p1/preprocess/Cardiovascular_Disease/code/GSE283522.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cardiovascular_Disease"
cohort = "GSE283522"

# Input paths
in_trait_dir = "../DATA/GEO/Cardiovascular_Disease"
in_cohort_dir = "../DATA/GEO/Cardiovascular_Disease/GSE283522"

# Output paths
out_data_file = "./output/preprocess/1/Cardiovascular_Disease/GSE283522.csv"
out_gene_data_file = "./output/preprocess/1/Cardiovascular_Disease/gene_data/GSE283522.csv"
out_clinical_data_file = "./output/preprocess/1/Cardiovascular_Disease/clinical_data/GSE283522.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
# Based on the series summary, this dataset includes RNA-seq data, so set is_gene_available to True.
is_gene_available = True

# 2) Variable Availability and Data Type Conversion

# 2.1) Data Availability
# There's no key with clear "Cardiovascular Disease" info, so trait_row is None.
trait_row = None

# For age, key 2 contains multiple distinct age ranges, so age_row = 2.
age_row = 2

# For gender, key 5 contains 'female' and 'male', so gender_row = 5.
gender_row = 5

# 2.2) Data Type Conversion
def convert_trait(value: str):
    """
    Since there's no actual cardiovascular disease data here,
    we return None for all inputs.
    """
    return None

def convert_age(value: str):
    """
    Extract numeric age by parsing the string after 'age:'.
    If it's a range like '55 - 59', we take the midpoint.
    If it's 'not applicable' or invalid, return None.
    """
    # Split on colon and strip
    content = value.split(":", 1)[-1].strip().lower()
    if "not applicable" in content or "missing" in content:
        return None
    
    # If it's in the form 'XX - YY'
    if "-" in content:
        parts = content.split("-")
        try:
            low = int(parts[0])
            high = int(parts[1])
            return (low + high) / 2
        except ValueError:
            return None
    else:
        # If a single number is found
        try:
            return float(content)
        except ValueError:
            return None

def convert_gender(value: str):
    """
    Convert 'female' to 0, 'male' to 1, otherwise None.
    """
    # Split on colon and strip
    content = value.split(":", 1)[-1].strip().lower()
    if "female" in content:
        return 0
    elif "male" in content:
        return 1
    else:
        return None

# 3) Save Metadata
# Trait availability depends on whether trait_row is None.
is_trait_available = (trait_row is not None)

# Because this is not the final step yet, we set is_final=False.
# We only perform initial filtering and record the dataset's availability.
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
# We skip this because trait_row is None, indicating the clinical trait data is not available.
# 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])