Single-cell RNA sequencing (scRNA-seq) has provided impressive temporal resolution to our understanding of continuous biological processes such as cell differentiation ( Wagner et al., 2016 Tanay and Regev, 2017). We show that accounting for the true amount of information recovered can produce biologically meaningful measurements of splicing in single cells. This gives the appearance of binary splicing outcomes, even when the underlying reality is consistent with more than one isoform per cell. Our simulations show that low gene expression and low capture efficiency distort the observed distribution of isoforms. We analyze alternative splicing in human and mouse single-cell RNA-seq datasets, and model them with a probabilistic simulator.

Here, we show that this pattern arises almost entirely from technical limitations.

Previous studies led to the surprising observation that alternative splicing among single cells is highly variable and follows a bimodal pattern: a given cell consistently produces either one or the other isoform for a particular splicing choice, with few cells producing both isoforms. Single-cell RNA sequencing provides powerful insight into the factors that determine each cellвЂ™s unique identity.

J.u.l.i.a. among the stars artifacts