Apr 5, 2016

Order under uncertainty: robust differential expression analysis using probabilistic models for pseudotime inference

BioRxiv : the Preprint Server for Biology
Kieran Campbell, Christopher Yau

Abstract

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. Preexisting methods for pseudotime ordering 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 compared and that probabilistic approaches provide greater robustness and measures of the temporal resolution that can be obtained from pseudotime inference.

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Mentioned in this Paper

Transcription, Genetic
Temporal Lobe/Cortex Disorder
Gene Expression
Cell Differentiation Process
Analysis
Approach
Experimentation
Computed (Procedure)
Protein Expression

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