Dec 3, 2017

GrandPrix: Scaling up the Bayesian GPLVM for single-cell data

BioRxiv : the Preprint Server for Biology
Sumon AhmedAlexis Boukouvalas

Abstract

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. 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 applied 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. Further, we demonstrate the flexibility of our approach by extending the model to higher-dimensional latent spaces that can be used to simultaneously infer pseudotime and other structure such as branching. Thus, the model has the capability of producing meaningful biological insights about cell ordering as well as cell fate regulation. Software available at github....Continue Reading

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Mentioned in this Paper

Computer Software
Real-Time Polymerase Chain Reaction
Lipid Droplet
Regulation of Biological Process
Anatomical Space Structure
Structure
Toxic Epidermal Necrolysis
Cell Fate Control
Branching (Qualifier Value)

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