p3/preprocess/Colon_and_Rectal_Cancer/code/GSE46862.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Colon_and_Rectal_Cancer"
cohort = "GSE46862"

# Input paths
in_trait_dir = "../DATA/GEO/Colon_and_Rectal_Cancer"
in_cohort_dir = "../DATA/GEO/Colon_and_Rectal_Cancer/GSE46862"

# Output paths
out_data_file = "./output/preprocess/3/Colon_and_Rectal_Cancer/GSE46862.csv"
out_gene_data_file = "./output/preprocess/3/Colon_and_Rectal_Cancer/gene_data/GSE46862.csv"
out_clinical_data_file = "./output/preprocess/3/Colon_and_Rectal_Cancer/clinical_data/GSE46862.csv"
json_path = "./output/preprocess/3/Colon_and_Rectal_Cancer/cohort_info.json"

# Get paths to the 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 feature (row) in clinical data 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("=== Dataset Background Information ===")
print(background_info)
print("\n=== Sample Characteristics ===")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene expression data availability
# Based on background info, this dataset uses Affymetrix GenChip arrays for gene expression profiling
is_gene_available = True

# 2.1 Data availability and row identification
# Row 0 contains chemoradiation therapy response data that can be used to infer cancer status
trait_row = 0  

# Row 1 contains age data
age_row = 1

# Row 2 contains gender data
gender_row = 2

# 2.2 Data type conversion functions
def convert_trait(x):
    # Extract value after colon
    if ':' in str(x):
        value = str(x).split(':')[1].strip()
        # Infer cancer treatment response - 
        # Convert to binary where MI (minimal response) = 1 (worse outcome)
        # and other responses (MO/NT/TO) = 0 (better outcome)
        if value == 'MI':
            return 1
        elif value in ['MO', 'NT', 'TO']:
            return 0
    return None

def convert_age(x):
    if ':' in str(x):
        value = str(x).split(':')[1].strip()
        try:
            return float(value)
        except:
            return None
    return None

def convert_gender(x):
    if ':' in str(x):
        value = str(x).split(':')[1].strip().lower()
        if value == 'female':
            return 0
        elif value == 'male':
            return 1
    return None

# 3. Save metadata
# Trait data is available since trait_row is not None
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. Extract clinical features
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 and save clinical data
print("Preview of clinical data:")
print(preview_df(clinical_df))
clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_df = get_genetic_data(matrix_file)

# Print DataFrame shape and first 20 row IDs
print("DataFrame shape:", genetic_df.shape)
print("\nFirst 20 row IDs:")
print(genetic_df.index[:20])

print("\nPreview of first few rows and columns:")
print(genetic_df.head().iloc[:, :5])
# These appear to be probe IDs from a microarray platform, not gene symbols
requires_gene_mapping = True
# Extract gene annotation data, excluding control probe lines
gene_metadata = get_gene_annotation(soft_file) 

# Preview filtered annotation data
print("Column names:")
print(gene_metadata.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_metadata))
# Looking at the gene annotation data dictionary, 'ID' matches the probe IDs in gene expression data,
# and 'gene_assignment' contains gene symbol information

# Get a mapping dataframe with probe IDs and gene symbols 
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='gene_assignment')

# Apply the gene mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(genetic_df, mapping_df)

# Normalize gene symbols to official HGNC symbols 
gene_data = normalize_gene_symbols_in_index(gene_data)

# Print the shape and preview the processed gene expression data
print("\nProcessed gene expression data shape:", gene_data.shape)
print("\nPreview of processed gene expression data:")
print(preview_df(gene_data))

# Save gene data
gene_data.to_csv(out_gene_data_file)
# 1. Normalize gene symbols and save
gene_data = normalize_gene_symbols_in_index(gene_data) 
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)

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

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

# 4. Check for biased features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and metadata saving
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="Dataset contains gene expression data from rectal cancer samples with chemoradiation therapy response information"
)

# 6. Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)