Table of the Top Differentially Expressed Tags
Extracts the top DE tags in a data frame for a given pair of groups, ranked by p-value or absolute log-fold change.
topTags(object, n=10, adjust.method="BH", sort.by="PValue", p.value=1)
DGEExactobject (output from
exactTest) or a
DGELRTobject (output from
glmLRT), containing the (at least) the elements
table: a data frame containing the log-concentration (i.e. expression level), the log-fold change in expression between the two groups/conditions and the p-value for differential expression, for each tag. If it is a
topTagswill also use the
comparisonelement, which is a vector giving the two experimental groups/conditions being compared. The object may contain other elements that are not used by
- scalar, number of tags to display/return
- character string stating the method used to adjust p-values for multiple testing, passed on to
- character string, should the top tags be sorted by p-value (
"PValue"), by absolute log-fold change (
"logFC"), or not sorted (
- cutoff value for adjusted p-values. Only tags with lower p-values are listed.
- an object of class
- a data frame containing the elements
logFC, the log-abundance ratio, i.e. fold change, for each tag in the two groups being compared,
logCPM, the log-average concentration/abundance for each tag in the two groups being compared,
PValue, exact p-value for differential expression using the NB model,
FDR, the p-value adjusted for multiple testing as found using
p.adjustusing the method specified.
- character string stating the method used to adjust p-values for multiple testing.
- a vector giving the names of the two groups being compared.
- character string stating the name of the test. The dimensions, row names and column names of a
TopTagscontaining the following elements for the top
nmost differentially expressed tags as determined by
TopTagsobject are defined by those of
TopTagsobjects also have a
showmethod so that printing produces a compact summary of their contents.Note that the terms `tag' and `gene' are synonymous here. The function is only named as `Tags' for historical reasons.
Robinson MD, Smyth GK (2008). Small-sample estimation of negative binomial dispersion, with applications to SAGE data. Biostatistics 9, 321-332.
Robinson MD, Smyth GK (2007). Moderated statistical tests for assessing differences in tag abundance. Bioinformatics 23, 2881-2887.
topTable in the limma package.
# generate raw counts from NB, create list object y <- matrix(rnbinom(80,size=1,mu=10),nrow=20) d <- DGEList(counts=y,group=rep(1:2,each=2),lib.size=rep(c(1000:1001),2)) rownames(d$counts) <- paste("gene",1:nrow(d$counts),sep=".") # estimate common dispersion and find differences in expression # here we demonstrate the 'exact' methods, but the use of topTags is # the same for a GLM analysis d <- estimateCommonDisp(d) de <- exactTest(d) # look at top 10 topTags(de) # Can specify how many genes to view tp <- topTags(de, n=15) # Here we view top 15 tp # Or order by fold change instead topTags(de,sort.by="logFC")