p3/preprocess/Rectal_Cancer/code/GSE170999.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Rectal_Cancer"
cohort = "GSE170999"

# Input paths
in_trait_dir = "../DATA/GEO/Rectal_Cancer"
in_cohort_dir = "../DATA/GEO/Rectal_Cancer/GSE170999"

# Output paths
out_data_file = "./output/preprocess/3/Rectal_Cancer/GSE170999.csv"
out_gene_data_file = "./output/preprocess/3/Rectal_Cancer/gene_data/GSE170999.csv"
out_clinical_data_file = "./output/preprocess/3/Rectal_Cancer/clinical_data/GSE170999.csv"
json_path = "./output/preprocess/3/Rectal_Cancer/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
# Yes, this dataset contains gene expression data (Affymetrix U133 platform mentioned)
is_gene_available = True

# 2. Variable Availability and Data Type Conversion

# 2.1 Find row indices for clinical variables
trait_row = 0  # KRAS mutation status is at row 0
age_row = None  # Age data not available 
gender_row = None  # Gender data not available

# 2.2 Data type conversion functions
def convert_trait(val):
    if not isinstance(val, str):
        return None
    val = val.lower().split(': ')[-1]
    if 'mutant' in val:
        return 1
    elif 'wild-type' in val:
        return 0
    return None
    
def convert_age(val):
    return None  # Not used since age data not available

def convert_gender(val):
    return None  # Not used since gender data not available

# 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
if trait_row is not None:
    clinical_features = 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 extracted features
    print("Preview of clinical features:")
    print(preview_df(clinical_features))
    
    # Save clinical data
    clinical_features.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])
# These appear to be probe IDs from Affymetrix U133 Plus 2.0 microarray
# Not gene symbols - need to map to HGNC gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)

# Display column names and preview data
print("Column names:")
print(gene_annotation.columns)

print("\nPreview of gene annotation data:")
print(preview_df(gene_annotation))
# 1. The 'ID' column in gene annotation matches probe IDs in gene expression data
# and 'Gene Symbol' contains the corresponding gene symbols

# 2. Extract mapping between probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_annotation, "ID", "Gene Symbol")

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

# Preview gene data
print("Preview of gene expression data after mapping:")
print(gene_data.head())
print("\nShape:", gene_data.shape)
# Reload clinical data that was processed earlier
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# 1. Normalize gene symbols
genetic_data = normalize_gene_symbols_in_index(gene_data)
genetic_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)

# 3. Handle missing values systematically  
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 = "Dataset contains gene expression data from rectal cancer patients with focus on KRAS mutation status."
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)