output/preprocess/Bladder_Cancer/code/GSE138118.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bladder_Cancer"
cohort = "GSE138118"

# Input paths
in_trait_dir = "../DATA/GEO/Bladder_Cancer"
in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE138118"

# Output paths
out_data_file = "./output/z1/preprocess/Bladder_Cancer/GSE138118.csv"
out_gene_data_file = "./output/z1/preprocess/Bladder_Cancer/gene_data/GSE138118.csv"
out_clinical_data_file = "./output/z1/preprocess/Bladder_Cancer/clinical_data/GSE138118.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 os
import re

# 1) Determine gene expression data availability
is_gene_available = True  # Expression profiling study of UCB vs healthy controls -> likely gene expression data

# 2) Variable availability and converters
# From the provided Sample Characteristics Dictionary:
# - trait (Bladder_Cancer): use row 0 ("stage at sample (histology after turbt): ...")
# - age: use row 2 ("age: ...")
# - gender: not available
trait_row = 0
age_row = 2
gender_row = None

def convert_trait(x):
    if x is None:
        return None
    s = str(x)
    # Extract value after colon if present
    if ':' in s:
        s = s.split(':', 1)[1]
    s = s.strip().lower()

    if s in ('', 'na', 'n/a', 'nan', 'none', 'unknown'):
        return None

    # Unknown/unenlightening entries
    if 'no histology' in s or 'no specim' in s or 'basingstoke' in s:
        return None

    # Controls
    if 'healthy' in s:
        return 0
    if s == 'neg' or s.startswith('neg'):
        return 0

    # Cases
    if 'g1' in s or 'g2' in s or 'g3' in s:
        return 1
    if 'pt' in s:  # e.g., pTa, pT1, pT2a
        return 1
    if any(tok in s for tok in ['carcinoma', 'tumour', 'tumor', 'ucb']):
        return 1

    return None

def convert_age(x):
    if x is None:
        return None
    s = str(x)
    # Extract value after colon if present
    if ':' in s:
        s = s.split(':', 1)[1]
    s = s.strip()
    if s in ('', 'na', 'n/a', 'nan', 'none', 'unknown'):
        return None
    m = re.search(r'[-+]?\d+\.?\d*', s)
    if not m:
        return None
    try:
        v = float(m.group())
        # Use integer age if it's integral
        return int(v) if abs(v - int(v)) < 1e-6 else v
    except Exception:
        return None

convert_gender = None  # Not available

# 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 (only if trait data is available)
if is_trait_available:
    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)
    print("Preview of selected clinical features:", preview)

    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_clinical_df.to_csv(out_clinical_data_file)

# 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 = True
print("requires_gene_mapping = True")

# Step 5: Gene Annotation
# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
gene_annotation = get_gene_annotation(soft_file)

# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
print("Gene annotation preview:")
print(preview_df(gene_annotation))

# Step 6: Gene Identifier Mapping
# Decide which columns to use for mapping
# Identifier column should match probe IDs like '16650001' in gene_data index
id_candidates = ['ID', 'probeset_id', 'ID_REF']
id_col = next((c for c in id_candidates if c in gene_annotation.columns), None)
if id_col is None:
    raise ValueError("No suitable probe ID column found in gene annotation.")

# Gene symbol text column candidates (to be parsed by extract_human_gene_symbols)
gene_candidates = ['gene_symbol', 'Gene Symbol', 'gene_assignment', 'mrna_assignment', 'symbol']
gene_col = next((c for c in gene_candidates if c in gene_annotation.columns), None)
if gene_col is None:
    raise ValueError("No suitable gene symbol/assignment column found in gene annotation.")

# Build mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col=id_col, gene_col=gene_col)

# Apply mapping to convert probe-level to gene-level expression
probe_data = gene_data  # preserve original probe-level data
gene_data = apply_gene_mapping(expression_df=probe_data, mapping_df=mapping_df)

# Step 7: Data Normalization and Linking
import os

# 1. Normalize gene symbols and save
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

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

# 4. Bias evaluation and removal of biased demographics
is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save cohort info
note = "INFO: Gender not available; trait derived from histology/grade field."
is_usable = validate_and_save_cohort_info(
    True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data, note
)

# 6. Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    unbiased_linked_data.to_csv(out_data_file)