p3/preprocess/Kidney_Clear_Cell_Carcinoma/code/GSE245862.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Kidney_Clear_Cell_Carcinoma"
cohort = "GSE245862"

# Input paths
in_trait_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma"
in_cohort_dir = "../DATA/GEO/Kidney_Clear_Cell_Carcinoma/GSE245862"

# Output paths
out_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/GSE245862.csv"
out_gene_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/gene_data/GSE245862.csv"
out_clinical_data_file = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/clinical_data/GSE245862.csv"
json_path = "./output/preprocess/3/Kidney_Clear_Cell_Carcinoma/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)

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

# Print background information
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene expression data availability
# Yes, this is a microarray study of gene expression data
is_gene_available = True

# 2.1 Data availability
# Looking at sample characteristics - key 0 contains phenotype data that can be used for trait classification
trait_row = 0

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

# 2.2 Data type conversion functions
def convert_trait(value):
    """Convert phenotype to binary - normal (0) vs modified STAT3 (1)"""
    if not isinstance(value, str):
        return None
    if ':' in value:
        value = value.split(':', 1)[1].strip()
    if "Normal" in value:
        return 0
    elif value:  # Any modified STAT3 phenotype
        return 1
    return None

convert_age = None
convert_gender = 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 since trait data is available
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 extracted features
print(preview_df(clinical_df))

# Save clinical data
clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data from the matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs
print("First 20 row IDs:")
print(genetic_data.index[:20].tolist())
# The IDs appear to be probe IDs or accession numbers, not gene symbols
# They are numerical identifiers, whereas gene symbols are typically alphanumeric like 'BRCA1', 'TP53' etc.
# Therefore mapping to gene symbols will be required
requires_gene_mapping = True
# Extract gene annotation data from SOFT file
gene_metadata = get_gene_annotation(soft_file_path)

# Display information about the annotation data
print("Column names:")
print(gene_metadata.columns.tolist())

# Look at general data statistics 
print("\nData shape:", gene_metadata.shape)

# Preview the first few rows
print("\nPreview of the annotation data:")
print(json.dumps(preview_df(gene_metadata), indent=2))
# Extract gene symbols from annotations and create mapping
def extract_hgnc_symbols(text):
    """Extract HGNC gene symbols from annotation text"""
    if not isinstance(text, str):
        return []
    pattern = r"HGNC Symbol;Acc:HGNC:\d+\] // ([A-Z][A-Z0-9-]+)"
    matches = re.findall(pattern, text)
    return list(set(matches))

mapping_df = gene_metadata[['ID', 'SPOT_ID.1']].copy()
mapping_df['Gene'] = mapping_df['SPOT_ID.1'].apply(extract_hgnc_symbols)
mapping_df = mapping_df[['ID', 'Gene']]

# Convert probe measurements to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Normalize gene symbols to their latest official symbols
gene_data = normalize_gene_symbols_in_index(gene_data)

# Preview processed gene data
print("Preview of gene data after mapping:")
print(preview_df(gene_data))

# Save gene expression data
gene_data.to_csv(out_gene_data_file)
# Select clinical features
clinical_features = geo_select_clinical_features(
    clinical_data,  # Use clinical_data from previous steps
    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
)

# 1. Gene data already normalized in previous step

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)

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

# 4. Judge whether features are biased and remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save metadata
note = "Dataset from a cancer gene expression study using oligonucleotide microarrays, containing samples of kidney chromophobe tumors and normal tissues."
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 the linked data only if it's usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)