baySeq: Empirical Bayesian methods for identifying differential expression in sequence count data

By Thomas J. Hardcastle, Krystyna A. Kelly. BMC Bioinformatics, 2010

813 citations recorded by the source corpus.

BackgroundHigh throughput sequencing has become an important technology for studying expression levels in many types of genomic, and particularly transcriptomic, data. One key way of analysing such data is to look for elements of the data which display particular patterns of differential expression in order to take these forward for further analysis and validation.ResultsWe propose a framework for defining patterns of differential expression and develop a novel algorithm, baySeq, which uses an empirical Bayes approach to detect these patterns of differential expression within a set of sequencing samples. The method assumes a negative binomial distribution for the data and derives an empirically determined prior distribution from the entire dataset. We examine the performance of the method on real and simulated data.ConclusionsOur method performs at least as well, and often better, than existing methods for analyses of pairwise differential expression in both real and simulated data. When we compare methods for the analysis of data from experimental designs involving multiple sample groups, our method again shows substantial gains in performance. We believe that this approach thus represents an important step forward for the analysis of count data from sequencing experiments.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/35f03921a48e03687333b9d275bf5e58bd2520b8

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Citations in the corpus

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 (2014, neutral): “BaySeq [7] and ShrinkBayes [8] estimate priors for a Bayesian model over all genes, and then provide posterior probabilities or false discovery rates for the case of differential expression.”
voom: precision weights unlock linear model analysis tools for RNA-seq read counts (2014, methodology): “This is the same context as assumed by a number of previous articles [13,18,20,21,34].”
Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-Seq data (2013, methodology): “These methods are parametric: they assume that (maybe after some simple transformation) each Nij is drawn from a particular distribution, such as Gaussian ([4, 12]), Poisson ([16, 7, 32, 11, 14]), negative binomial ([23, 24, 22, 11, 1]), et al ([2, 15]).”
EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments (2013, methodology): “The Negative Binomial assumption is common to each method considered here (Anders and Huber, 2010; Hardcastle and Kelly, 2010; Robinson and Smyth, 2007; Trapnell et al., 2012), and should therefore not provide advantage, or lack thereof, to any one method in particular.”
RNA Sequencing and Analysis (2015, neutral): “In addition to Cuffdiff, several other packages support testing differential expression, including baySeq (Hardcastle and Kelly 2010), DESeq (Anders and Huber 2010), DEGseq (Wang et al. 2010b), and edgeR (Robinson et al. 2010) (Table 2).”
A Bayesian hierarchical model to detect differentially methylated loci from single nucleotide resolution sequencing data (2014, positive): “Previous work focused on RNA-seq data has shown that the true biological variance among replicates in these data can be captured by the dispersion parameter, and several algorithms were developed to shrink the estimated dispersion parameters (24,26,27,30).”
The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote (2013, methodology): “We have created a Bioconductor package Rsubread to give access to Subread capabilities from the R command line, making a pipeline from FASTQ files to read count tables and statistical analyses using packages such as edgeR (50), baySeq (51) or diffBind (www.”
Differential analysis of RNA-seq incorporating quantification uncertainty (2016, background): “There are multiple sources of variance that contribute to the total variance observed between samples in an RNASeq experiment which we group into two classes: (1) “biological variance” which is a term used to describe variance in transcript abundance of biological and experimental origin and (2) “in”
Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible (2013, background): “However, some of these alternatives from the RNA-Seq community may outperform DESeq on microbiome data meeting special conditions, for example a large proportion of true positives and suf-ﬁcient replicates [67], small sample sizes [14], or extreme values [68].”
Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation (2012, methodology): “Other more explicitly Bayesian methods have been proposed for SAGE (38,39) or RNA-Seq data (40), the latter implemented in the Bioconductor package baySeq.”
Differential analysis of gene regulation at transcript resolution with RNA-seq (2012, background): “Previous studies observed that overdispersion in RNA-seq experiments increases with expression and proposed the negative binomial distribution as a means of controlling for it 17,22 .”
Differential expression in RNA-seq: a matter of depth. (2011, methodology): “Methods traditionally used for microarrays have paved the way to other approaches that take into account the discrete nature of the expression quantification and use different probability distributions to model data (Marioni et al. 2008; Robinson et al. 2010; Anders and Huber 2010; Sultan et al. 200”