Journal
PLOS COMPUTATIONAL BIOLOGY
Volume 12, Issue 11, Pages -Publisher
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pcbi.1005212
Keywords
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Funding
- UK Medical Research Council [MR/ L001411/1]
- Wellcome Trust [090532/Z/09/Z]
- John Fell Oxford University Press (OUP) Research Fund
- Li Ka Shing Foundation via Oxford-Stanford Big Data in Human Health Seed Grant
- MRC [MR/L001411/1, MC_PC_14131, MR/M00919X/1] Funding Source: UKRI
- Medical Research Council [MR/M00919X/1, MC_PC_14131, 1523984, MR/L001411/1] Funding Source: researchfish
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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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