output/preprocess/Endometrioid_Cancer/code/GSE94524.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Endometrioid_Cancer"
cohort = "GSE94524"

# Input paths
in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE94524"

# Output paths
out_data_file = "./output/z2/preprocess/Endometrioid_Cancer/GSE94524.csv"
out_gene_data_file = "./output/z2/preprocess/Endometrioid_Cancer/gene_data/GSE94524.csv"
out_clinical_data_file = "./output/z2/preprocess/Endometrioid_Cancer/clinical_data/GSE94524.csv"
json_path = "./output/z2/preprocess/Endometrioid_Cancer/cohort_info.json"


# Step 1: Initial Data Loading
from tools.preprocess import *
# 1. Identify the paths to the SOFT file and the matrix file
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# 2. Read the matrix file to obtain background information and sample characteristics data
background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)

# 3. Obtain the sample characteristics dictionary from the clinical dataframe
sample_characteristics_dict = get_unique_values_by_row(clinical_data)

# 4. Explicitly print out all the background information and the sample characteristics dictionary
print("Background Information:")
print(background_info)
print("Sample Characteristics Dictionary:")
print(sample_characteristics_dict)

# Step 2: Dataset Analysis and Clinical Feature Extraction
# 1. Gene Expression Data Availability
is_gene_available = True  # The study focuses on differential enhancer activity, suggesting gene expression data

# 2. Variable Availability and Data Type Conversion

# 2.1 Data Availability
# trait: Only one unique value 'tissue: endometrioid adenocarcinoma' - constant feature, not useful
trait_row = None  
age_row = None    # No age information available
gender_row = None # No gender information available

# 2.2 Data Type Conversion
def convert_trait(value):
    """Convert trait values to binary"""
    if value is None:
        return None
    return None  # Not used since trait_row is None

def convert_age(value):
    """Convert age values to continuous"""
    if value is None:
        return None
    return None  # Not used since age_row is None

def convert_gender(value):
    """Convert gender values to binary (0=female, 1=male)"""
    if value is None:
        return None
    return None  # Not used since gender_row is 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
# Skip this step since trait_row is None (no clinical data available)

# Step 3: Gene Data Extraction
# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
gene_data = get_genetic_data(matrix_file)

# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
print(gene_data.index[:20])

# Step 4: Gene Identifier Review
print("Examining gene identifiers...")
print("Sample identifiers:", gene_data.index[:10].tolist())

# These are numeric identifiers (1, 2, 3, etc.), not human gene symbols
# Human gene symbols are typically alphanumeric strings like BRCA1, TP53, GAPDH, etc.
# These numeric IDs likely represent probe IDs or other database identifiers that need mapping

requires_gene_mapping = True

# Step 5: Gene Annotation
# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
gene_annotation = get_gene_annotation(soft_file)

# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
print("Gene annotation preview:")
print(preview_df(gene_annotation))

# Step 6: Gene Identifier Mapping
# 1. Identify the mapping columns
# 'ID' column matches the gene expression data identifiers (numeric IDs)
# 'HUGO' column contains gene symbols, though it has some NaN values
prob_col = 'ID'
gene_col = 'HUGO'

# 2. Get gene mapping dataframe
gene_mapping = get_gene_mapping(gene_annotation, prob_col, gene_col)

# 3. Apply gene mapping to convert probe-level data to gene expression data
gene_data = apply_gene_mapping(gene_data, gene_mapping)

# Normalize gene symbols to ensure consistency
gene_data = normalize_gene_symbols_in_index(gene_data)

print(f"Gene expression data shape after mapping: {gene_data.shape}")
print(f"Sample gene names: {gene_data.index[:10].tolist()}")

# Step 7: Data Normalization and Linking
# 1. Normalize the obtained gene data and save it
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)

# Since no clinical data with variable traits is available (trait_row was None in Step 2),
# this dataset is not suitable for association studies
print("No variable clinical trait data available - dataset not suitable for association studies")

# Create empty dataframe to represent unavailable linked data
linked_data = pd.DataFrame()

# 5. Conduct final quality validation
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,  # Dataset is biased/unusable due to constant trait values
    df=linked_data,
    note="INFO: Dataset contains only constant trait values (all endometrioid adenocarcinoma), no variable clinical features for association analysis"
)

# 6. Since the dataset is not usable for association studies, do not save linked data file
print(f"Dataset usability: {is_usable}")