p3/preprocess/Rectal_Cancer/code/GSE150082.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Rectal_Cancer"
cohort = "GSE150082"

# Input paths
in_trait_dir = "../DATA/GEO/Rectal_Cancer"
in_cohort_dir = "../DATA/GEO/Rectal_Cancer/GSE150082"

# Output paths
out_data_file = "./output/preprocess/3/Rectal_Cancer/GSE150082.csv"
out_gene_data_file = "./output/preprocess/3/Rectal_Cancer/gene_data/GSE150082.csv"
out_clinical_data_file = "./output/preprocess/3/Rectal_Cancer/clinical_data/GSE150082.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 
# From Series_title and Series_summary, we can see this is a microarray gene expression dataset
is_gene_available = True 

# 2. Variable Availability and Data Type Conversion
# Trait (Response to treatment)
trait_row = 4 # 'response' field has Good/Poor values
def convert_trait(x):
    if pd.isna(x): return None
    val = x.split(': ')[1]
    if val == 'Poor': return 0
    if val == 'Good': return 1
    return None

# Age
age_row = 2 
def convert_age(x):
    if pd.isna(x): return None
    try:
        return int(x.split(': ')[1])
    except:
        return None

# Gender/Sex
gender_row = 0
def convert_gender(x): 
    if pd.isna(x): return None
    val = x.split(': ')[1]
    if val == 'F': return 0
    if val == 'M': return 1
    return 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
clinical_features = geo_select_clinical_features(clinical_data, trait, trait_row, convert_trait,
                                               age_row, convert_age,
                                               gender_row, convert_gender)

# Preview the extracted features
preview_dict = preview_df(clinical_features)
print("Preview of clinical features:")
print(preview_dict)

# 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])
# Based on inspecting the gene identifiers (e.g. 'A_23_P100001'), these appear to be probe IDs 
# from an Agilent microarray platform, not standard human gene symbols.
# They will need to be mapped to proper 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. Determine mapping columns:
# 'ID' column in annotation contains same identifiers as gene expression data
# 'GENE_SYMBOL' contains the gene symbols we want to map to

# 2. Get mapping dataframe with ID and gene symbol columns
gene_mapping = get_gene_mapping(gene_annotation, 'ID', 'GENE_SYMBOL')

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

# Preview transformed data
print("Gene expression data shape after mapping:", gene_data.shape)
print("\nPreview of first few rows:")
print(gene_data.head())
# 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)