title: Testing significance relative to a fold-change threshold is a TREAT.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  R code implementing our methods is contributed to the software package limma available at http://www.bioconductor.org.
authors.affiliation: The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3050, Australia.
authors.forename: Davis J
authors.initials: DJ
authors.lastname: McCarthy
authors.forename: Gordon K
authors.initials: GK
authors.lastname: Smyth
mesh_terms: D000465:Algorithms; D020869:Gene Expression Profiling / Q000379:methods*; D020407:Internet; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012984:Software