For each module in a set, calculate the number of genes which are in that module and which are significantly up- or down-regulated.
tmodDecideTests(
g,
lfc = NULL,
pval = NULL,
lfc.thr = 0.5,
pval.thr = 0.05,
labels = NULL,
filter.unknown = FALSE,
mset = "all"
)A list with as many elements as there were comparisons (columns in lfc and pval). Each element of the list is a data frame with the columns "Down", "Zero" and "Up" giving the number of the down-, not- and up-regulated genes respectively. Rows of the data frame correspond to module IDs.
a character vector with gene symbols
a numeric vector or a matrix with log fold changes
a numeric vector or a matrix with p-values. Must have the same dimensions as lfc
log fold change threshold
p-value threshold
Names of the comparisons. Either NULL or a character vector of length equal to the number of columns in lfc and pval.
If TRUE, modules with no annotation will be omitted
Which module set to use. Either a character vector ("LI", "DC" or "all", default: LI) or a list (see "Custom module definitions" below)
This function can be used to decide whether a module, as a whole, is up- or down regulated. For each module, it calculates the number of genes which are up-, down- or not regulated. A gene is considered to be up- regulated if the associated p-value is smaller than pval.thr and the associated log fold change is greater than lfc.thr. A gene is considered to be down- regulated if the associated p-value is smaller than pval.thr and the associated log fold change is smaller than lfc.thr.
Note that unlike decideTests from limma, tmodDecideTests does not correct the p-values for multiple testing -- therefore, the p-values should already be corrected.
tmodSummary, tmodPanelPlot, tmodDecideTestsLimma