tmodLimmaDecideTests: Up- and down-regulated genes in modules based on limma object
Description
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.
library(limma)
data(Egambia)
design <- cbind(Intercept=rep(1, 30), TB=rep(c(0,1), each= 15))
fit <- eBayes( lmFit(Egambia[,-c(1:3)], design))
ret <- tmodLimmaTest(fit, Egambia$GENE_SYMBOL)
pie <- tmodLimmaDecideTests(fit, Egambia$GENE_SYMBOL)
tmodPanelPlot(ret, pie=pie)
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