Up- and down-regulated genes in modules based on limma object

For each module in mset and for each coefficient in f$coefficients, this function calculates the numbers of significantly up- and down-regulated genes.

Usage

tmodLimmaDecideTests(
  f,
  genes,
  lfc.thr = 0.5,
  pval.thr = 0.05,
  filter.unknown = FALSE,
  adjust.method = "BH",
  mset = "all"
)

Arguments

f

result of linear model fit produced by limma functions lmFit and eBayes

genes

Either the name of the column in f$genes which contains the gene symbols corresponding to the gene set collection used, or a character vector with gene symbols

lfc.thr

log fold change threshold

pval.thr

p-value threshold

filter.unknown

If TRUE, modules with no annotation will be omitted

adjust.method

method used to adjust the p-values for multiple testing. See p.adjust(). Default:BH.

mset

Which module set to use (see tmodUtest for details)

Value

A list with as many elements as there were coefficients in f. 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. The object can directly be used in tmodPanelPlot as the pie parameter.

Details

For an f object returned by eBayes(), tmodLimmaDecideTests considers every coefficient in this model (every column of f$coefficients). For each such coefficient, tmodLimmaDecideTests calculates, for each module, the number of genes which are up- or down-regulated.

In short, tmodLimmaDecideTests is the equivalent of tmodDecideTests, but for limma objects returned by eBayes().

See also

tmodDecideTests, tmodLimmaTest, tmodPanelPlot

Examples

if (FALSE) {
data(Egambia)
design <- cbind(Intercept=rep(1, 30), TB=rep(c(0,1), each= 15))
if(require(limma)) {
  fit <- eBayes( lmFit(Egambia[,-c(1:3)], design))
  ret <- tmodLimmaTest(fit, Egambia$GENE_SYMBOL)
  pie <- tmodLimmaDecideTests(fit, Egambia$GENE_SYMBOL)
  tmodPanelPlot(ret, pie=pie)
}
}