decideTests(object,method="separate",adjust.method="BH",p.value=0.05,lfc=0)MArrayLM object output from eBayes or treat from which the t-statistics may be extracted."separate", "global", "hierarchical", "nestedF" or any partial string."none", "BH", "fdr" (equivalent to "BH"), "BY" and "holm". See p.adjust for details.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.
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.