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PLoS Comput Biol. 2016 Nov 21;12(11):e1005212. doi: 10.1371/journal.pcbi.1005212. eCollection 2016 Nov.

Order Under Uncertainty: Robust Differential Expression Analysis Using Probabilistic Models for Pseudotime Inference.

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Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom.
Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom.
Department of Statistics, University of Oxford, Oxford, United Kingdom.


Single cell gene expression profiling can be used to quantify transcriptional dynamics in temporal processes, such as cell differentiation, using computational methods to label each cell with a 'pseudotime' where true time series experimentation is too difficult to perform. However, owing to the high variability in gene expression between individual cells, there is an inherent uncertainty in the precise temporal ordering of the cells. Pre-existing methods for pseudotime estimation have predominantly given point estimates precluding a rigorous analysis of the implications of uncertainty. We use probabilistic modelling techniques to quantify pseudotime uncertainty and propagate this into downstream differential expression analysis. We demonstrate that reliance on a point estimate of pseudotime can lead to inflated false discovery rates and that probabilistic approaches provide greater robustness and measures of the temporal resolution that can be obtained from pseudotime inference.

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