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As single-cell RNA sequencing (scRNA-seq) technologies have rapidly developed, so have analysis methods. Many methods have been tested, developed, and validated using simulated datasets. Unfortunately, current simulations are often poorly documented, their similarity to real data is not demonstrated, or reproducible code is not available. Here, we present the Splatter Bioconductor package for simple, reproducible, and well-documented simulation of scRNA-seq data. Splatter provides an interface to multiple simulation methods including Splat, our own simulation, based on a gamma-Poisson distribution. Splat can simulate single populations of cells, populations with multiple cell types, or differentiation paths.
As scRNA-seq data have become available there has been a rapid development of new bioinformatics tools attempting to unlock its potential. Currently there are at least 120 software packages that have been designed specifically for the analysis of scRNA-seq data, the majority of which have been published in peer-reviewed journals or as preprints . The focus of these tools is often different from those designed for the analysis of a bulk RNA-seq experiment. In a bulk experiment, the groups of samples are known and a common task is to test for genes that are differentially expressed (DE) between two or more groups. In contrast, the groups in a single-cell experiment are usually unknown and the analysis is often more exploratory.
Existing scRNA-seq analysis packages, and any new methods that are being developed, should demonstrate two properties: first that they can do what they claim to do, whether that is clustering, lineage tracing, differential expression testing or improved performance compared to other methods; and second that they produce some meaningful biological insight. The second criterion is specific to particular studies but it should be possible to address the first point in a more general way.
In this paper we present Splatter, an R Bioconductor package for reproducible and accurate simulati