output/preprocess/Bladder_Cancer/code/GSE201395.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bladder_Cancer"
cohort = "GSE201395"

# Input paths
in_trait_dir = "../DATA/GEO/Bladder_Cancer"
in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE201395"

# Output paths
out_data_file = "./output/z1/preprocess/Bladder_Cancer/GSE201395.csv"
out_gene_data_file = "./output/z1/preprocess/Bladder_Cancer/gene_data/GSE201395.csv"
out_clinical_data_file = "./output/z1/preprocess/Bladder_Cancer/clinical_data/GSE201395.csv"
json_path = "./output/z1/preprocess/Bladder_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
# Step 1: Determine gene expression availability based on background info
# Affymetrix HTA 2.0 is a gene expression microarray platform.
is_gene_available = True

# Step 2: Determine availability of trait, age, and gender
# The dataset consists of urothelial carcinoma cell lines only; no human subject-level trait variability.
# Sample characteristics only include cell line names; no age or gender data.
trait_row = None
age_row = None
gender_row = None

# Step 2.2: Define conversion functions

def _after_colon(value):
    if value is None:
        return None
    s = str(value)
    return s.split(":", 1)[-1].strip() if ":" in s else s.strip()

def convert_trait(value):
    """
    Map to binary bladder cancer status if inferable:
    - 1: cancer/urothelial carcinoma/tumor
    - 0: normal/control/non-tumor/benign
    - None: unknown
    """
    v = _after_colon(value)
    if not v:
        return None
    vl = v.lower()
    non_tumor_tokens = ["normal", "control", "healthy", "non-tumor", "benign", "adjacent normal", "no cancer"]
    tumor_tokens = ["cancer", "carcinoma", "tumor", "malignant", "urothelial", "bladder"]

    if any(t in vl for t in non_tumor_tokens):
        return 0
    if any(t in vl for t in tumor_tokens):
        return 1
    return None

def convert_age(value):
    v = _after_colon(value)
    if not v:
        return None
    vl = v.lower()
    if vl in {"na", "n/a", "unknown", "none", "missing"}:
        return None
    # Extract first number as age
    import re
    m = re.search(r"(\d+(\.\d+)?)", vl)
    if m:
        try:
            return float(m.group(1))
        except Exception:
            return None
    return None

def convert_gender(value):
    v = _after_colon(value)
    if not v:
        return None
    vl = v.lower()
    if vl in {"female", "f", "woman", "women"}:
        return 0
    if vl in {"male", "m", "man", "men"}:
        return 1
    return None

# Step 3: Initial filtering and save metadata
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
)

# Step 4: Clinical feature extraction (skip because trait_row is None)
# If trait_row were available, we would extract and save clinical features like this:
# selected_clinical_df = geo_select_clinical_features(
#     clinical_df=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
# )
# preview = preview_df(selected_clinical_df)
# selected_clinical_df.to_csv(out_clinical_data_file, index=True)