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Empirical likelihood tests for nonparametric detection of differential expression from RNA-seq data

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  • Thorne Thomas

    (School of Informatics, University of Edinburgh, EH8 9AB, UK)

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.

Suggested Citation

  • Thorne Thomas, 2015. "Empirical likelihood tests for nonparametric detection of differential expression from RNA-seq data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(6), pages 575-583, December.
    • Handle: RePEc:bpj:sagmbi:v:14:y:2015:i:6:p:575-583:n:5
    DOI: 10.1515/sagmb-2015-0095
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    References listed on IDEAS

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    1. Bartolucci, Francesco, 2007. "A penalized version of the empirical likelihood ratio for the population mean," Statistics & Probability Letters, Elsevier, vol. 77(1), pages 104-110, January.
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