p3/preprocess/Epilepsy/code/GSE123993.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Epilepsy"
cohort = "GSE123993"

# Input paths
in_trait_dir = "../DATA/GEO/Epilepsy"
in_cohort_dir = "../DATA/GEO/Epilepsy/GSE123993"

# Output paths
out_data_file = "./output/preprocess/3/Epilepsy/GSE123993.csv"
out_gene_data_file = "./output/preprocess/3/Epilepsy/gene_data/GSE123993.csv"
out_clinical_data_file = "./output/preprocess/3/Epilepsy/clinical_data/GSE123993.csv"
json_path = "./output/preprocess/3/Epilepsy/cohort_info.json"

# Get relevant file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Get dictionary of unique values per row in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("Background Information:")
print("-" * 50)
print(background_info)
print("\n")

# Print clinical data unique values
print("Sample Characteristics:")
print("-" * 50)
for row, values in unique_values_dict.items():
    print(f"{row}:")
    print(f"  {values}")
    print()
# 1. Gene Expression Data Availability
# The dataset uses Affymetrix HuGene arrays for whole genome expression profiling,
# so it contains gene expression data
is_gene_available = True

# 2.1 Data Availability
# Trait (intervention group) is in row 3
trait_row = 3
# Age is not explicitly recorded (all are elderly > 65 but exact age unknown)
age_row = None 
# Gender is in row 1
gender_row = 1

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    # Extract value after colon
    if ':' in value:
        value = value.split(':')[1].strip()
    # Convert to binary: 1 for treatment, 0 for placebo
    if '25-hydroxycholecalciferol' in value or '25(OH)D3' in value:
        return 1
    elif 'Placebo' in value:
        return 0
    return None

def convert_gender(value):
    if ':' in value:
        value = value.split(':')[1].strip()
    # Convert to binary: 0 for female, 1 for male    
    if value.lower() == 'female':
        return 0
    elif value.lower() == 'male':
        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
# Extract clinical features since trait_row is not None
selected_clinical_df = geo_select_clinical_features(clinical_df=clinical_data,
                                                  trait=trait,
                                                  trait_row=trait_row,
                                                  convert_trait=convert_trait,
                                                  gender_row=gender_row,
                                                  convert_gender=convert_gender)

# Preview the clinical data
preview_dict = preview_df(selected_clinical_df)
print("Preview of clinical data:")
print(preview_dict)

# Save clinical data
selected_clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 probe IDs
print("First 20 probe IDs:")
print(genetic_data.index[:20])
# These IDs appear to be probe IDs from a microarray platform rather than standard gene symbols
# They are numeric identifiers starting with '1665' which is consistent with microarray probe formats
# We will need to map these probe IDs to their corresponding gene symbols
requires_gene_mapping = True
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)

# Preview column names and first few values
preview_dict = preview_df(gene_annotation)
print("Column names and preview values:")
for col, values in preview_dict.items():
    print(f"\n{col}:")
    print(values)
# 1. Looking at gene annotation data, 'ID' column matches identifiers in gene expression data,
#    and 'gene_assignment' contains gene symbols

# 2. Extract mapping between probe IDs and gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')

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

# 4. Normalize gene symbols to handle synonyms
gene_data = normalize_gene_symbols_in_index(gene_data)

# 5. Save gene expression data
gene_data.to_csv(out_gene_data_file)

# Preview gene data
preview_dict = preview_df(gene_data)
print("Preview of gene data:")
for i, (col, values) in enumerate(preview_dict.items()):
    if i >= 5:  # limit to first 5 items
        break
    print(f"\n{col}:")
    print(values)
# Read the processed clinical and gene data files 
clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
gene_data = pd.read_csv(out_gene_data_file, index_col=0)  # Already normalized in step 6

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

# Handle missing values systematically
linked_data = handle_missing_values(linked_data, trait)

# Detect bias in trait and demographic features, remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# Validate data quality and save cohort info
note = ("This dataset studies vitamin D supplementation effects on skeletal muscle transcriptome. "
        "Data quality is acceptable but the study size is relatively small.")
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=is_biased,
    df=linked_data,
    note=note
)

# Save linked data if usable
if is_usable:
    linked_data.to_csv(out_data_file)
else:
    print(f"Dataset {cohort} did not pass quality validation and will not be saved.")