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splatPop: simulating population scale single-cell RNA sequencing data

Population-scale single-cell RNA sequencing (scRNA-seq) is now viable, enabling finer resolution functional genomics studies and leading to a rush to adapt bulk methods and develop new single-cell-specific methods to perform these studies. Simulations are useful for developing, testing, and benchmar...

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Autores principales: Azodi, Christina B., Zappia, Luke, Oshlack, Alicia, McCarthy, Davis J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8672480/
https://www.ncbi.nlm.nih.gov/pubmed/34911537
http://dx.doi.org/10.1186/s13059-021-02546-1
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author Azodi, Christina B.
Zappia, Luke
Oshlack, Alicia
McCarthy, Davis J.
author_facet Azodi, Christina B.
Zappia, Luke
Oshlack, Alicia
McCarthy, Davis J.
author_sort Azodi, Christina B.
collection PubMed
description Population-scale single-cell RNA sequencing (scRNA-seq) is now viable, enabling finer resolution functional genomics studies and leading to a rush to adapt bulk methods and develop new single-cell-specific methods to perform these studies. Simulations are useful for developing, testing, and benchmarking methods but current scRNA-seq simulation frameworks do not simulate population-scale data with genetic effects. Here, we present splatPop, a model for flexible, reproducible, and well-documented simulation of population-scale scRNA-seq data with known expression quantitative trait loci. splatPop can also simulate complex batch, cell group, and conditional effects between individuals from different cohorts as well as genetically-driven co-expression. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s13059-021-02546-1).
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spelling pubmed-86724802021-12-15 splatPop: simulating population scale single-cell RNA sequencing data Azodi, Christina B. Zappia, Luke Oshlack, Alicia McCarthy, Davis J. Genome Biol Method Population-scale single-cell RNA sequencing (scRNA-seq) is now viable, enabling finer resolution functional genomics studies and leading to a rush to adapt bulk methods and develop new single-cell-specific methods to perform these studies. Simulations are useful for developing, testing, and benchmarking methods but current scRNA-seq simulation frameworks do not simulate population-scale data with genetic effects. Here, we present splatPop, a model for flexible, reproducible, and well-documented simulation of population-scale scRNA-seq data with known expression quantitative trait loci. splatPop can also simulate complex batch, cell group, and conditional effects between individuals from different cohorts as well as genetically-driven co-expression. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s13059-021-02546-1). BioMed Central 2021-12-15 /pmc/articles/PMC8672480/ /pubmed/34911537 http://dx.doi.org/10.1186/s13059-021-02546-1 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Method
Azodi, Christina B.
Zappia, Luke
Oshlack, Alicia
McCarthy, Davis J.
splatPop: simulating population scale single-cell RNA sequencing data
title splatPop: simulating population scale single-cell RNA sequencing data
title_full splatPop: simulating population scale single-cell RNA sequencing data
title_fullStr splatPop: simulating population scale single-cell RNA sequencing data
title_full_unstemmed splatPop: simulating population scale single-cell RNA sequencing data
title_short splatPop: simulating population scale single-cell RNA sequencing data
title_sort splatpop: simulating population scale single-cell rna sequencing data
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8672480/
https://www.ncbi.nlm.nih.gov/pubmed/34911537
http://dx.doi.org/10.1186/s13059-021-02546-1
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