output/preprocess/Adrenocortical_Cancer/code/GSE143383.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Adrenocortical_Cancer"
cohort = "GSE143383"

# Input paths
in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE143383"

# Output paths
out_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/GSE143383.csv"
out_gene_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/gene_data/GSE143383.csv"
out_clinical_data_file = "./output/z1/preprocess/Adrenocortical_Cancer/clinical_data/GSE143383.csv"
json_path = "./output/z1/preprocess/Adrenocortical_Cancer/cohort_info.json"


# Step 1: Initial Data Loading
from tools.preprocess import *
# 1. Identify the paths to the SOFT file and the matrix file
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# 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("Sample Characteristics Dictionary:")
print(sample_characteristics_dict)

# Step 2: Dataset Analysis and Clinical Feature Extraction
import re

# 1) Gene expression data availability
is_gene_available = True  # Affymetrix PrimeView gene expression profiling (not miRNA/methylation)

# 2) Variable availability based on Sample Characteristics Dictionary
# Provided dictionary indicates only gender info at key 0 with multiple values.
trait_row = None   # ACC tumor-only cohort; trait is constant/not provided explicitly => not available
age_row = None     # No age field present
gender_row = 0     # gender: F/M/unknown

# 2.2) Conversion functions
def _after_colon(val):
    if val is None:
        return None
    s = str(val)
    parts = s.split(":", 1)
    return parts[1].strip() if len(parts) == 2 else s.strip()

def convert_trait(v):
    # Binary: 1 = Adrenocortical_Cancer (ACC/tumor), 0 = control/normal/benign
    x = _after_colon(v)
    if x is None or x == "":
        return None
    xl = x.lower()
    pos_terms = [
        "adrenocortical carcinoma", "acc", "carcinoma", "tumor", "metastatic",
        "adrenocortical cancer"
    ]
    neg_terms = [
        "normal", "control", "benign", "adjacent normal", "healthy", "adenoma",
        "hyperplasia"
    ]
    if any(term in xl for term in pos_terms):
        return 1
    if any(term in xl for term in neg_terms):
        return 0
    return None

def convert_age(v):
    # Continuous: extract first numeric age in years
    x = _after_colon(v)
    if x is None or x == "":
        return None
    m = re.search(r"(\d+(\.\d+)?)", x)
    if not m:
        return None
    try:
        age = float(m.group(1))
        if 0 <= age <= 120:
            return age
    except Exception:
        pass
    return None

def convert_gender(v):
    # Binary: female=0, male=1, unknown=None
    x = _after_colon(v)
    if x is None or x == "":
        return None
    xl = x.strip().lower()
    if xl in {"f", "female", "woman", "women"}:
        return 0
    if xl in {"m", "male", "man", "men"}:
        return 1
    if xl in {"u", "unk", "unknown", "na", "n/a", "not available"}:
        return None
    # Heuristics
    if "female" in xl:
        return 0
    if "male" in xl:
        return 1
    return None

# 3) Save metadata with initial filtering
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: skipped because trait_row is None (no usable trait variable)