decideTests {limma} | R Documentation |
Classify a series of related t-statistics as up, down or not significant.
decideTests(object,method="separate",adjust.method="fdr",p.value=0.05)
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. See p.adjust for possible values. |
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 default settings with method="separate"
is equivalent to using topTable
separately for each coefficient in the linear model fit. 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
An overview of linear model functions in limma is given by 5.LinearModels.