output/preprocess/Bladder_Cancer/code/GSE145261.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bladder_Cancer"
cohort = "GSE145261"

# Input paths
in_trait_dir = "../DATA/GEO/Bladder_Cancer"
in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE145261"

# Output paths
out_data_file = "./output/z1/preprocess/Bladder_Cancer/GSE145261.csv"
out_gene_data_file = "./output/z1/preprocess/Bladder_Cancer/gene_data/GSE145261.csv"
out_clinical_data_file = "./output/z1/preprocess/Bladder_Cancer/clinical_data/GSE145261.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  # Comprehensive molecular analysis; likely includes gene expression (not just miRNA/methylation)

# 2) Variable availability and conversion functions

# Keys inferred from Sample Characteristics Dictionary:
# 0: subject age
# 1: subject gender
# 2: tissue (constant: bladder)
# 3: tissue type (constant here: small cell carcinoma (SCC))
trait_row = None   # All samples are bladder cancer (SCC); no variation for the trait "Bladder_Cancer"
age_row = 0
gender_row = 1

def _extract_value(cell):
    if cell is None:
        return None
    # Typical format "key: value"
    parts = str(cell).split(":", 1)
    val = parts[1] if len(parts) > 1 else parts[0]
    return val.strip()

def convert_trait(cell):
    """
    Map to binary: 1 = bladder cancer case; 0 = non-cancer/normal.
    This function is defined for completeness but not used because trait_row is None in this cohort.
    """
    val = _extract_value(cell)
    if val is None:
        return None
    v = val.lower()
    if any(k in v for k in ["na", "unknown", "not available", "n/a", "none", ""]):
        return None
    # Heuristics for normal/control
    if any(k in v for k in ["normal", "adjacent normal", "benign", "healthy", "control", "non-cancer"]):
        return 0
    # Heuristics for bladder cancer case
    cancer_terms = ["cancer", "carcinoma", "tumor", "tumour", "small cell", "scc", "urothelial", "uc", "bladder cancer"]
    if any(k in v for k in cancer_terms):
        return 1
    return None  # if unsure

def convert_age(cell):
    val = _extract_value(cell)
    if val is None:
        return None
    v = val.lower()
    if any(k in v for k in ["na", "unknown", "not available", "n/a", "none", ""]):
        return None
    # Extract first integer/float occurrence
    m = re.search(r"(\d+(\.\d+)?)", v)
    if not m:
        return None
    try:
        age = float(m.group(1))
        # Basic human age sanity check
        if 0 < age < 120:
            return age
    except Exception:
        pass
    return None

def convert_gender(cell):
    val = _extract_value(cell)
    if val is None:
        return None
    v = val.strip().lower()
    if any(k in v for k in ["na", "unknown", "not available", "n/a", "none", ""]):
        return None
    if v in ["female", "f", "woman", "women"]:
        return 0
    if v in ["male", "m", "man", "men"]:
        return 1
    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 becomes available in future, uncomment the following:
# if trait_row is not None:
#     selected = 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_df(selected, n=5)
#     os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
#     selected.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
# The identifiers like 'ILMN_1343291' are Illumina probe IDs, not human gene symbols.
requires_gene_mapping = True
print(f"requires_gene_mapping = {requires_gene_mapping}")

# 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
# Determine columns for probe IDs and gene symbols based on the annotation preview
probe_col = 'ID'
gene_symbol_col = 'Symbol'

# 2. Build the mapping dataframe (ID -> Gene Symbol)
mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)

# 3. Apply mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)

# Step 7: Data Normalization and Linking
import os

# 1) Normalize gene symbols and save gene-level data
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)

# Since trait was not available in this cohort (trait_row=None in Step 2), skip linking and downstream steps.
# 5) Final validation and save cohort info (no linked data due to missing trait)
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,  # placeholder; not used when trait unavailable
    df=normalized_gene_data,
    note="INFO: Trait 'Bladder_Cancer' not recorded/variable in this series (all samples are SCC); "
         "clinical trait unavailable, so linked data not produced. Gene-level expression saved."
)

# 6) Do not save linked data because trait is unavailable and dataset is not usable for association analysis.