p1/preprocess/Cardiovascular_Disease/code/GSE273225.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cardiovascular_Disease"
cohort = "GSE273225"

# Input paths
in_trait_dir = "../DATA/GEO/Cardiovascular_Disease"
in_cohort_dir = "../DATA/GEO/Cardiovascular_Disease/GSE273225"

# Output paths
out_data_file = "./output/preprocess/1/Cardiovascular_Disease/GSE273225.csv"
out_gene_data_file = "./output/preprocess/1/Cardiovascular_Disease/gene_data/GSE273225.csv"
out_clinical_data_file = "./output/preprocess/1/Cardiovascular_Disease/clinical_data/GSE273225.csv"
json_path = "./output/preprocess/1/Cardiovascular_Disease/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. Gene Expression Data Availability
is_gene_available = True  # The dataset uses nCounter digital gene expression (not purely miRNA or methylation).

# 2. Variable Availability and Data Type Conversion

# 2.1 Data Availability
# From inspecting the sample characteristics, there's no indication of "Cardiovascular_Disease" or similar trait info.
# Hence, trait is not available in this dataset:
trait_row = None

# The 'donor age (y)' entries are at row 3, and they have multiple unique values:
age_row = 3

# The 'donor sex' entries at row 4 have both "male" and "female" values, so gender is available:
gender_row = 4

# 2.2 Data Type Conversion
def convert_trait(x: str):
    """
    No trait data is available, so always return None.
    """
    return None

def convert_age(x: str):
    """
    Convert 'donor age (y): 63' to a continuous numeric value.
    Parse out the number after the colon, if available.
    """
    try:
        # split by ':' and take the last part
        val_str = x.split(':')[-1].strip()
        return float(val_str)
    except:
        return None

def convert_gender(x: str):
    """
    Convert 'donor sex: male' -> 1, 'donor sex: female' -> 0.
    Return None if parsing fails.
    """
    try:
        val_str = x.split(':')[-1].strip().lower()
        if val_str == 'female':
            return 0
        elif val_str == 'male':
            return 1
        else:
            return None
    except:
        return None

# 3. Save Metadata with initial filtering
is_trait_available = (trait_row is not None)
is_final = False  # initial filtering
validate_and_save_cohort_info(
    is_final=is_final,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=is_trait_available
)

# 4. Clinical Feature Extraction
# Since trait_row is None, we skip this step (no clinical data extraction).
# STEP3
# Attempt to read gene expression data; if the library function yields an empty DataFrame,
# try re-reading without ignoring lines that start with '!' (because sometimes GEO data may
# place actual expression rows under lines that begin with '!').

gene_data = get_genetic_data(matrix_file)
if gene_data.empty:
    print("[WARNING] The gene_data is empty. Attempting alternative loading without treating '!' as comments.")
    import gzip

    # Locate the marker line first
    skip_rows = 0
    with gzip.open(matrix_file, 'rt') as file:
        for i, line in enumerate(file):
            if "!series_matrix_table_begin" in line:
                skip_rows = i + 1
                break

    # Read the data again, this time not treating '!' as comment
    gene_data = pd.read_csv(
        matrix_file,
        compression="gzip",
        skiprows=skip_rows,
        delimiter="\t",
        on_bad_lines="skip"
    )
    gene_data = gene_data.rename(columns={"ID_REF": "ID"}).astype({"ID": "str"})
    gene_data.set_index("ID", inplace=True)

# Print the first 20 row IDs to confirm data structure
print(gene_data.index[:20])
# Based on the given identifiers (e.g., ABCB1, ABCF1, ABL1), these appear to be standard human gene symbols.
# Therefore, no mapping to gene symbols is required.
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.