  Dimensionality reduction is crucial for analyzing large-scale single-cell
RNA-seq data. Gaussian Process Latent Variable Models (GPLVMs) offer an
interpretable dimensionality reduction method, but current scalable models lack
effectiveness in clustering cell types. We introduce an improved model, the
amortized stochastic variational Bayesian GPLVM (BGPLVM), tailored for
single-cell RNA-seq with specialized encoder, kernel, and likelihood designs.
This model matches the performance of the leading single-cell variational
inference (scVI) approach on synthetic and real-world COVID datasets and
effectively incorporates cell-cycle and batch information to reveal more
interpretable latent structures as we demonstrate on an innate immunity
dataset.
