  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.
