Praxis-BGM: Clustering of Omics Data Using Semi-Supervised Transfer Learning for Gaussian Mixture Models via Natural-Gradient Variational Inference

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ID: 317624
2026
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Abstract
Abstract Motivation High-dimensional omics data are typically measured on limited sample sizes, which challenges model-based clustering methods such as Gaussian mixture models (GMMs), often leading to instability and poor generalization under complex mixture structures. To address these limitations, we developed Praxis-BGM, a natural-gradient variational inference framework for GMMs. Praxis-BGM enables semi-supervised transfer learning by incorporating an informative prior GMM estimated from large-scale reference data with robust cluster structures. The prior model can encode cluster-specific means, covariance structures, and structural connectivity patterns, and is updated using the target data with variational inference to improve clustering in small-sample settings. Results Using the Variational Online Newton (VON) algorithm, we derived natural-gradient updates for the standard parameters of GMMs. Implemented in the Python library JAX for accelerator-oriented computation, Praxis-BGM is computationally efficient and scalable. Across extensive simulations and two real-world applications—breast cancer bulk transcriptomics for subtype recovery and single-cell transcriptomics for cross-platform cell-type label transfer—Praxis-BGM improves posterior clustering performance, stability, and biological interpretability, even when priors are partially mismatched. Availability and Implementation Praxis-BGM is freely available at https://github.com/ContiLab-usc/Praxis-BGM, and an archival version is available on Zenodo at https://doi.org/10.5281/zenodo.19657680. Supplementary Information Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7165029261 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Qiran Jia, Jesse A. Goodrich, David V. Conti
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag395
URL
https://doi.org/10.1093/bioinformatics/btag395
Keywords Keywords not found

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