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Clustering of Omic Data Using Semi-Supervised Transfer Learning for Gaussian Mixture Models via Natural-Gradient

Qiran Jia1, Jesse A Goodrich1, David V Conti1

  • 1Division of Biostatistics and Health Data Science, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California.

Biorxiv : the Preprint Server for Biology
|November 26, 2025
PubMed
Summary
This summary is machine-generated.

Praxis-BGM uses natural-gradient variational inference to improve Gaussian Mixture Models for high-dimensional omic data. This framework enhances semi-supervised clustering accuracy and biological interpretability, even with mismatched prior data.

Keywords:
Bayesian ClusteringGaussian Mixture ModelOmicsStatistical Transfer LearningVariational Inference

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Machine Learning

Background:

  • High-throughput technologies generate high-dimensional omic data.
  • Small sample sizes challenge model-based clustering like Gaussian Mixture Models.
  • Existing methods struggle with generalization and model instability.

Purpose of the Study:

  • To develop a novel framework, Praxis-BGM, for robust clustering of high-dimensional omic data.
  • To enable semi-supervised transfer learning by incorporating informative priors.
  • To improve model generalization and biological interpretability.

Main Methods:

  • Developed a natural-gradient variational inference framework (Praxis-BGM) for Gaussian Mixture Models.
  • Incorporated cluster-specific means, covariances, and structural connectivity from reference data as informative priors.
  • Utilized the Variational Online Newton algorithm for natural-gradient updates and Bayes Factors for feature selection.
  • Implemented using JAX for efficient, scalable computation.

Main Results:

  • Praxis-BGM demonstrates computational efficiency and scalability.
  • Successfully applied to breast cancer subtyping using bulk transcriptomic data.
  • Effectively transferred cell-type annotations between different single-cell RNA-seq technologies in a human pancreas study.
  • Enhanced semi-supervised clustering accuracy and biological interpretability, even with partially mismatched priors.

Conclusions:

  • Praxis-BGM offers a powerful and efficient approach for clustering high-dimensional omic data.
  • The framework effectively leverages prior knowledge for semi-supervised transfer learning.
  • Praxis-BGM improves biological interpretability in omics data analysis.