Journal Article

Testing significance relative to a fold-change threshold is a TREAT

Davis J. McCarthy and Gordon K. Smyth

in Bioinformatics

Volume 25, issue 6, pages 765-771
Published in print March 2009 | ISSN: 1367-4803
Published online January 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp053

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Motivation: Statistical methods are used to test for the differential expression of genes in microarray experiments. The most widely used methods successfully test whether the true differential expression is different from zero, but give no assurance that the differences found are large enough to be biologically meaningful.

Results: We present a method, t-tests relative to a threshold (TREAT), that allows researchers to test formally the hypothesis (with associated p-values) that the differential expression in a microarray experiment is greater than a given (biologically meaningful) threshold. We have evaluated the method using simulated data, a dataset from a quality control experiment for microarrays and data from a biological experiment investigating histone deacetylase inhibitors. When the magnitude of differential expression is taken into account, TREAT improves upon the false discovery rate of existing methods and identifies more biologically relevant genes.

Availability: R code implementing our methods is contributed to the software package limma available at http://www.bioconductor.org.

Contact: smyth@wehi.edu.au

Journal Article.  5100 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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