Abstract

We demonstrate how to develop and apply Gaussian Process models for dimensionality reduction and inference of branching dynamics in single cell transcriptomic data. We will discuss two models:

GrandPrix: an efficient implementation of the Gaussian process latent variable model which allows scaling up the GP approach to modern single-cell datasets. We apply our method on microarray, nCounter, RNA -seq, qPCR and droplet-based datasets from different organisms. The model converges an order of magnitude faster compared to existing methods whilst achieving similar levels of estimation accuracy.

The branching Gaussian process (BGP): a non-parametric model that is able to identify branching dynamics for individual genes and provide an estimate of branching times for each gene with an associated credible region. We demonstrate the effectiveness of our method on simulated data, a single-cell RNA -seq haematopoiesis study and mouse embryonic stem cells generated using droplet barcoding. The method is robust to high levels of technical variation and dropout, which are common in single-cell data.