p3/preprocess/Thymoma/code/GSE42977.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Thymoma"
cohort = "GSE42977"

# Input paths
in_trait_dir = "../DATA/GEO/Thymoma"
in_cohort_dir = "../DATA/GEO/Thymoma/GSE42977"

# Output paths
out_data_file = "./output/preprocess/3/Thymoma/GSE42977.csv"
out_gene_data_file = "./output/preprocess/3/Thymoma/gene_data/GSE42977.csv"
out_clinical_data_file = "./output/preprocess/3/Thymoma/clinical_data/GSE42977.csv"
json_path = "./output/preprocess/3/Thymoma/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 shape and first few rows to verify data
print("Background Information:")
print(background_info)
print("\nClinical Data Shape:", clinical_data.shape)
print("\nFirst few rows of Clinical Data:")
print(clinical_data.head())

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
# Based on background info, this is a microarray study, so gene expression data should be available
is_gene_available = True

# 2.1 Data Availability
# From sample characteristics dictionary, trait (Thymoma) data is in row 0
trait_row = 0 
# Age and gender data are not available in sample characteristics
age_row = None
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    if pd.isna(value):
        return None
    # Extract value after "tissue: "
    value = value.split("tissue: ")[-1].strip()
    # Convert to binary - 1 for Thymoma/Metastatic Thymoma, 0 for others
    if value in ["Thymoma", "Metastatic Thymoma"]:
        return 1
    return 0

def convert_age(value):
    return None  # Age data not available

def convert_gender(value):
    return None  # Gender data not available

# 3. Save Metadata
# Perform initial validation (is_final=False)
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. Clinical Feature Extraction
# Since trait_row is not None, we 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
print("\nPreview of extracted clinical features:")
print(preview_df(clinical_features))

# Save clinical features to CSV
clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs 
print("First 20 gene/probe IDs:")
print(list(genetic_data.index[:20]))
# These are Illumina probe IDs (starting with ILMN_), not human gene symbols
# They will need to be mapped to official gene symbols
requires_gene_mapping = True
# Extract gene annotation from SOFT file 
gene_annotation = get_gene_annotation(soft_file_path)

# Preview annotation structure
preview = preview_df(gene_annotation)
print("Gene annotation preview:")
print(preview)
# 1. Observe the identifiers
# Gene expression data uses probe IDs starting with "ILMN_"
# In gene annotation, these IDs are under the 'ID' column 
# Gene symbols are under the 'Symbol' column

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

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

# Print shape and preview to verify the conversion
print("\nGene expression data shape:", gene_data.shape)
print("\nPreview of gene expression data:")
print(preview_df(gene_data))
# 1. Normalize gene symbols in gene expression data
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)
print("\nGene data shape (normalized gene-level):", gene_data.shape) 

# Load clinical data previously processed
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
print("\nClinical data shape:", selected_clinical_df.shape)

# 2. Link clinical and genetic data using normalized gene-level data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
print("\nLinked data shape:", linked_data.shape)

# 3. Handle missing values systematically  
if trait in linked_data.columns:
    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 = "Data was successfully preprocessed from probe-level to gene-level expression using gene symbol normalization with NCBI Gene database."
    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 and not biased
    if is_usable and not trait_biased:
        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
        linked_data.to_csv(out_data_file)