edgeR (version 3.14.0)

topTags: 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)


a DGEExact object (output from exactTest) or a DGELRT object (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 DGEExact object, then topTags will also use the comparison element, which is a vector giving the two experimental groups/conditions being compared. The object may contain other elements that are not used by topTags.
scalar, number of tags to display/return
character string stating the method used to adjust p-values for multiple testing, passed on to p.adjust
character string, should the top tags be sorted by p-value ("PValue"), by absolute log-fold change ("logFC"), or not sorted ("none").
cutoff value for adjusted p-values. Only tags with lower p-values are listed.


an object of class TopTags containing the following elements for the top n most differentially expressed tags as determined by sort.by:
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.adjust using 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 TopTags object are defined by those of table, see dim.TopTags or dimnames.TopTags.TopTags objects also have a show method 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.

See Also

exactTest, glmLRT, p.adjust.

Analogous to topTable in the limma package.


Run this code
# 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
# Can specify how many genes to view
tp <- topTags(de, n=15)
# Here we view top 15
# Or order by fold change instead

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