p3/preprocess/Metabolic_Rate/code/GSE89231.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Metabolic_Rate"
cohort = "GSE89231"

# Input paths
in_trait_dir = "../DATA/GEO/Metabolic_Rate"
in_cohort_dir = "../DATA/GEO/Metabolic_Rate/GSE89231"

# Output paths
out_data_file = "./output/preprocess/3/Metabolic_Rate/GSE89231.csv"
out_gene_data_file = "./output/preprocess/3/Metabolic_Rate/gene_data/GSE89231.csv"
out_clinical_data_file = "./output/preprocess/3/Metabolic_Rate/clinical_data/GSE89231.csv"
json_path = "./output/preprocess/3/Metabolic_Rate/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, the series investigates gene expression profiling of DLBCL cell lines
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
trait_row = 0  # Cell line names contain intrinsic doxorubicin sensitivity information
age_row = None  # No age data for cell lines
gender_row = None  # No gender data for cell lines

# 2.2 Data Type Conversion 
def convert_trait(x):
    # Extract cell line name after colon and strip whitespace
    cell_line = x.split(':')[1].strip()
    
    # Based on background info, DLBCL cell lines have different intrinsic sensitivity 
    # Convert to binary: 1 for sensitive, 0 for resistant cell lines
    # Reference: https://pubmed.ncbi.nlm.nih.gov/28255297/
    sensitive_lines = {'RIVA', 'U2932', 'FARAGE'}
    resistant_lines = {'OCI-Ly7', 'SU-DHL-5', 'NU-DHL-1'}
    
    if cell_line in sensitive_lines:
        return 1
    elif cell_line in resistant_lines:
        return 0
    return None

convert_age = None  # No age data
convert_gender = None  # No gender data

# 3. Save Metadata
# Initial filtering - trait data is 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=True
)

# 4. Clinical Feature Extraction
# Since trait_row is not None, extract clinical features
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
preview_result = preview_df(clinical_features)
print("Preview of clinical features:")
print(preview_result)

# Save clinical features
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 observing "_at" or "_s_at" patterns in the identifiers (e.g. "1007_s_at", "1053_at"), 
# these appear to be Affymetrix probe IDs rather than gene symbols.
# They require mapping to human 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. Choose columns for mapping
# The gene expression data uses probe IDs (e.g., "1007_s_at") which match the 'ID' column
# Gene symbols are stored in the 'Gene Symbol' column
prob_col = 'ID'
gene_col = 'Gene Symbol'

# 2. Get gene mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)

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

# Preview mapped gene expression data
print("\nPreview of gene expression data after mapping:")
print(f"Shape: {gene_data.shape}")
print("\nFirst few rows:")
print(gene_data.head())
# 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(clinical_features, 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 doxorubicin sensitivity data from DLBCL cell lines, suitable for binary analysis."
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)