The NBP Negative Binomial Model for Assessing Differential Gene Expression from RNA-Seq

Di, Yanming; Schafer, Daniel W.; Cumbie, Jason S.; Chang, Jeff H.

doi:10.2202/1544-6115.1637

Cited by 161 publications

(232 citation statements)

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“…We have run these procedures on other packages and methods such as DESeq2 and edgeR-robust with essentially similar results. Of course, many other methods exist, including but not limited to: Cuffdiff (Trapnell et al (2010)), Cuffdiff2 (Trapnell et al (2013)), NBPSeq (Di, Schafer, Cumbie, and Chang (2011)), TSPM (Auer and Doerge (2011)), baySeq (Hardcastle and Kelly (2010)), EBSeq (Leng et al (2013)), NOISeq (Tarazona, García-Alcalde, Dopazo, Ferrer, and Conesa (2011)), SAMseq (J. Li and Tibshirani (2013)), ShrinkSeq (Van De Wiel et al (2012)), DEGSeq (Wang, Feng, Wang, Wang, and Zhang (2010)), BBSeq (Y.-H. Zhou, Xia, and Wright (2011)), FDM (Singh et al (2011)), RSEM (B. Li and Dewey (2011)), Myrna (Langmead, Hansen, and Leek (2010)), PANDORA (Moulos and Hatzis (2014)), ALDEx2 (Fernandes et al (2014)), PoissonSeq (J. Li, Witten, Johnstone, and Tibshirani (2011)), and GPSeq (Srivastava and Chen (2010)).…”

Section: Cc-by-nd 40 International License Peer-reviewed) Is the Autmentioning

confidence: 99%

Excess False Positive Rates in Methods for Differential Gene Expression Analysis using RNA-Seq Data

Rocke

Ruan

Zhang

et al. 2015

Preprint

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Motivation: An important property of a valid method for testing for differential expression is that the false positive rate should at least roughly correspond to the p-value cutoff, so that if 10,000 genes are tested at a p-value cutoff of 10 −4 , and if all the null hypotheses are true, then there should be only about 1 gene declared to be significantly differentially expressed. We tested this by resampling from existing RNA-Seq data sets and also by matched negative binomial simulations.Results: Methods we examined, which rely strongly on a negative binomial model, such as edgeR, DESeq, and DESeq2, show large numbers of false positives in both the resampled real-data case and in the simulated negative binomial case. This also occurs with a negative binomial generalized linear model function in R. Methods that use only the variance function, such as limma-voom, do not show excessive false positives, as is also the case with a variance stabilizing transformation followed by linear model analysis with limma. The excess false positives are likely caused by apparently small biases in estimation of negative binomial dispersion and, perhaps surprisingly, occur mostly when the mean and/or the dispersion is high, rather than for low-count genes. Contact:dmrocke@ucdavis.edu, lruan@ucdavis.edu, yilzhang@ucdavis.edu, gt4636b@gatech.edu, bpdurbin@ucdavis.edu, saviran@ucdavis.edu.Supplementary Information: The computational tools developed for this study are freely available via our website http://dmrocke.ucdavis.edu/software.html. They can be downloaded as R code or run directly through an interactive web-based shiny application to reproduce the analysis presented here per a user's choice of dataset and the methods to be evaluated.

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Section: Cc-by-nd 40 International License Peer-reviewed) Is the Autmentioning

confidence: 99%

Excess False Positive Rates in Methods for Differential Gene Expression Analysis using RNA-Seq Data

Rocke

Ruan

Zhang

et al. 2015

Preprint

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“…(See also Di et al (2011) for a comparison of approaches to model mean-variance relations in RNA-Seq data.) To obtain the coefficients a 0 and a 1 , we regress the dispersion estimatesα il for all counting bins from all genes on their average normalized count valuesμ il with a gamma-family GLM.…”

Section: Model and Inferencementioning

confidence: 99%

Detecting differential usage of exons from RNA-Seq data

Anders¹,

Reyes²,

Huber³

2012

Nat Prec

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RNA-Seq is a powerful tool for the study of alternative splicing and other forms of alternative isoform expression. Understanding the regulation of these processes requires sensitive and specific detection of differential isoform abundance in comparisons between conditions, cell types or tissues. We present DEXSeq, a statistical method to test for differential exon usage in RNA-Seq data. DEXSeq employs generalized linear models and offers reliable control of false discoveries by taking biological variation into account. DEXSeq detect genes, and in many cases specific exons, that are subject to differential exon usage with high sensitivity. We demonstrate the versatility of DEXSeq by applying it to several data sets. The method facilitates the study of regulation and function of alternative exon usage on a genome-wide scale. An implementation of DEXSeq is available as an R/Bioconductor package.

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“…Some prominent examples include Lu et al (2005), Robinson and Smyth (2008a,b), Anders and Huber (2010), Hardcastle and Kelly (2010), Di et al (2011), Van De Wiel et al (2012), andMcCarthy et al (2012). A recent review of methods was provided by Lorenz et al (2014).…”

Section: Significance Testing For Rna-seq Read Count Datamentioning

confidence: 99%

Detecting Differentially Expressed Genes with RNA-seq Data Using Backward Selection to Account for the Effects of Relevant Covariates

Nguyen

Nettleton

et al. 2015

JABES

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A common challenge in analysis of transcriptomic data is to identify differentially expressed genes, i.e., genes whose mean transcript abundance levels differ across the levels of a factor of scientific interest. Transcript abundance levels can be measured simultaneously for thousands of genes in multiple biological samples using RNA sequencing (RNA-seq) technology. Part of the variation in RNA-seq measures of transcript abundance may be associated with variation in continuous and/or categorical covariates measured for each experimental unit or RNA sample. Ignoring relevant covariates or modeling the effects of irrelevant covariates can be detrimental to identifying differentially expressed genes. We propose a backward selection strategy for selecting a set of covariates whose effects are accounted for when searching for differentially expressed genes. We illustrate our approach through the analysis of an RNA-seq study intended to identify genes differentially expressed between two lines of pigs divergently selected for residual feed intake. We use simulation to show the advantages of our backward selection procedure over alternative strategies that either ignore or adjust for all measured covariates.

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The NBP Negative Binomial Model for Assessing Differential Gene Expression from RNA-Seq

Cited by 161 publications

References 0 publications

Excess False Positive Rates in Methods for Differential Gene Expression Analysis using RNA-Seq Data

Excess False Positive Rates in Methods for Differential Gene Expression Analysis using RNA-Seq Data

Detecting differential usage of exons from RNA-Seq data

Detecting Differentially Expressed Genes with RNA-seq Data Using Backward Selection to Account for the Effects of Relevant Covariates

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