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Identifying stably expressed genes from multiple RNA-Seq data sets

We examined RNA-Seq data on 211 biological samples from 24 different Arabidopsis experiments carried out by different labs. We grouped the samples according to tissue types, and in each of the groups, we identified genes that are stably expressed across biological samples, treatment conditions, and...

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Autores principales: Zhuo, Bin, Emerson, Sarah, Chang, Jeff H., Di, Yanming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5178351/
https://www.ncbi.nlm.nih.gov/pubmed/28028467
http://dx.doi.org/10.7717/peerj.2791
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author Zhuo, Bin
Emerson, Sarah
Chang, Jeff H.
Di, Yanming
author_facet Zhuo, Bin
Emerson, Sarah
Chang, Jeff H.
Di, Yanming
author_sort Zhuo, Bin
collection PubMed
description We examined RNA-Seq data on 211 biological samples from 24 different Arabidopsis experiments carried out by different labs. We grouped the samples according to tissue types, and in each of the groups, we identified genes that are stably expressed across biological samples, treatment conditions, and experiments. We fit a Poisson log-linear mixed-effect model to the read counts for each gene and decomposed the total variance into between-sample, between-treatment and between-experiment variance components. Identifying stably expressed genes is useful for count normalization and differential expression analysis. The variance component analysis that we explore here is a first step towards understanding the sources and nature of the RNA-Seq count variation. When using a numerical measure to identify stably expressed genes, the outcome depends on multiple factors: the background sample set and the reference gene set used for count normalization, the technology used for measuring gene expression, and the specific numerical stability measure used. Since differential expression (DE) is measured by relative frequencies, we argue that DE is a relative concept. We advocate using an explicit reference gene set for count normalization to improve interpretability of DE results, and recommend using a common reference gene set when analyzing multiple RNA-Seq experiments to avoid potential inconsistent conclusions.
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spelling pubmed-51783512016-12-27 Identifying stably expressed genes from multiple RNA-Seq data sets Zhuo, Bin Emerson, Sarah Chang, Jeff H. Di, Yanming PeerJ Bioinformatics We examined RNA-Seq data on 211 biological samples from 24 different Arabidopsis experiments carried out by different labs. We grouped the samples according to tissue types, and in each of the groups, we identified genes that are stably expressed across biological samples, treatment conditions, and experiments. We fit a Poisson log-linear mixed-effect model to the read counts for each gene and decomposed the total variance into between-sample, between-treatment and between-experiment variance components. Identifying stably expressed genes is useful for count normalization and differential expression analysis. The variance component analysis that we explore here is a first step towards understanding the sources and nature of the RNA-Seq count variation. When using a numerical measure to identify stably expressed genes, the outcome depends on multiple factors: the background sample set and the reference gene set used for count normalization, the technology used for measuring gene expression, and the specific numerical stability measure used. Since differential expression (DE) is measured by relative frequencies, we argue that DE is a relative concept. We advocate using an explicit reference gene set for count normalization to improve interpretability of DE results, and recommend using a common reference gene set when analyzing multiple RNA-Seq experiments to avoid potential inconsistent conclusions. PeerJ Inc. 2016-12-20 /pmc/articles/PMC5178351/ /pubmed/28028467 http://dx.doi.org/10.7717/peerj.2791 Text en ©2016 Zhuo et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Zhuo, Bin
Emerson, Sarah
Chang, Jeff H.
Di, Yanming
Identifying stably expressed genes from multiple RNA-Seq data sets
title Identifying stably expressed genes from multiple RNA-Seq data sets
title_full Identifying stably expressed genes from multiple RNA-Seq data sets
title_fullStr Identifying stably expressed genes from multiple RNA-Seq data sets
title_full_unstemmed Identifying stably expressed genes from multiple RNA-Seq data sets
title_short Identifying stably expressed genes from multiple RNA-Seq data sets
title_sort identifying stably expressed genes from multiple rna-seq data sets
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5178351/
https://www.ncbi.nlm.nih.gov/pubmed/28028467
http://dx.doi.org/10.7717/peerj.2791
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