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. 2009 Mar 15;25(6):765-71.
doi: 10.1093/bioinformatics/btp053. Epub 2009 Jan 28.

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

Davis J McCarthy  1 , Gordon K Smyth
Affiliations

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

Davis J McCarthy et al. Bioinformatics. .

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.

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Figures

Fig. 1.
Fig. 1.
FDRs for six different gene selection statistics from the analysis of simulated data. The rates are the means of actual FDRs for 1000 simulated datasets.
Fig. 2.
Fig. 2.
FDRs for six different gene selection statistics from the analysis of real experimental data. The dataset was produced by a quality control experiment conducted at the Peter MacCallum Cancer Centre. The rates are the means of the actual FDRs from 1000 analyses of pairs of arrays selected at random from the 99 replicate arrays in the dataset.
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