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=NULL, number=10, genelist=fit$genes, adjust.method="BH",
         sort.by="B", resort.by=NULL, p.value=1, lfc=0, confint=FALSE)
toptable(fit, coef=1, number=10, genelist=NULL, A=NULL, eb=NULL, adjust.method="BH",
         sort.by="B", resort.by=NULL, p.value=1, lfc=0, confint=FALSE, ...)
topTableF(fit, number=10, genelist=fit$genes, adjust.method="BH",
         sort.by="F", p.value=1, lfc=0)
topTreat(fit, coef=1, sort.by="p", resort.by=NULL, ...)

Arguments

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 and eBayes.

coef

column number or column name specifying which coefficient or contrast of the linear model is of interest. For topTable, can also be a vector of column subscripts, in which case the gene ranking is by F-statistic for that set of contrasts.

number

maximum number of genes to list

genelist

data frame or character vector containing gene information. For topTable only, this defaults to fit$genes.

A

matrix of A-values or vector of average A-values. For topTable only, this defaults to fit$Amean.

eb

output list from ebayes(fit). If NULL, this will be automatically generated.

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".

sort.by

character string specifying statistic to rank genes by. Possible values for topTable and toptable are "logFC", "AveExpr", "t", "P", "p", "B" or "none". (Permitted synonyms are "M" for "logFC", "A" or "Amean" for "AveExpr", "T" for "t" and "p" for "P".) Possibilities for topTableF are "F" or "none". Possibilities for topTreat are as for topTable except for "B".

resort.by

character string specifying statistic to sort the selected genes by in the output data.frame. Possibilities are the same as for sort.by.

p.value

cutoff value for adjusted p-values. Only genes with lower p-values are listed.

lfc

minimum absolute log2-fold-change required. topTable and topTableF include only genes with (at least one) absolute log-fold-changes greater than lfc. topTreat does not remove genes but ranks genes by evidence that their log-fold-change exceeds lfc.

confint

logical, should confidence 95% intervals be output for logFC? Alternatively, can take a numeric value between zero and one specifying the confidence level required.

...

For toptable, other arguments are passed to ebayes (if eb=NULL). For topTreat, other arguments are passed to topTable.

Details

toptable is an earlier interface and is retained only for backward compatibility.

These functions summarize the linear model fit object produced by lmFit, lm.series, gls.series or mrlm by selecting the top-ranked genes for any given contrast. topTable and topTableF assume that the linear model fit has already been processed by eBayes. topTreat assumes that the fit has been processed by treat.

The p-values for the coefficient/contrast of interest are adjusted for multiple testing by a call to p.adjust. The "BH" method, which controls the expected false discovery rate (FDR) below the specified value, is the default adjustment method because it is the most likely to be appropriate for microarray studies. Note that the adjusted p-values from this method are bounds on the FDR rather than p-values in the usual sense. Because they relate to FDRs rather than rejection probabilities, they are sometimes called q-values. 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.

The sort.by argument specifies the criterion used to select the top genes. The choices are: "logFC" 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 a different order is wanted, then the resort.by argument may be useful. For example, topTable(fit, sort.by="B", resort.by="logFC") selects the top genes according to log-odds of differential expression and then orders the selected genes by log-ratio in decreasing order. Or topTable(fit, sort.by="logFC", resort.by="logFC") would select the genes by absolute log-fold-change and then sort them from most positive to most negative.

topTableF ranks genes on the basis of moderated F-statistics for one or more coefficients. If topTable is called and coef has two or more elements, then the specified columns will be extracted from fit and topTableF called on the result. topTable with coef=NULL is the same as topTableF, unless the fitted model fit has only one column.

Toptable output for all probes in original (unsorted) order can be obtained by topTable(fit,sort="none",n=Inf). However write.fit or write may be preferable if the intention is to write the results to a file. A related method is as.data.frame(fit) which coerces an MArrayLM object to a data.frame.

By default number probes are listed. Alternatively, by specifying p.value and number=Inf, all genes with adjusted p-values below a specified value can be listed.

The argument lfc gives the ability to filter genes by log-fold change. This argument is not available for topTreat because treat already handles fold-change thresholding in a more sophisticated way.

Value

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

genelist

one or more columns of probe annotation, if genelist was included as input

logFC

estimate of the log2-fold-change corresponding to the effect or contrast (for topTableF there may be several columns of log-fold-changes)

CI.L

left limit of confidence interval for logFC (if confint=TRUE or confint is numeric)

CI.R

right limit of confidence interval for logFC (if confint=TRUE or confint is numeric)

AveExpr

average log2-expression for the probe over all arrays and channels, same as Amean in the MarrayLM object

t

moderated t-statistic (omitted for topTableF)

F

moderated F-statistic (omitted for topTable unless more than one coef is specified)

P.Value

raw p-value

adj.P.Value

adjusted p-value or q-value

B

log-odds that the gene is differentially expressed (omitted for topTreat)

If fit had unique rownames, then the row.names of the above data.frame are the same in sorted order. Otherwise, the row.names of the data.frame indicate the row number in fit. If fit had duplicated row names, then these are preserved in the ID column of the data.frame, or in ID0 if genelist already contained an ID column.

Note

Although topTable enables users to set p-value and lfc cutoffs simultaneously, this is not generally recommended. If the fold changes and p-values are not highly correlated, then the use of a fold change cutoff can increase the false discovery rate above the nominal level. Users wanting to use fold change thresholding are usually recommended to use treat and topTreat instead.

In general, the adjusted p-values returned by adjust.method="BH" remain valid as FDR bounds only when the genes remain sorted by p-value. Resorting the table by log-fold-change can increase the false discovery rate above the nominal level for genes at the top of resorted table.

Author(s)

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.

Examples

#  See lmFit examples
[Package limma version 3.34.5 Index]