output/preprocess/Bladder_Cancer/code/GSE185264.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bladder_Cancer"
cohort = "GSE185264"

# Input paths
in_trait_dir = "../DATA/GEO/Bladder_Cancer"
in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE185264"

# Output paths
out_data_file = "./output/z1/preprocess/Bladder_Cancer/GSE185264.csv"
out_gene_data_file = "./output/z1/preprocess/Bladder_Cancer/gene_data/GSE185264.csv"
out_clinical_data_file = "./output/z1/preprocess/Bladder_Cancer/clinical_data/GSE185264.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
import re

# 1. Gene Expression Data Availability
is_gene_available = True  # NanoString nCounter RNA profiling indicates gene expression data

# 2. Variable Availability and Data Type Conversion

# 2.1 Data Availability (based on provided Sample Characteristics)
trait_row = None       # Trait "Bladder_Cancer" is constant (all cases), thus not available for association
age_row = None         # No age field observed
gender_row = 7         # 'Sex: M' / 'Sex: F'

# 2.2 Data Type Conversion

def _extract_value(cell):
    if cell is None:
        return None
    # Expecting "field: value"
    parts = str(cell).split(":", 1)
    val = parts[1] if len(parts) > 1 else parts[0]
    val = val.strip()
    if val in {"NA", "NaN", ".", "", "None", "nan"}:
        return None
    return val

def convert_trait(cell):
    """
    Binary: presence of bladder cancer (1) vs normal/control (0).
    Heuristic mapping if applied elsewhere:
      - map values containing 'bladder', 'cancer', 'tumor', 'nmibc' -> 1
      - map 'normal', 'control', 'adjacent normal', 'observation' (if used as control) -> 0
    Unknown -> None
    """
    val = _extract_value(cell)
    if val is None:
        return None
    s = val.lower()
    # Positive disease indicators
    if any(k in s for k in ["bladder", "cancer", "tumor", "nmibc", "urothelial"]):
        return 1
    # Control indicators
    if any(k in s for k in ["normal", "control", "healthy", "adjacent normal", "benign"]):
        return 0
    return None

def convert_age(cell):
    """
    Continuous: extract numeric age (years).
    """
    val = _extract_value(cell)
    if val is None:
        return None
    # extract first number (integer or float)
    m = re.search(r"(\d+(\.\d+)?)", val)
    if not m:
        return None
    try:
        return float(m.group(1))
    except Exception:
        return None

def convert_gender(cell):
    """
    Binary: female=0, male=1.
    """
    val = _extract_value(cell)
    if val is None:
        return None
    s = val.strip().lower()
    if s in {"m", "male"}:
        return 1
    if s in {"f", "female"}:
        return 0
    return None

# 3. Save Metadata (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 (skip because trait_row is None)
if trait_row is not None:
    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 if age_row is not None else None,
        gender_row=gender_row,
        convert_gender=convert_gender if gender_row is not None else None
    )
    preview = preview_df(selected_clinical_df, n=5)
    selected_clinical_df.to_csv(out_clinical_data_file, index=True)

# Step 3: Gene Data Extraction
# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
gene_data = get_genetic_data(matrix_file)

# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
print(gene_data.index[:20])

# Step 4: Gene Identifier Review
requires_gene_mapping = False
print(f"requires_gene_mapping = {requires_gene_mapping}")

# Step 5: Data Normalization and Linking
# 1. Normalize gene symbols and save gene expression data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# 2-6. Proceed conditionally based on trait availability
if ('trait_row' in locals()) and (trait_row is not None):
    # Ensure clinical features are available; create if missing
    if 'selected_clinical_data' not in locals():
        selected_clinical_data = 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 if age_row is not None else None,
            gender_row=gender_row,
            convert_gender=convert_gender if gender_row is not None else None
        )

    # Link clinical and genetic data
    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)

    # 3. Handle missing values
    linked_data = handle_missing_values(linked_data, trait)

    # 4. Bias checks
    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

    # 5. Final validation and save cohort info
    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=is_trait_biased,
        df=unbiased_linked_data,
        note="INFO: Trait and clinical features linked; proceeded with full preprocessing."
    )

    # 6. Save linked data if usable
    if is_usable:
        unbiased_linked_data.to_csv(out_data_file)

else:
    # Trait is not available; skip linking and downstream steps
    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=False,
        df=normalized_gene_data.T,
        note="INFO: Trait not available in sample characteristics; only gene data normalized and saved."
    )
    # Do not save linked data when trait is unavailable