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

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 2.4.7 Index]