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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,...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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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. |
format | Online Article Text |
id | pubmed-7214028 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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