SCnorm: robust normalization of single-cell RNA-seq data

Normalization of RNA-sequencing data is essential for accurate downstream inference, but the assumptions upon which most methods are based do not hold in the single-cell setting. Consequently, applying existing normalization methods to single-cell RNA-seq data introduces artifacts that bias downstre...

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Detalles Bibliográficos
Autores principales: Bacher, Rhonda, Chu, Li-Fang, Leng, Ning, Gasch, Audrey P., Thomson, James A., Stewart, Ron M., Newton, Michael, Kendziorski, Christina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2017
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5473255/
https://www.ncbi.nlm.nih.gov/pubmed/28418000
http://dx.doi.org/10.1038/nmeth.4263
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author Bacher, Rhonda
Chu, Li-Fang
Leng, Ning
Gasch, Audrey P.
Thomson, James A.
Stewart, Ron M.
Newton, Michael
Kendziorski, Christina
author_facet Bacher, Rhonda
Chu, Li-Fang
Leng, Ning
Gasch, Audrey P.
Thomson, James A.
Stewart, Ron M.
Newton, Michael
Kendziorski, Christina
author_sort Bacher, Rhonda
collection PubMed
description Normalization of RNA-sequencing data is essential for accurate downstream inference, but the assumptions upon which most methods are based do not hold in the single-cell setting. Consequently, applying existing normalization methods to single-cell RNA-seq data introduces artifacts that bias downstream analyses. To address this, we introduce SCnorm for accurate and efficient normalization of scRNA-seq data.
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spelling pubmed-54732552017-10-17 SCnorm: robust normalization of single-cell RNA-seq data Bacher, Rhonda Chu, Li-Fang Leng, Ning Gasch, Audrey P. Thomson, James A. Stewart, Ron M. Newton, Michael Kendziorski, Christina Nat Methods Article Normalization of RNA-sequencing data is essential for accurate downstream inference, but the assumptions upon which most methods are based do not hold in the single-cell setting. Consequently, applying existing normalization methods to single-cell RNA-seq data introduces artifacts that bias downstream analyses. To address this, we introduce SCnorm for accurate and efficient normalization of scRNA-seq data. 2017-04-17 2017-06 /pmc/articles/PMC5473255/ /pubmed/28418000 http://dx.doi.org/10.1038/nmeth.4263 Text en Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms
spellingShingle Article
Bacher, Rhonda
Chu, Li-Fang
Leng, Ning
Gasch, Audrey P.
Thomson, James A.
Stewart, Ron M.
Newton, Michael
Kendziorski, Christina
SCnorm: robust normalization of single-cell RNA-seq data
title SCnorm: robust normalization of single-cell RNA-seq data
title_full SCnorm: robust normalization of single-cell RNA-seq data
title_fullStr SCnorm: robust normalization of single-cell RNA-seq data
title_full_unstemmed SCnorm: robust normalization of single-cell RNA-seq data
title_short SCnorm: robust normalization of single-cell RNA-seq data
title_sort scnorm: robust normalization of single-cell rna-seq data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5473255/
https://www.ncbi.nlm.nih.gov/pubmed/28418000
http://dx.doi.org/10.1038/nmeth.4263
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