Speaker: Prof. Magnus Rattray, University of Manchester

Title:“Using Gaussian processes to model branching dynamics from single-cell data”

Abstract: In single-cell gene expressions experiments each cell may be at a different point in some dynamic process but the time information for each cell is not available. Pseudotime methods seek to infer time from these high-dimensional and noisy data points. We are developing methods for inference of pseudotime and branching dynamics in single-cell gene expression data. We use Gaussian processes, which allow for uncertainty in pseudotime inference and provide a natural prior over branching processes. To make inference tractable we have implemented methods using the GPflow/Tensorflow package, which allows for efficient inference through gradient-based optimisation of variational marginal likelihoods.

References: 

Sumon Ahmed, Magnus Rattray, Alexis Boukouvalas “GrandPrix: Scaling up the Bayesian GPLVM for single-cell data” Bioinformatics, bty533 (in press).

Alexis Boukouvalas, James Hensman, Magnus Rattray “BGP: Branched Gaussian processes for identifying gene-specific branching dynamics in single cell data” Genome biology 19 (1), 65