Splatter: simulation of single-cell RNA sequencing data
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Abstract
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.
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Papers evaluating the performance of single-cell-specific tools all compute the simulations as if cells were independent.
“Furthermore, papers evaluating the performance of single-cell-specific tools all compute the simulations as if cells were independent15–21.”
From A practical solution to pseudoreplication bias in single-cell studies (2021) · cites this paper as [16]
The Background (page unavailable) says many single-cell analysis packages use simulations to evaluate their performance, but does not say those simulations treat cells as independent. The Results sections on simulation models and differentiation paths describe Splatter’s own simulations, not a survey establishing that all tool-evaluation papers make that assumption. Searches for independent cells and intercellular dependence found no passage supporting the universal claim. (LLM, conf=0.72)
Experimental — accuracy not yet measuredSignificant marker genes are found even when clustering random data generated by splatter.
“We thus find significant marker genes even when clustering random data generated by splatter (Zappia et al, 2017) (see Appendix Supplementary Text S3).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Zappia et al, 2017
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- Authors
- Zappia, L., Phipson, B., & Oshlack, A.
- Affiliations
- Royal Children's Hospital (AU)
- Corresponding author
- Zappia, Luke
- Phipson, Belinda
- Oshlack, Alicia
- Venue
- Genome Biology
- Published
- 2017-09-12
- Volume
- 18
- Issue
- 1
- Pages
- 174-174
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- PMC
- PMC5596896
- OpenAlex
- W2949067670
- Cited by
- 905
- References
- 54
- Citations by year
- 2026: 68 · 2025: 115 · 2024: 153 · 2023: 98 · 2022: 138 · 2021: 138 · 2020: 96 · 2019: 61 · 2018: 29 · 2017: 8 · 2016: 1
- FWCI
- 21.67
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Genome biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- Funders
- Australian Government
- State Government of Victoria
- Medical Research Council
- National Health and Medical Research Council (1126157)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- single-cell RNA sequencing, data simulation, splatter, splats, Bioconductor package, cell type composition, reproducibility
- MeSH terms
- Animals, Computer Simulation, Humans, Models, Genetic, Software, Reproducibility of Results, Cluster Analysis, Sequence Analysis, RNA, Single-Cell Analysis, Datasets as Topic
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