The VampPrior Mixture Model
<|reference_start|>The VampPrior Mixture Model: Current clustering priors for deep latent variable models (DLVMs) require defining the number of clusters a-priori and are susceptible to poor initializations. Addressing these deficiencies could greatly benefit deep learning-based scRNA-seq analysis by performing integration and clustering simultaneously. We adapt the VampPrior (Tomczak & Welling, 2018) into a Dirichlet process Gaussian mixture model, resulting in the VampPrior Mixture Model (VMM), a novel prior for DLVMs. We propose an inference procedure that alternates between variational inference and Empirical Bayes to cleanly distinguish variational and prior parameters. Using the VMM in a Variational Autoencoder attains highly competitive clustering performance on benchmark datasets. Augmenting scVI (Lopez et al., 2018), a popular scRNA-seq integration method, with the VMM significantly improves its performance and automatically arranges cells into biologically meaningful clusters.<|reference_end|>
@article{stirn2024the,
    title={The VampPrior Mixture Model},
    author={Andrew Stirn and David A. Knowles},
    journal={arXiv preprint arXiv:2402.04412},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.04412},
    primaryClass={cs.LG cs.AI stat.ML}
}