p3/preprocess/COVID-19/code/GSE211378.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "COVID-19"
cohort = "GSE211378"

# Input paths
in_trait_dir = "../DATA/GEO/COVID-19"
in_cohort_dir = "../DATA/GEO/COVID-19/GSE211378"

# Output paths
out_data_file = "./output/preprocess/3/COVID-19/GSE211378.csv"
out_gene_data_file = "./output/preprocess/3/COVID-19/gene_data/GSE211378.csv"
out_clinical_data_file = "./output/preprocess/3/COVID-19/clinical_data/GSE211378.csv"
json_path = "./output/preprocess/3/COVID-19/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 from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

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

# Print background info
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# Based on series summary mentioning "Whole Blood profiling", gene expression data should be available
is_gene_available = True

# 2.1 Data Availability 
# Based on series design describing COVID convalescent vs Healthy donors
trait_row = 12  # nanostring_id contains trait info
age_row = None  # No age data available
gender_row = None  # No gender data available

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    """Convert COVID-19 status to binary (0: healthy, 1: COVID convalescent)"""
    if not value or ':' not in value:
        return None
    id_str = value.split(':')[1].strip()
    # From series design, ID format suggests trait info
    if '_' in id_str:
        return 1  # COVID convalescent
    else:
        return 0  # Healthy

def convert_age(value):
    """Not needed as age data is not available"""
    return None

def convert_gender(value):
    """Not needed as gender data is not available"""
    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
if trait_row is not None:
    clinical_features = geo_select_clinical_features(clinical_data, trait, trait_row, convert_trait)
    print("Preview of extracted clinical features:")
    print(preview_df(clinical_features))
    clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data
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 look like official human gene symbols (HGNC approved symbols)
# Examples:
# ACE - Angiotensin Converting Enzyme
# ACKR2/3/4 - Atypical Chemokine Receptors 
# ACSL1/3/4 - Acyl-CoA Synthetase Long Chain Family Members
# AKT1/2/3 - AKT Serine/Threonine Kinases
requires_gene_mapping = False
# 1. Normalize gene symbols and save
normalized_gene_data = normalize_gene_symbols_in_index(genetic_data)
normalized_gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
clinical_data_loaded = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_data_loaded, normalized_gene_data)

# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)

# 4. Check for bias in features
trait_biased, filtered_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate and save cohort info
note = "This dataset contains COVID-19 binary trait data (convalescent vs healthy) and gene expression data from whole blood samples. Age and gender data are not available."
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=filtered_data,
    note=note
)

# 6. Save linked data if usable
if is_usable:
    filtered_data.to_csv(out_data_file)