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SPsimSeq: semi-parametric simulation of bulk and single-cell RNA-sequencing data

SUMMARY: SPsimSeq is a semi-parametric simulation method to generate bulk and single-cell RNA-sequencing data. It is designed to simulate gene expression data with maximal retention of the characteristics of real data. It is reasonably flexible to accommodate a wide range of experimental scenarios,...

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Detalles Bibliográficos
Autores principales: Assefa, Alemu Takele, Vandesompele, Jo, Thas, Olivier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7214028/
https://www.ncbi.nlm.nih.gov/pubmed/32065619
http://dx.doi.org/10.1093/bioinformatics/btaa105
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author Assefa, Alemu Takele
Vandesompele, Jo
Thas, Olivier
author_facet Assefa, Alemu Takele
Vandesompele, Jo
Thas, Olivier
author_sort Assefa, Alemu Takele
collection PubMed
description SUMMARY: SPsimSeq is a semi-parametric simulation method to generate bulk and single-cell RNA-sequencing data. It is designed to simulate gene expression data with maximal retention of the characteristics of real data. It is reasonably flexible to accommodate a wide range of experimental scenarios, including different sample sizes, biological signals (differential expression) and confounding batch effects. AVAILABILITY AND IMPLEMENTATION: The R package and associated documentation is available from https://github.com/CenterForStatistics-UGent/SPsimSeq. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-72140282020-05-15 SPsimSeq: semi-parametric simulation of bulk and single-cell RNA-sequencing data Assefa, Alemu Takele Vandesompele, Jo Thas, Olivier Bioinformatics Applications Notes SUMMARY: SPsimSeq is a semi-parametric simulation method to generate bulk and single-cell RNA-sequencing data. It is designed to simulate gene expression data with maximal retention of the characteristics of real data. It is reasonably flexible to accommodate a wide range of experimental scenarios, including different sample sizes, biological signals (differential expression) and confounding batch effects. AVAILABILITY AND IMPLEMENTATION: The R package and associated documentation is available from https://github.com/CenterForStatistics-UGent/SPsimSeq. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2020-05-15 2020-02-17 /pmc/articles/PMC7214028/ /pubmed/32065619 http://dx.doi.org/10.1093/bioinformatics/btaa105 Text en © The Author(s) 2020. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Applications Notes
Assefa, Alemu Takele
Vandesompele, Jo
Thas, Olivier
SPsimSeq: semi-parametric simulation of bulk and single-cell RNA-sequencing data
title SPsimSeq: semi-parametric simulation of bulk and single-cell RNA-sequencing data
title_full SPsimSeq: semi-parametric simulation of bulk and single-cell RNA-sequencing data
title_fullStr SPsimSeq: semi-parametric simulation of bulk and single-cell RNA-sequencing data
title_full_unstemmed SPsimSeq: semi-parametric simulation of bulk and single-cell RNA-sequencing data
title_short SPsimSeq: semi-parametric simulation of bulk and single-cell RNA-sequencing data
title_sort spsimseq: semi-parametric simulation of bulk and single-cell rna-sequencing data
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7214028/
https://www.ncbi.nlm.nih.gov/pubmed/32065619
http://dx.doi.org/10.1093/bioinformatics/btaa105
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