Splatter: simulation of single-cell RNA sequencing data

By Luke Zappia, Belinda Phipson, Alicia Oshlack. Genome Biology, 2017

800 citations recorded by the source corpus.

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.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/755b48a3a9743b394c0d2f6034ce004873835eb0

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Citations in the corpus

Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data (2018, methodology): “3) (Zappia et al., 2017) to simulate ground truth data for testing the doublet detector.”
A benchmark of batch-effect correction methods for single-cell RNA sequencing data (2020, methodology): “In the final scenario, we used the Splatter package [19] to generate simulation datasets with different drop-out rates and unbalanced cell counts across batches.”
Orchestrating single-cell analysis with Bioconductor (2019, background): “Alternately, the splatter package 84 can simulate scRNA-seq data that contains multiple cell types, batch effects, varying levels of drop-out events, differential gene expression and trajectories.”
Eleven grand challenges in single-cell data science (2020, methodology): “There are few 82 exceptions known to us, including the tools 83 Splatter [Zappia et al., 2017], powsimR [Vi- 84 eth et al.”
BayesFlow: Learning Complex Stochastic Models With Invertible Neural Networks (2020, background): “Indeed, there are many instances of research domains where a single model is extensively explored and independently ﬁtted by multiple researches to test scientiﬁc hypotheses [55, 59, 46, 11].”
DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors (2018, methodology): “…a single literature-supported cell state annotation may still have sufficient transcriptional heterogeneity to produce doublets that DoubletFinder can detect. scRNA-Seq Data Simulation: scRNA-seq data was simulated using the ‘splatter’ R package (Zappia et al., 2017), as in (Wolock et al., 2018).”
Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data (2021, neutral): “Finally, the Scissor selected cells will be further characterized in downstream analyses, such as exploration of signature genes and functionally enriched pathways (Fig.”
Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics (2017, methodology): “In order to examine the performance of Slingshot and other methods in a wide range of scenarios, we performed a simulation study using the Bioconductor R package splatter [28] to produce artificial single-cell RNA-Seq datasets.”
Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq (2018, methodology): “Materials and methods Simulations Our simulation framework is based on Splatter (Zappia et al., 2017) but is re-implemented in Python and adapted to allow simulation of doublets and activity programs.”
Clustering single-cell RNA-seq data with a model-based deep learning approach (2019, methodology): “Therefore, fixing the dropout rate at 17% (default setting of Splatter), we evaluated the methods under various sigma values.”
Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data (2022, methodology): “To investigate this effect, we utilized the Splatter method 35 , and generated 45 simulated datasets with various dropout rates: 15 datasets with ~50% dropouts, 15 datasets with ~65% dropouts, and 15 datasets with ~80% dropouts (see Methods).”
Current best practices in single‐cell RNA‐seq analysis: a tutorial (2019, positive): “We thus find significant marker genes even when clustering random data generated by splatter (Zappia et al, 2017) (see Appendix Supplementary Text S3).”