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

  • Thomas Thorne ORCID logo EMAIL logo
Published/Copyright: December 10, 2015
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Statistical Applications in Genetics and Molecular Biology
From the journal Statistical Applications in Genetics and Molecular Biology Volume 14 Issue 6

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.

Keywords: differential expression; RNA-seq; transcriptomics
Corresponding author: Thomas Thorne, School of Informatics, University of Edinburgh, EH8 9AB, UK, e-mail: .

Acknowledgments

This work was supported by the University of Edinburgh Chancellor’s Fellowship to T.T.

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Supplemental Material:

The online version of this article (DOI: 10.1515/sagmb-2015-0095) offers supplementary material, available to authorized users.

Published Online: 2015-12-10
Published in Print: 2015-12-1

©2015 by De Gruyter

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Homology cluster differential expression analysis for interspecies mRNA-Seq experiments
  4. Using informative Multinomial-Dirichlet prior in a t-mixture with reversible jump estimation of nucleosome positions for genome-wide profiling
  5. On the validity of within-nuclear-family genetic association analysis in samples of extended families
  6. An Empirical Bayes risk prediction model using multiple traits for sequencing data
  7. Empirical likelihood tests for nonparametric detection of differential expression from RNA-seq data
https://doi.org/10.1515/sagmb-2015-0095
Keywords for this article
differential expression; RNA-seq; transcriptomics

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Homology cluster differential expression analysis for interspecies mRNA-Seq experiments
  4. Using informative Multinomial-Dirichlet prior in a t-mixture with reversible jump estimation of nucleosome positions for genome-wide profiling
  5. On the validity of within-nuclear-family genetic association analysis in samples of extended families
  6. An Empirical Bayes risk prediction model using multiple traits for sequencing data
  7. Empirical likelihood tests for nonparametric detection of differential expression from RNA-seq data
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