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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