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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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2016-12-01
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| Series: | PeerJ |
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| Online Access: | https://peerj.com/articles/2791.pdf |
| _version_ | 1797419210628923392 |
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| author | Bin Zhuo Sarah Emerson Jeff H. Chang Yanming Di |
| author_facet | Bin Zhuo Sarah Emerson Jeff H. Chang Yanming Di |
| author_sort | Bin Zhuo |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T06:45:04Z |
| format | Article |
| id | doaj.art-3066613b2da74063bff628e09b7a46b8 |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T06:45:04Z |
| publishDate | 2016-12-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-3066613b2da74063bff628e09b7a46b82023-12-03T10:37:54ZengPeerJ Inc.PeerJ2167-83592016-12-014e279110.7717/peerj.2791Identifying stably expressed genes from multiple RNA-Seq data setsBin Zhuo0Sarah Emerson1Jeff H. Chang2Yanming Di3Department of Statistics, Oregon State University, Corvallis, OR, United StatesDepartment of Statistics, Oregon State University, Corvallis, OR, United StatesDepartment of Botany and Plant Pathology, Oregon State University, Corvallis, OR, United StatesDepartment of Statistics, Oregon State University, Corvallis, OR, United StatesWe 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.https://peerj.com/articles/2791.pdfStably expressed geneRNA-SeqNumerical stability measureReference gene set |
| spellingShingle | Bin Zhuo Sarah Emerson Jeff H. Chang Yanming Di Identifying stably expressed genes from multiple RNA-Seq data sets PeerJ Stably expressed gene RNA-Seq Numerical stability measure Reference gene set |
| 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 | Stably expressed gene RNA-Seq Numerical stability measure Reference gene set |
| url | https://peerj.com/articles/2791.pdf |
| work_keys_str_mv | AT binzhuo identifyingstablyexpressedgenesfrommultiplernaseqdatasets AT sarahemerson identifyingstablyexpressedgenesfrommultiplernaseqdatasets AT jeffhchang identifyingstablyexpressedgenesfrommultiplernaseqdatasets AT yanmingdi identifyingstablyexpressedgenesfrommultiplernaseqdatasets |