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Bioinformatics Advance Access originally published online on January 28, 2009
Bioinformatics 2009 25(6):765-771; doi:10.1093/bioinformatics/btp053
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© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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

Davis J. McCarthy and Gordon K. Smyth *

The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3050, Australia

*To whom correspondence should be addressed.


  Abstract

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{at}wehi.edu.au

Associate Editor: Joaquin Dopazo

Received on October 12, 2008; revised on January 21, 2009; accepted on January 22, 2009

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