_mapping(gene_annotation, probe_col, gene_col)

# 3. Convert probe-level expression to gene-level expression
gene_data = apply_gene_mapping(genetic_data, gene_mapping)

# Save gene expression data to file
gene_data.to_csv(out_gene_data_file)

# Preview result
print("\nGene expression data preview:")
print(preview_df(gene_data))
print("\nShape:", gene_data.shape)
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
print("Gene data shape after normalization:", gene_data.shape)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) 
gene_data.to_csv(out_gene_data_file)

# Load clinical data previously processed
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
print("\nClinical data shape:", selected_clinical_df.shape)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
print("\nLinked data shape:", linked_data.shape)

# 3. Handle missing values systematically  
if trait in linked_data.columns:
    linked_data = handle_missing_values(linked_data, trait)

    # 4. Check for bias in trait and demographic features
    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

    # 5. Final validation and information saving
    note = "This dataset contains gene expression data from myxoid liposarcoma samples, with metastasis status as the trait."
    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=note
    )

    # 6. Save linked data only if usable 
    if is_usable:
        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
        linked_data.to_csv(out_data_file)
else:
    # Handle case where clinical features were not properly extracted
    note = "Failed to extract clinical trait information from sample characteristics."
    validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path, 
        is_gene_available=True,
        is_trait_available=False,
        is_biased=None,
        df=None,
        note=note
    )