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SAMstrt: statistical test for differential expression in single-cell transcriptome with spike-in normalization

Motivation: Recent transcriptome studies have revealed that total transcript numbers vary by cell type and condition; therefore, the statistical assumptions for single-cell transcriptome studies must be revisited. SAMstrt is an extension code for SAMseq, which is a statistical method for differentia...

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Detalles Bibliográficos
Autores principales: Katayama, Shintaro, Töhönen, Virpi, Linnarsson, Sten, Kere, Juha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3810855/
https://www.ncbi.nlm.nih.gov/pubmed/23995393
http://dx.doi.org/10.1093/bioinformatics/btt511
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author Katayama, Shintaro
Töhönen, Virpi
Linnarsson, Sten
Kere, Juha
author_facet Katayama, Shintaro
Töhönen, Virpi
Linnarsson, Sten
Kere, Juha
author_sort Katayama, Shintaro
collection PubMed
description Motivation: Recent transcriptome studies have revealed that total transcript numbers vary by cell type and condition; therefore, the statistical assumptions for single-cell transcriptome studies must be revisited. SAMstrt is an extension code for SAMseq, which is a statistical method for differential expression, to enable spike-in normalization and statistical testing based on the estimated absolute number of transcripts per cell for single-cell RNA-seq methods. Availability and Implementation: SAMstrt is implemented on R and available in github (https://github.com/shka/R-SAMstrt). Contact: shintaro.katayama@ki.se Supplementary Information: Supplementary data are available at Bioinformatics online.
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spelling pubmed-38108552013-10-29 SAMstrt: statistical test for differential expression in single-cell transcriptome with spike-in normalization Katayama, Shintaro Töhönen, Virpi Linnarsson, Sten Kere, Juha Bioinformatics Applications Notes Motivation: Recent transcriptome studies have revealed that total transcript numbers vary by cell type and condition; therefore, the statistical assumptions for single-cell transcriptome studies must be revisited. SAMstrt is an extension code for SAMseq, which is a statistical method for differential expression, to enable spike-in normalization and statistical testing based on the estimated absolute number of transcripts per cell for single-cell RNA-seq methods. Availability and Implementation: SAMstrt is implemented on R and available in github (https://github.com/shka/R-SAMstrt). Contact: shintaro.katayama@ki.se Supplementary Information: Supplementary data are available at Bioinformatics online. Oxford University Press 2013-11-15 2013-08-31 /pmc/articles/PMC3810855/ /pubmed/23995393 http://dx.doi.org/10.1093/bioinformatics/btt511 Text en © The Author 2013. Published by Oxford University Press. All rights reserved. http://creativecommons.org/licenses/by/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Notes
Katayama, Shintaro
Töhönen, Virpi
Linnarsson, Sten
Kere, Juha
SAMstrt: statistical test for differential expression in single-cell transcriptome with spike-in normalization
title SAMstrt: statistical test for differential expression in single-cell transcriptome with spike-in normalization
title_full SAMstrt: statistical test for differential expression in single-cell transcriptome with spike-in normalization
title_fullStr SAMstrt: statistical test for differential expression in single-cell transcriptome with spike-in normalization
title_full_unstemmed SAMstrt: statistical test for differential expression in single-cell transcriptome with spike-in normalization
title_short SAMstrt: statistical test for differential expression in single-cell transcriptome with spike-in normalization
title_sort samstrt: statistical test for differential expression in single-cell transcriptome with spike-in normalization
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3810855/
https://www.ncbi.nlm.nih.gov/pubmed/23995393
http://dx.doi.org/10.1093/bioinformatics/btt511
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