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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 simulation of single-cell RNA sequencing data. Splatter is a framework designed to provide a consistent interface to multiple published simulations, enabling researchers to quickly simulate scRNA-seq count data in a reproducible fashion and make comparisons between simulations and real data. Along with the framework we have developed our own simulation model, Splat, and show how it compares to previously published simulations based on real datasets. We also provide a short example of how simulations can be used for assessing analysis methods.
Splatter provides implementations of our own simulation model, Splat, as well as several previously published simulations. The previous simulations have either been published as R code associated with a paper or as functions in existing packages. By including them in Splatter, we have made them available in a single place in a more accessible way. If only a script was originally published, such as the Lun  and Lun 2  simulations, the simulations have been re-implemented in Splatter. If the simulation is available in an existing R package, for example, scDD  and BASiCS , we have simply written wrappers that provide consistent input and output but use the package implementation. We have endeavored to keep the simulations and estimation procedures as close as possible to what was originally published while providing a consistent interface within Splatter. The six different simulations currently available in Splatter are described below.
The scDD package aims to test for differential expression between two groups of cells but also more complex changes such as differential distributions or differential proportions . This is reflected in the scDD simulation, which can contain a mixture of genes simulated to have different distributions, or differing proportions where the expression of the gene is multi-modal. This simulation also samples information from a real dataset. As the scDD simulation is designed to reproduce a high quality, filtered dataset, it only samples from genes with less than 75% zeros. As a result, it only simulates relatively highly expressed genes. The Splatter package simply provides wrapper functions to the simulation function in the scDD package, while capturing the necessary inputs and outputs needed to compare to other simulations. The full details of the scDD simulation are described in the scDD package vignette .
The BASiCS package introduced a model for separating variation in scRNA-seq data into biological and technical components based on the expression of external spike-in controls . This model also enables cell-specific normalization and was extended to detect differential expression between groups of cells . Similar to the scDD simulation, Splatter provides a wrapper for the BASiCS simulation function, which is able to produce datasets with both endogenous and spike-in genes as well as multiple batches of cells. As the BASiCS simulation contains both biological and technical variation, it can be used to test the ability of methods to distinguish between the two.
Here we have developed Splatter, an independent framework for the reproducible simulation of scRNA-seq data. Splatter is available as an R package from Bioconductor, under a GPL-3 license, and implements a series of simulation models. Splatter can easily estimate parameters for each model from real data, generate synthetic datasets and quickly create a series of diagnostic plots comparing different simulations and datasets.
To easily generate a simulation that is similar to a given dataset, Splatter includes functions to estimate the parameters for each simulation from real datasets. Just as with the simulation models themselves, the estimation procedures are based on what has been published and there is variation in how many parameters can be estimated for each model. We have given significant attention to estimating the parameters for the Splat simulation. The parameters that control the mean expression of each gene (\( \alpha \) and \( \beta \)) are estimated by fitting a gamma distribution to the winsorized means of the library size normalized counts using the fitdistrplus package . The library size normalization is a basic normalization where the counts in the original dataset are adjusted so that each cell has the same number of total counts (in this case the median across all cells) and any genes that are all zero are removed. We found that genes with extreme means affect the fit of the gamma distribution and that this effect was mitigated by winsorizing the top and bottom 10% of values to the 10th and 90th percentiles, respectively. Parameters for the library size distribution (\( \mu^L \) and \( \sigma^L \)) are estimated in a similar way by fitting a log-normal distribution to the unnormalised library sizes.
BCV parameters are estimated using the estimateDisp function in the edgeR package . When testing the estimation procedure on simulated datasets we observed that the edgeR estimate of common dispersion was inflated (Additional file 1: Figure S16); therefore, we apply a linear correction to this value (\( \widehat\phi=0.1+0.25\widehat\phi_\mathrmedgeR \)).
Each of the real datasets used in the comparison of simulations is publicly available. Raw FASTQ files for the Camp dataset were downloaded from SRA (accession SRP066834) and processed using a Bpipe (v0.9.9.3)  pipeline that examined the quality of reads using FastQC (v0.11.4), aligned the reads to the hg38 reference genome using STAR (v2.5.2a) , and counted reads overlapping genes in the Gencode V22 annotation using featureCounts (v1.5.0-p3) . Matrices of gene by cell expression values for the Klein (accession GSM1599500) and Zeisel (accession GSE60361) datasets were downloaded from GEO. For the Tung dataset the matrix of molecules (UMIs) aligned to each gene available from was used. These data are also available from GEO (accession GSE77288). The Salmon  quantification files for the Engel dataset were download from the Conquer database ( :3838/conquer/) and converted to a gene by cell matrix using the tximport  package. 041b061a72