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. 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.
Highlights
The emergence of high-throughput single cell genomics as a tool for the precision study of biological systems [1,2,3,4] has given rise to a variety of novel computational and statistical modelling challenges [5, 6]
In the absence of the ability to conduct true time series experiments, pseudotime algorithms exploit the asynchronous cellular nature of these systems to mathematically assign a “pseudotime” to each cell based on its molecular profile allowing the cells to be aligned and the sequence of gene activation events retrospectively inferred
One particular area of interest has been the study of transcriptional dynamics in temporal processes, such as cell differentiation or proliferation [7, 8], in order to understand the coordinated changes in transcription programming that underlie these processes
Summary
The emergence of high-throughput single cell genomics as a tool for the precision study of biological systems [1,2,3,4] has given rise to a variety of novel computational and statistical modelling challenges [5, 6]. Investigators have adopted computational methods to identify temporal signatures and trends from unordered genomic profiles of single cells, a process known as pseudotemporal ordering [9,10,11,12,13,14,15,16,17,18,19]. Pseudotemporal ordering of whole transcriptome profiles of single cells with unsupervised computational methods has an advantage over flow cytometry-based assays in that it does not rely on a priori knowledge of marker genes The principle underlying these methods is that each single cell RNA sequencing experiment constitutes a time series in which each cell represents a distinct time point along a continuum representing the underlying degree of temporal progress (Fig 1A). The pseudotimes could be used to identify genes that are differentially expressed across pseudotime (Fig 1D) providing insight into the evolution of transcription programming
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