GrandPrix: scaling up the Bayesian GPLVM for single-cell data.

ABSTRACT:

Motivation

The Gaussian Process Latent Variable Model (GPLVM) is a popular approach for dimensionality reduction of single-cell data and has been used for pseudotime estimation with capture time information. However, current implementations are computationally intensive and will not scale up to modern droplet-based single-cell datasets which routinely profile many tens of thousands of cells.

Results

We provide an efficient implementation which allows scaling up this approach to modern single-cell datasets. We also generalize the application of pseudotime inference to cases where there are other sources of variation such as branching dynamics. We apply our method on microarray, nCounter, RNA-seq, qPCR and droplet-based datasets from different organisms. The model converges an

SUBMITTER: Ahmed S 

PROVIDER: S-EPMC6298059 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature