title: Empirical likelihood tests for nonparametric detection of differential expression from RNA-seq data.
abstract: The availability of large quantities of transcriptomic data in the form of RNA-seq count data has necessitated the development of methods to identify genes differentially expressed between experimental conditions. Many existing approaches apply a parametric model of gene expression and so place strong assumptions on the distribution of the data. Here we explore an alternate nonparametric approach that applies an empirical likelihood framework, allowing us to define likelihoods without specifying a parametric model of the data. We demonstrate the performance of our method when applied to gold standard datasets, and to existing experimental data. Our approach outperforms or closely matches performance of existing methods in the literature, and requires modest computational resources. An R package, EmpDiff implementing the methods described in the paper is available from: http://homepages.inf.ed.ac.uk/tthorne/software/packages/EmpDiff_0.99.tar.gz.
authors.forename: Thomas
authors.identifier: http://orcid.org/0000-0002-7396-5116
authors.initials: T
authors.lastname: Thorne
mesh_terms: D020869:Gene Expression Profiling; D006801:Humans; D016013:Likelihood Functions; D061986:MCF-7 Cells; D012333:RNA, Messenger / Q000235:genetics / Q000378:metabolism; D012372:ROC Curve; D018336:Receptors, Aryl Hydrocarbon / Q000235:genetics / Q000378:metabolism; D017423:Sequence Analysis, RNA; D018709:Statistics, Nonparametric; D059467:Transcriptome
chemical_list: D012333:RNA, Messenger; D018336:Receptors, Aryl Hydrocarbon