Preprocessing choices affect RNA velocity results for droplet scRNA-seq data.

Min Li,Rob Patro,Charlotte Soneson,Michael B Stadler,Avi Srivastava

doi:10.1371/journal.pcbi.1008585

Abstract

Experimental single-cell approaches are becoming widely used for many purposes, including investigation of the dynamic behaviour of developing biological systems. Consequently, a large number of computational methods for extracting dynamic information from such data have been developed. One example is RNA velocity analysis, in which spliced and unspliced RNA abundances are jointly modeled in order to infer a ‘direction of change’ and thereby a future state for each cell in the gene expression space. Naturally, the accuracy and interpretability of the inferred RNA velocities depend crucially on the correctness of the estimated abundances. Here, we systematically compare five widely used quantification tools, in total yielding thirteen different quantification approaches, in terms of their estimates of spliced and unspliced RNA abundances in five experimental droplet scRNA-seq data sets. We show that there are substantial differences between the quantifications obtained from different tools, and identify typical genes for which such discrepancies are observed. We further show that these abundance differences propagate to the downstream analysis, and can have a large effect on estimated velocities as well as the biological interpretation. Our results highlight that abundance quantification is a crucial aspect of the RNA velocity analysis workflow, and that both the definition of the genomic features of interest and the quantification algorithm itself require careful consideration.

Highlights

Single-cell RNA-seq enables high-throughput profiling of gene expression on a transcriptome-wide scale in individual cells [1,2,3,4,5,6]
Applied to single-cell RNA-seq data, RNA velocity analysis provides a way to estimate the rate of change of the gene expression levels in individual cells
This, in turn, enables estimation of what the gene expression profile of each cell will look like a short time into the future and lets researchers infer likely developmental relationships among different types of cells in a tissue

Summary

Introduction

Single-cell RNA-seq (scRNA-seq) enables high-throughput profiling of gene expression on a transcriptome-wide scale in individual cells [1,2,3,4,5,6]. The increased resolution compared to bulk RNA-seq, where only average expression profiles across populations of cells are obtained, provides vastly improved potential to study a variety of biological questions. One such question concerns the dynamics of biological systems, reflected in, e.g., cellular differentiation and development [7]. Many computational methods for trajectory inference from scRNA-seq have been presented in the literature (reviewed by [8]) These methods typically use the similarity of the gene expression profiles between cells to construct a (possibly branching) path through the observed set of cells, representing the trajectory of interest. Projecting the cells onto this path provides an ordering of the cells by so called pseudotime

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