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Article: PLNseq: a multivariate Poisson lognormal distribution for high‐throughput matched RNA‐sequencing read count data

TitlePLNseq: a multivariate Poisson lognormal distribution for high‐throughput matched RNA‐sequencing read count data
Authors
KeywordsRna‐Seq
Differential Expression Analysis
Poisson Lognormal Model
Matched Samples
Issue Date2015
PublisherJohn Wiley & Sons Ltd. The Journal's web site is located at http://www.interscience.wiley.com/jpages/0277-6715/
Citation
Statistics in Medicine, 2015, v. 34, p. 1577-1589 How to Cite?
AbstractHigh‐throughput RNA‐sequencing (RNA‐seq) technology provides an attractive platform for gene expression analysis. In many experimental settings, RNA‐seq read counts are measured from matched samples or taken from the same subject under multiple treatment conditions. The induced correlation therefore should be evaluated and taken into account in deriving tests of differential expression. We proposed a novel method ‘PLNseq’, which uses a multivariate Poisson lognormal distribution to model matched read count data. The correlation is directly modeled through Gaussian random effects, and inferences are made by likelihood methods. A three‐stage numerical algorithm is developed to estimate unknown parameters and conduct differential expression analysis. Results using simulated data demonstrate that our method performs reasonably well in terms of parameter estimation, DE analysis power, and robustness. PLNseq also has better control of FDRs than the benchmarks edgeR and DESeq2 in the situations where the correlation is different across the genes but can still be accurately estimated. Furthermore, direct evaluation of correlation through PLNseq enables us to develop a new and more powerful test for DE analysis. Application to a lung cancer study is provided to illustrate the practical utilities of our method. An R package implementing the method is also publicly available. Copyright © 2015 John Wiley & Sons, Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/221672
ISSN
2015 Impact Factor: 1.533
2015 SCImago Journal Rankings: 1.811
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhang, H-
dc.contributor.authorXu, J-
dc.contributor.authorJiang, N-
dc.contributor.authorHu, X-
dc.contributor.authorLuo, Z-
dc.date.accessioned2015-12-04T15:29:00Z-
dc.date.available2015-12-04T15:29:00Z-
dc.date.issued2015-
dc.identifier.citationStatistics in Medicine, 2015, v. 34, p. 1577-1589-
dc.identifier.issn0277-6715-
dc.identifier.urihttp://hdl.handle.net/10722/221672-
dc.description.abstractHigh‐throughput RNA‐sequencing (RNA‐seq) technology provides an attractive platform for gene expression analysis. In many experimental settings, RNA‐seq read counts are measured from matched samples or taken from the same subject under multiple treatment conditions. The induced correlation therefore should be evaluated and taken into account in deriving tests of differential expression. We proposed a novel method ‘PLNseq’, which uses a multivariate Poisson lognormal distribution to model matched read count data. The correlation is directly modeled through Gaussian random effects, and inferences are made by likelihood methods. A three‐stage numerical algorithm is developed to estimate unknown parameters and conduct differential expression analysis. Results using simulated data demonstrate that our method performs reasonably well in terms of parameter estimation, DE analysis power, and robustness. PLNseq also has better control of FDRs than the benchmarks edgeR and DESeq2 in the situations where the correlation is different across the genes but can still be accurately estimated. Furthermore, direct evaluation of correlation through PLNseq enables us to develop a new and more powerful test for DE analysis. Application to a lung cancer study is provided to illustrate the practical utilities of our method. An R package implementing the method is also publicly available. Copyright © 2015 John Wiley & Sons, Ltd.-
dc.languageeng-
dc.publisherJohn Wiley & Sons Ltd. The Journal's web site is located at http://www.interscience.wiley.com/jpages/0277-6715/-
dc.relation.ispartofStatistics in Medicine-
dc.subjectRna‐Seq-
dc.subjectDifferential Expression Analysis-
dc.subjectPoisson Lognormal Model-
dc.subjectMatched Samples-
dc.titlePLNseq: a multivariate Poisson lognormal distribution for high‐throughput matched RNA‐sequencing read count data-
dc.typeArticle-
dc.identifier.emailXu, J: xujf@hku.hk-
dc.identifier.authorityXu, J=rp02086-
dc.identifier.doi10.1002/sim.6449-
dc.identifier.pmid25641202-
dc.identifier.scopuseid_2-s2.0-84926341161-
dc.identifier.hkuros260473-
dc.identifier.volume34-
dc.identifier.spage1577-
dc.identifier.epage1589-
dc.identifier.isiWOS:000352524100010-

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