EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments

By Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M. G. Smits, J. Haag, M. Gould, Ron M. Stewart, Christina Kendziorski. Bioinform., 2013

1,216 citations recorded by the source corpus.

MOTIVATION Messenger RNA expression is important in normal development and differentiation, as well as in manifestation of disease. RNA-seq experiments allow for the identification of differentially expressed (DE) genes and their corresponding isoforms on a genome-wide scale. However, statistical methods are required to ensure that accurate identifications are made. A number of methods exist for identifying DE genes, but far fewer are available for identifying DE isoforms. When isoform DE is of interest, investigators often apply gene-level (count-based) methods directly to estimates of isoform counts. Doing so is not recommended. In short, estimating isoform expression is relatively straightforward for some groups of isoforms, but more challenging for others. This results in estimation uncertainty that varies across isoform groups. Count-based methods were not designed to accommodate this varying uncertainty, and consequently, application of them for isoform inference results in reduced power for some classes of isoforms and increased false discoveries for others. RESULTS Taking advantage of the merits of empirical Bayesian methods, we have developed EBSeq for identifying DE isoforms in an RNA-seq experiment comparing two or more biological conditions. Results demonstrate substantially improved power and performance of EBSeq for identifying DE isoforms. EBSeq also proves to be a robust approach for identifying DE genes. AVAILABILITY AND IMPLEMENTATION An R package containing examples and sample datasets is available at http://www.biostat.wisc.edu/kendzior/EBSEQ/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0141c5e6f271f98722904c30d3ae9fd348bd7204

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Citations in the corpus

A tissue-mapped axolotl de novo transcriptome enables identification of limb regeneration factors (2017, positive): “RSEM (Li and Dewey, 2011) and MMSEQ (Turro et al., 2011) software were used to estimate expression values for transcripts, and edgeR (Robinson et al., 2010), EBSeq (Leng et al., 2013), and mmdiff (Turro et al., 2014) were used to identify significantly differentially expressed transcripts.”
A survey of best practices for RNA-seq data analysis (2016, background): “…like edgeR, uses the negative binomial as the reference distribution and provides its own normalization approach [48, 58]. baySeq [59] and EBSeq [60] are Bayesian approaches, also based on the negative binomial model, that define a collection of models to describe the differences among…”
Annotation-free quantification of RNA splicing using LeafCutter (2017, neutral): “Popular approaches for studying alternative splicing from RNA-seq estimate isoform ratios 5,6,7,8 or exon inclusion levels 9,10 .”
RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis (2018, methodology): “Bayesian approaches for identifying DTE based on estimated counts, e.g., ranking via Bayes factors, include EBSeq [134], which uses an empirical Bayesian hierarchical model, and MMSeq [79,168], which fits a linear mixed model to data via Markov Chain Monte Carlo techniques.”
Differential analysis of RNA-seq incorporating quantification uncertainty (2016, methodology): “We also examined the effect of different filtering strategies on performance by comparing sleuth with other programs on a common filtered set of genes, showing that sleuth maintains its advantage independent of filtering (Supplementary Figures 9-14).”
A comparison of methods for differential expression analysis of RNA-seq data (2013, methodology): “Nine of them work on the count data directly: DESeq [7], edgeR [23], NBPSeq [15], TSPM [13], baySeq [14], EBSeq [24], NOISeq [25], SAMseq [26] and ShrinkSeq [27].”
How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use? (2015, background): “…(e.g., Anders and Huber 2010; Hardcastle and Kelly 2010; Robinson et al. 2010; Wang et al. 2010; Tarazona et al. 2011; Li et al. 2012; Lund et al. 2012; Trapnell et al. 2012; Leng et al. 2013; Li and Tibshirani 2013; Frazee et al. 2014; Law et al. 2014; Love et al. 2014; Moulos and Hatzis 2015).”
Human pluripotent stem cell-derived neural constructs for predicting neural toxicity (2015, methodology): “RNA-Seq data were then analyzed by EBSeq (32) to identify genes up-regulated within the neural constructs compared with undifferentiated human ES cells (Dataset S2).”
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 (2014, neutral): “The negative-binomial-based approaches compared were DESeq (old) [4], edgeR [33], edgeR with the robust option [34], DSS [6] and EBSeq [35].”
Comparison of software packages for detecting differential expression in RNA-seq studies (2013, background): “EBSeq [11] is mainly developed to identify differentially expressed isoforms but has demonstrated a robust outcome in gene level analyses as well.”
Temporal Control of Mammalian Cortical Neurogenesis by m6A Methylation (2017, positive): “A uthor M anuscript A uthor M anuscript A uthor M anuscript A uthor M anuscript Leng N, Dawson JA, Thomson JA, Ruotti V, Rissman AI, Smits BM, Haag JD, Gould MN, Stewart RM, Kendziorski C. EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments.”
Robustly detecting differential expression in RNA sequencing data using observation weights (2013, methodology): “EBSeq appears to lack power and can be both liberal and conservative.”