| decideTests {limma} | R Documentation |
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.
decideTests(object,method="separate",adjust.method="BH",p.value=0.05,lfc=0)
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", "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 |
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.
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.
Gordon Smyth
An overview of multiple testing functions is given in 08.Tests.