{"doi":"10.1101/2025.11.13.688299","title":"Praxis-BGM: Clustering of Omics Data Using Semi-Supervised Transfer Learning for Gaussian Mixture Models via Natural-Gradient Variational Inference","abstract":"Recent advances in high-throughput technologies have enabled observational studies to collect high-dimensional omic data. However, such data, often measured on small sample sizes, pose challenges to model-based clustering approaches such as Gaussian Mixture Models. Existing methods often fail to generalize due to model instability under complex mixture patterns. To overcome these limitations, we propose a natural-gradient variational inference framework for Gaussian mixture models named Praxis-BGM that incorporates informative priors-cluster-specific means, covariances, and structural connectivity-from large-scale reference data with known cluster or class labels to enable semi-supervised transfer learning. We derive natural-gradient updates that integrate prior knowledge, leveraging the Variational Online Newton algorithm. We also perform feature selection for clustering using Bayes Factors. Implemented using the JAX library for accelerator-oriented computation, Praxis-BGM is computationally efficient and scalable. We demonstrate the effectiveness of Praxis-BGM in extensive simulations and with two real-world applications: bulk transcriptomic datasets for breast cancer subtyping (the Cancer Genome Atlas Breast Invasive Carcinoma and the Molecular Taxonomy of Breast Cancer International Consortium), and transferring cell-type annotations between single-cell transcriptomic datasets produced by different single-cell RNA-seq technologies in a human pancreas study. Even when priors are partially mismatched with the target data, Praxis-BGM enhances semi-supervised clustering accuracy and biological interpretability.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2025,"id":581106,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9556,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":848520,"name":"Jesse A. Goodrich","orcid":"0000-0001-6615-0472","position":1,"is_corresponding":false},{"id":720,"name":"David V. Conti","orcid":"0000-0002-2941-7833","position":2,"is_corresponding":false},{"id":1213638,"name":"Qiran Jia","orcid":"0000-0002-0790-5967","position":0,"is_corresponding":true}],"reference_count":31,"raw_metadata":null,"created_at":"2026-07-19T02:58:47.357782Z","pmid":"41292835","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}