Single-cell RNA sequencing (scRNA-seq) is rapidly becoming a tool of choice for biologists who wish to investigate gene expression. In contrast to traditional bulk RNA-seq experiments, which measure expression averaged across millions of cells, single-cell experiments can be used to observe how genes are expressed in individual cells. Along with the dramatic increase in resolution provided by scRNA-seq comes an array of bioinformatics challenges. Single-cell data is relatively sparse (for both biological and technical reasons), quality control is difficult and it is unclear how to replicate measurements. Researchers have risen to address these challenges and there are currently more than 125 software tools available for analysing scRNA-seq data. We have catalogued these software tools in the scRNA-tools database (www.scRNA-tools.org). Analysis of this database shows that there are now methods available for a wide range of tasks, from pre-processing unique molecular identifiers to detecting allele-specific expression. However, the biggest areas of development have been in clustering cells to identify cell types and ordering of cells to understand dynamic processes. We also find that the R statistical programming language is the most popular platform for scRNA-seq analysis tools, followed by Python, and that the majority of tools have been described in peer-reviewed papers or preprints and are available under open-source software licenses.
With the ever increasing number of analysis methods available it is important to be able to assess and compare the performance, quality and limitations of an analysis tool. This is often done, at least in part, by testing methods on simulated datasets where the true answers are known. Unfortunately, current scRNA-seq simulations are frequently poorly documented, not reproducible and do not demonstrate similarity to real data or experimental designs. To address these concerns we have developed Splatter, a Bioconductor R package for reproducible simulation of scRNA-seq datasets. Splatter is a simulation framework that currently includes four previously published simulation models, allowing users to estimate parameters from real data in order to easily generate realistic synthetic scRNA-seq datasets. Here we discuss some of the challenges of simulating scRNA-seq data and present a comparison of the simulation methods available in Splatter (bioconductor.org/packages/splatter). As part of Splatter we also introduce our own simulation model, Splat, capable of reproducing scRNA-seq datasets with multiple groups of cells, differentiation paths or batch effects.
Simulation and analysis
tools for single-cell RNA
Gene Cell Cell Cell Cell
Svensson et al. arXiv . ,
Gene Cell Cell Cell Cell
Cell type specific?
Provide a truth to test against
- Often poorly documented and explained
- Not easily reproducible or reusable
- Don’t demonstrate similarity to real data
“Splatter: simulation of single-cell RNA sequencing data.”
Genome Biology ( ) DOI: . /s - - -
Defined library sizes
Simple - Negative binomial
Lun - NB with cell factors
Lun ATL, Bach K, Marioni JC. Genome Biology ( ).
DOI: . /s - - - .
Lun 2 - Sampled NB with batch effects
Lun ATL, Marioni JC. Biostatistics ( ).
DOI: . /biostatistics/kxw .
scDD - NB with bimodality
Korthauer KD, et al. Genome Biology ( ).
DOI: . /s - - -y.
BASiCS - NB with spike-ins
Vallejos CA, Marioni JC, Richardson S. PLoS Comp. Bio. ( ).
DOI: . /journal.pcbi. .
params1 params2 sim1 sim2 datasets Splat = sim1,
Simple = sim2)
comp diff Using Splatter
Tung P-Y et al. Sci. Rep. ( ) DOI: . /srep
A A A
B B B
C C C
Tung et al. iPSCs, C capture
Means Difference in Means
Zeros per cell Difference in zeros
Groups Batches Paths
New in Splatter . .
Many tools for scRNA-seq analysis
Catalogued in the scRNA-tools database
Can be tested using synthetic datasets
Splatter is our package for simulating scRNA-seq data
Everyone that makes tools and data available
“Splatter: simulation of
single-cell RNA sequencing
Genome Biology ( )
. /s - - -
“Exploring the single-cell
RNA-seq analysis landscape
with the scRNA-tools database”
bioRxiv ( )
DOI: . /