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.
method
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.
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
lfc
minimum log2-fold-change required
Details
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.
Value
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.
Note
Although this function enables users to set p-value and lfc cutoffs simultaneously, this is not generally recommended.
If the fold changes and p-values are not highly correlated, then the use of a fold change cutoff can increase the false discovery rate above the nominal level.
Users wanting to use fold change thresholding are recommended to use treat instead of eBayes, and to leave lfc at the default value when using decideTests.
Author(s)
Gordon Smyth
See Also
An overview of multiple testing functions is given in 08.Tests.
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