toptable {limma}R Documentation

Table of Top Genes from Linear Model Fit

Description

Extract a table of the top-ranked genes from a linear model fit.

Usage

toptable(fit,coef=1,number=10,genelist=NULL,A=NULL,eb=NULL,adjust.method="holm",sort.by="B",...)
topTable(fit,coef=1,number=10,genelist=NULL,adjust.method="holm",sort.by="B")

Arguments

fit for toptable, this is an output list from lm.series, gls.series or rlm.series. For topTable is an object of class MArrayLM.
coef column number of the effect or contrast to rank the genes on
number how many genes to pick out
genelist a data frame containing the gene allocation list or a vector containing the gene names
A matrix of A-values or vector of average A-values.
eb output list from ebayes(fit)
adjust.method method to use to adjust the P-values for multiple testing, e.g., "holm" or "fdr". See p.adjust for the available options. If NULL or "none" then the P-values are not adjusted.
sort.by statistic to rank genes by. Possibilities are "M", "A", "T", "P" or "B".
... any other arguments are passed to ebayes if eb is NULL

Details

This function summarizes a fit object produced by lm.series, gls.series or rlm.series by selecting the top-ranked genes for any given contrast.

Value

A dataframe with a row for the number top genes and the following columns:

genelist if genelist was included as input
M estimate of the effect or the contrast, on the log2 scale
t moderated t-statistic
P.Value nominal P-value
B log odds that the gene is differentially expressed

Author(s)

Gordon Smyth

See Also

ebayes, p.adjust, lm.series, gls.series, rlm.series.

Examples

#  Simulate gene expression data,
#  6 microarrays and 100 genes with first gene differentially expressed
M <- matrix(rnorm(100*6,sd=0.3),100,6)
M[1,1:3] <- M[1,1:3] + 2
#  Design matrix includes two treatments, one for first 3 and one for last 3 arrays
design <- cbind(First3Arrays=c(1,1,1,0,0,0),Last3Arrays=c(0,0,0,1,1,1))
fit <- lm.series(M,design=design)
toptable(fit)

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