toptable {limma}R Documentation

Table of Top Genes from Linear Model Fit


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




fit list containing a linear model fit produced by lmFit, lm.series, gls.series or mrlm. For topTable, fit should be an object of class MArrayLM as produced by lmFit.
coef column number or column name specifying which coefficient or contrast of the linear model is of interest
number how many genes to pick out
genelist data frame or character vector containing gene information. If not specified, this will be taken from the genes component of fit.
A matrix of A-values or vector of average A-values.
eb output list from ebayes(fit)
adjust.method method used to adjust the p-values for multiple testing. Options, in increasing conservatism, include "none", "BH", "BY" and "holm". See p.adjust for the complete list of options. A NULL value will result in the default adjustment method, which is "BH". character string specifying statistic to rank genes by. Possibilities are "M", "A", "T", "t", "P", "p" or "B". character string specifying statistic to sort the selected genes by in the output data.frame. Possibilities are "M", "A", "T", "t", "P", "p" or "B".
... any other arguments are passed to ebayes if eb is NULL


This function summarizes a linear model fit object produced by lmFit, lm.series, gls.series or mrlm by selecting the top-ranked genes for any given contrast. topTable() assumes that the linear model fit has already been processed by eBayes(). Note that toptable is the earlier interface and is being phased out.

The p-values for the coefficient/contrast of interest are adjusted for multiple testing by a call to p.adjust. The "holm" method is the default because it is conservative and valid for any type of dependence between the p-values. In most microarray contexts however the less conservative Benjamini and Hochberg method "fdr" may be more suitable. See help("p.adjust") for more information. Note, if there is no good evidence for differential expression in the experiment, that it is quite possible for all the adjusted p-values to be large, even for all of them to be equal to one. It is quite possible for all the adjusted p-values to be equal to one if the smallest p-value is no smaller than 1/ngenes where ngenes is the number of genes with non-missing p-values. Note that p-values adjusted to control the false discovery rate are often called q-values.

The argument specifies the criterion used to select the top genes. The choices are: "M" to sort by the (absolute) coefficient representing the log-fold-change; "A" to sort by average expression level (over all arrays) in descending order; "T" or "t" for absolute t-statistic; "P" or "p" for p-values; or "B" for the lods or B-statistic.

Normally the genes appear in order of selection in the output table. If one wants the table to be in a different order, the argument may be used. For example, topTable(fit,"B","M") selects the top genes according to log-odds of differential expression and then orders the resulting genes by log-ratio in decreasing order. Or topTable(fit,"M","M") would select the genes by absolute log-ratio and then sort then by signed log-ratio from must positive to most negative.


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 adjusted p-value or q-value
B log odds that the gene is differentially expressed


Gordon Smyth

See Also

An overview of linear model and testing functions is given in 06.LinearModels. See also p.adjust in the stats package.


#  See lmFit examples

[Package limma version 2.4.7 Index]