p1/preprocess/COVID-19/code/GSE275334.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "COVID-19"
cohort = "GSE275334"

# Input paths
in_trait_dir = "../DATA/GEO/COVID-19"
in_cohort_dir = "../DATA/GEO/COVID-19/GSE275334"

# Output paths
out_data_file = "./output/preprocess/1/COVID-19/GSE275334.csv"
out_gene_data_file = "./output/preprocess/1/COVID-19/gene_data/GSE275334.csv"
out_clinical_data_file = "./output/preprocess/1/COVID-19/clinical_data/GSE275334.csv"
json_path = "./output/preprocess/1/COVID-19/cohort_info.json"

# STEP1
from tools.preprocess import *

# 1. Attempt to identify the paths to the SOFT file and the matrix file
try:
    soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
except AssertionError:
    print("[WARNING] Could not find the expected '.soft' or '.matrix' files in the directory.")
    soft_file, matrix_file = None, None

if soft_file is None or matrix_file is None:
    print("[ERROR] Required GEO files are missing. Please check file names in the cohort directory.")
else:
    # 2. Read the matrix file to obtain background information and sample characteristics data
    background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
    clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
    background_info, clinical_data = get_background_and_clinical_data(matrix_file,
                                                                      background_prefixes,
                                                                      clinical_prefixes)

    # 3. Obtain the sample characteristics dictionary from the clinical dataframe
    sample_characteristics_dict = get_unique_values_by_row(clinical_data)

    # 4. Explicitly print out all the background information and the sample characteristics dictionary
    print("Background Information:")
    print(background_info)
    print("\nSample Characteristics Dictionary:")
    print(sample_characteristics_dict)
# 1. Decide if gene expression data is available
is_gene_available = True  # from the background info: NanoString Immune Exhaustion gene expression panel is used

# 2. Identify availability and conversion for trait, age, and gender
trait_row = 3  # "disease: Healthy control", "disease: Long COVID", "disease: ME/CFS"
age_row = 1    # "age (years): 24", "age (years): 46", ...
gender_row = 2 # "Sex: Female", "Sex: Male"

# Define data type conversion functions
def convert_trait(raw_val: str):
    """
    Convert the disease field to a binary indicator for COVID-19:
    1 if 'Long COVID', 0 if 'Healthy control' or 'ME/CFS'.
    """
    parts = raw_val.split(':', 1)
    if len(parts) > 1:
        value = parts[1].strip().lower()
        if value == "long covid":
            return 1
        elif value in ["healthy control", "me/cfs"]:
            return 0
    return None

def convert_age(raw_val: str):
    """
    Convert the age field to a float.
    """
    parts = raw_val.split(':', 1)
    if len(parts) > 1:
        value = parts[1].strip()
        try:
            return float(value)
        except ValueError:
            return None
    return None

def convert_gender(raw_val: str):
    """
    Convert gender to 0 for female and 1 for male.
    """
    parts = raw_val.split(':', 1)
    if len(parts) > 1:
        value = parts[1].strip().lower()
        if value == "female":
            return 0
        elif value == "male":
            return 1
    return None

# 3. Conduct initial filtering on the dataset
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 (only if trait data is available)
if is_trait_available:
    selected_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
    )

    # Inspect a preview of the extracted clinical features
    print(preview_df(selected_clinical_df))

    # Save the clinical data
    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
# STEP3
# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
gene_data = get_genetic_data(matrix_file)

# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
print(gene_data.index[:20])
# These identifiers (e.g., ACACA, ACADVL, ACAT2, ACSL3, etc.) are recognized human gene symbols.
# Therefore, no additional mapping is needed.
print("requires_gene_mapping = False")
import os
import pandas as pd

# STEP7: Data Normalization and Linking

# 1) Normalize the gene symbols in the previously obtained gene_data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# 2) Load clinical data only if it exists and is non-empty
if os.path.exists(out_clinical_data_file) and os.path.getsize(out_clinical_data_file) > 0:
    # Read the file
    clinical_temp = pd.read_csv(out_clinical_data_file)

    # Adjust row index to label the trait, age, and gender properly
    if clinical_temp.shape[0] == 3:
        clinical_temp.index = [trait, "Age", "Gender"]
    elif clinical_temp.shape[0] == 2:
        clinical_temp.index = [trait, "Gender"]
    elif clinical_temp.shape[0] == 1:
        clinical_temp.index = [trait]

    # 2) Link the clinical and normalized genetic data
    linked_data = geo_link_clinical_genetic_data(clinical_temp, normalized_gene_data)

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

    # 4) Check for severe bias in the trait; remove biased demographic features if present
    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

    # 5) Final quality validation and save metadata
    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=f"Final check on {cohort} with {trait}."
    )

    # 6) If the linked data is usable, save it
    if is_usable:
        linked_data.to_csv(out_data_file)
else:
    # If no valid clinical data file is found, finalize metadata indicating trait unavailability
    is_usable = validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=True,
        is_trait_available=False,
        is_biased=True,  # Force a fallback so that it's flagged as unusable
        df=pd.DataFrame(),
        note=f"No trait data found for {cohort}, final metadata recorded."
    )
    # Per instructions, do not save a final linked data file when trait data is absent.