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 2.4.7

Index]