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Splatter: simulation of single-cell RNA sequencing data

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...

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Detalles Bibliográficos
Autores principales: Zappia, Luke, Phipson, Belinda, Oshlack, Alicia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5596896/
https://www.ncbi.nlm.nih.gov/pubmed/28899397
http://dx.doi.org/10.1186/s13059-017-1305-0
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author Zappia, Luke
Phipson, Belinda
Oshlack, Alicia
author_facet Zappia, Luke
Phipson, Belinda
Oshlack, Alicia
author_sort Zappia, Luke
collection PubMed
description 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-017-1305-0) contains supplementary material, which is available to authorized users.
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spelling pubmed-55968962017-09-15 Splatter: simulation of single-cell RNA sequencing data Zappia, Luke Phipson, Belinda Oshlack, Alicia Genome Biol Method 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-017-1305-0) contains supplementary material, which is available to authorized users. BioMed Central 2017-09-12 /pmc/articles/PMC5596896/ /pubmed/28899397 http://dx.doi.org/10.1186/s13059-017-1305-0 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Method
Zappia, Luke
Phipson, Belinda
Oshlack, Alicia
Splatter: simulation of single-cell RNA sequencing data
title Splatter: simulation of single-cell RNA sequencing data
title_full Splatter: simulation of single-cell RNA sequencing data
title_fullStr Splatter: simulation of single-cell RNA sequencing data
title_full_unstemmed Splatter: simulation of single-cell RNA sequencing data
title_short Splatter: simulation of single-cell RNA sequencing data
title_sort splatter: simulation of single-cell rna sequencing data
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5596896/
https://www.ncbi.nlm.nih.gov/pubmed/28899397
http://dx.doi.org/10.1186/s13059-017-1305-0
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