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Open AccessArticle

A Comparison of Methods for RNA-Seq Differential Expression Analysis and a New Empirical Bayes Approach

1
Department of Mathematics, Florida State University, Tallahassee, FL 32306, USA
2
Department of Pharmacology and Systems Therapeutics, Mount Sinai School of Medicine, New York, NY 10029, USA
3
Division of Biostatistics, The Ohio State University, Columbus, OH 43210, USA
*
Author to whom correspondence should be addressed.
Biosensors 2013, 3(3), 238-258; https://doi.org/10.3390/bios3030238
Received: 12 May 2013 / Revised: 9 June 2013 / Accepted: 12 June 2013 / Published: 28 June 2013
(This article belongs to the Special Issue Bio- and Chemo-Sensor Networks and Arrays)
Transcriptome-based biosensors are expected to have a large impact on the future of biotechnology. However, a central aspect of transcriptomics is differential expression analysis, where, currently, deep RNA sequencing (RNA-seq) has the potential to replace the microarray as the standard assay for RNA quantification. Our contributions here to RNA-seq differential expression analysis are two-fold. First, given the high cost of an RNA-seq run, biological replicates are rare, and therefore, information sharing across genes to obtain variance estimates is crucial. To handle such information sharing in a rigorous manner, we propose an hierarchical, empirical Bayes approach (R-EBSeq) that combines the Cufflinks model for generating relative transcript abundance measurements, known as FPKM (fragments per kilobase of transcript length per million mapped reads) with the EBArrays framework, which was previously developed for empirical Bayes analysis of microarray data. A desirable feature of R-EBSeq is easy-to-implement analysis of more than pairwise comparisons, as we illustrate with experimental data. Secondly, we develop the standard RNA-seq test data set, on the level of reads, where 79 transcripts are artificially differentially expressed and, therefore, explicitly known. This test data set allows us to compare the performance, in terms of the true discovery rate, of R-EBSeq to three other widely used RNAseq data analysis packages: Cuffdiff, DEseq and BaySeq. Our analysis indicates that DESeq identifies the first half of the differentially expressed transcripts well, but then is outperformed by Cuffdiff and R-EBSeq. Cuffdiff and R-EBSeq are the two top performers. Thus, R-EBSeq offers good performance, while allowing flexible and rigorous comparison of multiple biological conditions. View Full-Text
Keywords: next-generation sequencing; empirical Bayes; gene expression data next-generation sequencing; empirical Bayes; gene expression data
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Wesolowski, S.; Birtwistle, M.R.; Rempala, G.A. A Comparison of Methods for RNA-Seq Differential Expression Analysis and a New Empirical Bayes Approach. Biosensors 2013, 3, 238-258.

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