venn {limma} | R Documentation

## Venn Diagrams

### Description

Compute classification counts or plot classification counts in a Venn diagram.

### Usage

    vennCounts(x, include="both")
    vennDiagram(object, include="both", names, mar=rep(1,4), cex=1.5, ...)

### Arguments

`x` | numeric matrix of 0's and 1's indicating significance of a test. Usually created by `decideTests`.
`object` | either a `TestResults` matrix or a `VennCounts` object produced by `vennCounts`.
`include` | character string specifying whether to counts genes up-regulated, down-regulated or both. Choices are `"both"`, `"up"` or `"down"`.
`names` | optional character vector giving names for the sets or contrasts
`mar` | numeric vector of length 4 specifying the width of the margins around the plot. This argument is passed to `par`.
`cex` | numerical value giving the amount by which the contrast names should be scaled on the plot relative to the default.plotting text. See `par`.
`...` | any other arguments are passed to `plot`

### Value

`vennCounts` produces a `VennCounts` object, which is a numeric matrix with last column `"Counts"` giving counts for each possible vector outcome. `vennDiagram` causes a plot to be produced on the current graphical device. For `venDiagram`, the number of columns of `object` should be three or fewer.

### Author(s)

Gordon Smyth and James Wettenhall

### See Also

An overview of linear model functions in limma is given by 06.LinearModels.

### Examples

    Y <- matrix(rnorm(100*6),100,6)
    Y[1:10,3:4] <- Y[1:10,3:4]+3
    Y[1:20,5:6] <- Y[1:20,5:6]+3
    design <- cbind(1,c(0,0,1,1,0,0),c(0,0,0,0,1,1))
    fit <- eBayes(lmFit(Y,design))
    results <- decideTests(fit)
    a <- vennCounts(results)
    print(a)
    vennDiagram(a)

[Package limma version 2.6.2 Index]