tmodLimmaDecideTests | R Documentation |
For each module in mset and for each coefficient in f$coefficients, this function calculates the numbers of significantly up- and down-regulated genes.
tmodLimmaDecideTests(
f,
genes,
lfc.thr = 0.5,
pval.thr = 0.05,
filter.unknown = FALSE,
adjust.method = "BH",
mset = "all"
)
f |
result of linear model fit produced by limma functions lmFit and eBayes |
genes |
Either the name of the column in f$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) |
For an f object returned by eBayes(), tmodLimmaDecideTests considers every coefficient in this model (every column of f$coefficients). For each such coefficient, tmodLimmaDecideTests calculates, for each module, the number of genes which are up- or down-regulated.
In short, tmodLimmaDecideTests is the equivalent of tmodDecideTests, but for limma objects returned by eBayes().
A list with as many elements as there were coefficients in f. Each element of the list is a data frame with the columns "Down", "Zero" and "Up" giving the number of the down-, not- and up-regulated genes respectively. Rows of the data frame correspond to module IDs. The object can directly be used in tmodPanelPlot as the pie parameter.
tmodDecideTests, tmodLimmaTest, tmodPanelPlot
## Not run:
data(Egambia)
design <- cbind(Intercept=rep(1, 30), TB=rep(c(0,1), each= 15))
if(require(limma)) {
fit <- eBayes( lmFit(Egambia[,-c(1:3)], design))
ret <- tmodLimmaTest(fit, Egambia$GENE_SYMBOL)
pie <- tmodLimmaDecideTests(fit, Egambia$GENE_SYMBOL)
tmodPanelPlot(ret, pie=pie)
}
## End(Not run)
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