limma (version 3.28.14)

decideTests: 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.




MArrayLM object output from eBayes or treat from which the t-statistics may be extracted.
character string specify how probes and contrasts are to be combined in the multiple testing strategy. Choices are "separate", "global", "hierarchical", "nestedF" or any partial string.
character string specifying p-value adjustment method. Possible values are "none", "BH", "fdr" (equivalent to "BH"), "BY" and "holm". See p.adjust for details.
numeric value between 0 and 1 giving the desired size of the test
minimum log2-fold-change required


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.If lfc>0 then contrasts are judged significant only when the log2-fold change is at least this large in absolute value. For example, one might choose lfc=log2(1.5) to restrict to 50% changes or lfc=1 for 2-fold changes. In this case, contrasts must satisfy both the p-value and the fold-change cutoff to be judged significant.


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="hierarchical" 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. Please see the limma User's Guide for a discussion of the statistical properties of these methods.

See Also

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