decideTests {limma}R Documentation

Multiple Testing Across Genes and Contrasts


Classify a series of related t-statistics as up, down or not significant. A number of different multiple testing schemes are offered which adjust for multiple testing down the genes as well as across contrasts for each gene.




object MArrayLM object output from eBayes from which the t-statistics may be extracted.
method character string specify how probes and contrasts are to be combined in the multiple testing strategy. Choices are "separate", "global", "heirarchical", "nestedF" or any partial string.
adjust.method character string specifying p-value adjustment method. Possible values are "none", "BH", "fdr" (equivalent to "BH"), "BY" and "holm". See p.adjust for details.
p.value numeric value between 0 and 1 giving the desired size of the test


These functions implement multiple testing procedures for determining whether each statistic in a matrix of t-statistics should be considered significantly different from zero. Rows of tstat correspond to genes and columns to coefficients or contrasts.

The setting method="separate" is equivalent to using topTable separately for each coefficient in the linear model fit, and will give the same lists of probes if adjust.method is the same. method="global" will treat the entire matrix of t-statistics as a single vector of unrelated tests. method="heirarchical" adjusts down genes and then across contrasts. method="nestedF" adjusts down genes and then uses classifyTestsF to classify contrasts as significant or not for the selected genes.


An object of class TestResults. This is essentially a numeric matrix with elements -1, 0 or 1 depending on whether each t-statistic is classified as significantly negative, not significant or significantly positive respectively.


Gordon Smyth

See Also

An overview of multiple testing functions is given in 08.Tests.

[Package limma version 2.4.7 Index]