output/preprocess/Breast_Cancer/code/GSE225328.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Breast_Cancer"
cohort = "GSE225328"

# Input paths
in_trait_dir = "../DATA/GEO/Breast_Cancer"
in_cohort_dir = "../DATA/GEO/Breast_Cancer/GSE225328"

# Output paths
out_data_file = "./output/z2/preprocess/Breast_Cancer/GSE225328.csv"
out_gene_data_file = "./output/z2/preprocess/Breast_Cancer/gene_data/GSE225328.csv"
out_clinical_data_file = "./output/z2/preprocess/Breast_Cancer/clinical_data/GSE225328.csv"
json_path = "./output/z2/preprocess/Breast_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 based on background info ("Transcriptome profiling...")
is_gene_available = True

# 2) Variable availability from the provided Sample Characteristics Dictionary:
# {0: ['disease: early-stage luminal breast cancer'], 1: ['Sex: female']}
# - Trait (Breast_Cancer): only cancer cases -> constant -> not available
# - Age: not provided -> not available
# - Gender: only female -> constant -> not available
trait_row = None
age_row = None
gender_row = None

# 2.2) Data type conversion functions
def _after_colon(val):
    if val is None:
        return None
    s = str(val)
    parts = s.split(":", 1)
    s = parts[1] if len(parts) == 2 else parts[0]
    return s.strip()

def convert_trait(val):
    s = _after_colon(val)
    if s is None:
        return None
    s_low = s.lower()
    # Map to binary: 1 = breast cancer/case, 0 = control/normal
    cancer_keywords = ["cancer", "carcinoma", "tumor", "tumour", "malignant", "case", "luminal"]
    control_keywords = ["control", "normal", "healthy", "benign", "adjacent normal", "non-cancer"]
    if any(k in s_low for k in cancer_keywords):
        return 1
    if any(k in s_low for k in control_keywords):
        return 0
    return None

def convert_age(val):
    s = _after_colon(val)
    if s is None:
        return None
    s_low = s.lower()
    if s_low in {"na", "n/a", "none", "unknown", "not available", "not provided"}:
        return None
    m = re.search(r'(\d+(\.\d+)?)', s_low)
    if m:
        try:
            return float(m.group(1))
        except Exception:
            return None
    return None

def convert_gender(val):
    s = _after_colon(val)
    if s is None:
        return None
    s_low = s.lower()
    if s_low in {"female", "f", "woman", "women", "girl"}:
        return 0
    if s_low in {"male", "m", "man", "men", "boy"}:
        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 is skipped because trait_row is None (no usable clinical variability).