SynOmicsBench

SynOmicsBench is a unified benchmarking framework for synthetic data generation (SDG) for clinical transcriptomic cancer cohorts.

Achieving a trade-off between biological utility and patient privacy is critical for secure data sharing when applying transcriptomic clinical datasets to artificial intelligence in precision oncology. Here, we present the SynOmicsBench framework. SynOmicsBench combines standardized preprocessing with multidimensional evaluation, prioritizing downstream biological validation alongside statistical fidelity and attack-based privacy assessment. This work provides a reproducible decision-support tool for method selection and promotes biologically informed, privacy-aware adoption of synthetic data in precision oncology.

Installation

pip install synomicsbench

Python 3.12+ is required.

Quick Start

import pandas as pd
from synomicsbench.processing.preprocessing import DataProcessor
from synomicsbench.processing.metadata import MetaData
from synomicsbench.synthesizer.GaussianCopulasynthesizer import GaussianCopulasynthesizer
from synomicsbench.metrics.fidelity.UnivariateSimilarity import UnivariateSimilarity

# 1. Preprocess
data = pd.read_csv("clinical_transcriptomic_data.csv")
data = DataProcessor.remove_unknown_entities(data, id_column="Patient_ID")
data = DataProcessor.remove_duplications(data, axis=0).reset_index(drop=True)
data = DataProcessor.mice_imputation(data, iterations=10, n_estimators=100)

# 2. Metadata
metadata = MetaData.get_metadata(
    data=data,
    ordinal_features=["Mstage", "Tx_Start_ECOG", "numPriorTherapies"],
    threshold_unique_values=10,
)

# 3. Generate synthetic data
synth = GaussianCopulasynthesizer(output_path="./results", metadata=metadata)
synthetic_data = synth.generate(
    data=data,
    seed=42,
    n_samples=data.shape[0],
    output_filename="synthetic_data.csv",
)

# 4. Evaluate
evaluator = UnivariateSimilarity(output_dir="./results/evaluation")
score = evaluator.get_univariate_score(
    original_data=data,
    synthetic_data=synthetic_data,
    metadata=metadata,
    save=True,
)
print(f"Univariate Fidelity Score: {score:.4f}")

Documentation

Full documentation, API reference, and benchmarking results: https://trinhthechuong.github.io/SynOmicsBench/

Citation

If you use SynOmicsBench in your research, please cite:

Trinh, T. C., Woillard, J. B., Uguzzoni, G., & Battail, C. (2024). A unified benchmark of synthetic data generation for clinical and transcriptomic cancer data. (Manuscript in preparation)

License

MIT License