Gaussian process models of pseudotime and branching in single-cell gene expression experiments

02 Feb 2018, 13:00 - 14:00

Michael Sterling 057

02/02/2018 01:00 PM 02/02/2018 02:00 PM Europe/London Gaussian process models of pseudotime and branching in single-cell gene expression experiments Gaussian process models of pseudotime and branching in single-cell gene expression experiments Michael Sterling 057 Join our mailing list

Speaker: Magnus Rattray, Manchester

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” bioRxiv 227843; doi: https://doi.org/10.1101/227843

Alexis Boukouvalas, James Hensman, Magnus Rattray "BGP: Branched Gaussian processes for identifying gene-specific branching dynamics in single cell data” bioRxiv 166868; doi: https://doi.org/10.1101/166868

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