p3/preprocess/Rectal_Cancer/code/GSE109057.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Rectal_Cancer"
cohort = "GSE109057"

# Input paths
in_trait_dir = "../DATA/GEO/Rectal_Cancer"
in_cohort_dir = "../DATA/GEO/Rectal_Cancer/GSE109057"

# Output paths
out_data_file = "./output/preprocess/3/Rectal_Cancer/GSE109057.csv"
out_gene_data_file = "./output/preprocess/3/Rectal_Cancer/gene_data/GSE109057.csv"
out_clinical_data_file = "./output/preprocess/3/Rectal_Cancer/clinical_data/GSE109057.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
# This is gene expression microarray data, as stated in background info
is_gene_available = True

# 2. Feature Row and Type Conversion Functions
# trait is available from row 0
trait_row = 0
def convert_trait(x: str) -> int:
    # All samples are rectal cancer, binary encoding 1
    return 1 

# gender is available from row 1
gender_row = 1
def convert_gender(x: str) -> int:
    val = x.split(': ')[1]
    if val == 'F':
        return 0
    elif val == 'M':
        return 1
    return None

# age is available from row 2
age_row = 2
def convert_age(x: str) -> float:
    val = x.split(': ')[1]
    # Extract lower bound of age range as representative value
    try:
        lower = float(val.split(' <=')[0])
        return lower
    except:
        return None

# 3. Save initial filtering results
validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=True
)

# 4. Extract clinical features and save
clinical_df = geo_select_clinical_features(
    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_df(clinical_df))

# Save to CSV
clinical_df.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 the row IDs like '11715100_at', '11715101_s_at', '11715102_x_at'
# These appear to be Affymetrix probe IDs rather than 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))
# Create mapping dataframe using ID and Gene Symbol columns
mapping_df = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol')

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

# Normalize gene symbols using standard dictionary
gene_data = normalize_gene_symbols_in_index(gene_data)

# Save gene expression data 
gene_data.to_csv(out_gene_data_file)

# Print preview of gene data 
print("Preview of gene expression data:")
print(preview_df(gene_data))
# Reload clinical data that was processed earlier
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

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

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_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 examining chemoradiotherapy response."
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)