gplots (version 2.14.2)

venn: Plot a Venn diagram

Description

Plot a Venn diagrams for up to 5 sets

Usage

venn(data, universe=NA, small=0.7, showSetLogicLabel=FALSE,
     simplify=FALSE, show.plot=TRUE)
## S3 method for class 'venn':
plot(x, y, ..., small=0.7, showSetLogicLabel=FALSE,
          simplify=FALSE)

Arguments

data,x

Either a list list containing vectors of names or indices of group members, or a data frame containing boolean indicators of group membership (see below)

universe

Subset of valid name/index elements. Values ignore values in code{data} not in this list will be ignored. Use NA to use all elements of data (the default).

small

Character scaling of the smallest group counts

showSetLogicLabel

Logical flag indicating whether the internal group label should be displayed

simplify

Logical flag indicating whether unobserved groups should be omitted.

show.plot

Logical flag indicating whether the plot should be displayed. If false, simply returns the group count matrix.

y,...

Ignored

Value

• Invisibly returns an object of class "venn", containing a matrix of all possible sets of groups, and the observed count of items belonging to each The fist column contains observed counts, subsequent columns contain 0-1 indicators of group membership.

Details

data should be either a named list of vectors containing character string names ("GeneAABBB", "GeneBBBCY", .., "GeneXXZZ") or indexes of group members (1, 2, .., N), or a data frame containing indicator variables (TRUE, FALSE, TRUE, ..) for group membership. Group names will be taken from the component list element or column names.

Examples

##
## Example using a list of item names belonging to the
## specified group.
##

## construct some fake gene names..
oneName <- function() paste(sample(LETTERS,5,replace=TRUE),collapse="")
geneNames <- replicate(1000, oneName())
                       
## 
GroupA <- sample(geneNames, 400, replace=FALSE)
GroupB <- sample(geneNames, 750, replace=FALSE)
GroupC <- sample(geneNames, 250, replace=FALSE)
GroupD <- sample(geneNames, 300, replace=FALSE)
input  <-list(GroupA,GroupB,GroupC,GroupD)
input

venn(input)


##
## Example using a list of item indexes belonging to the
## specified group.
##
GroupA.i <- which(geneNames %in% GroupA)
GroupB.i <- which(geneNames %in% GroupB)
GroupC.i <- which(geneNames %in% GroupC)
GroupD.i <- which(geneNames %in% GroupD)
input.i  <-list(A=GroupA.i,B=GroupB.i,C=GroupC.i,D=GroupD.i)
input.i

venn(input.i)

##
## Example using a data frame of indicator ('f'lag) columns
##
GroupA.f <- geneNames %in% GroupA
GroupB.f <- geneNames %in% GroupB
GroupC.f <- geneNames %in% GroupC
GroupD.f <- geneNames %in% GroupD
input.df <- data.frame(A=GroupA.f,B=GroupB.f,C=GroupC.f,D=GroupD.f)
head(input.df)
venn(input.df)

## smaller set to create empty groupings
small <- input.df[1:20,]

venn(small, simplify=FALSE) # with empty groupings
venn(small, simplify=TRUE)  # without empty groupings

## Capture group counts, but don't plot
tmp <- venn(input, show.plot=FALSE)
tmp

## Show internal binary group labels
venn(input, showSetLogicLabel=TRUE)

## Limit  universe
tmp <- venn(input, universe=geneNames[1:100])
tmp

##
## Example to determine which elements are in A and B but not in 
## C and D: first determine the universe, then form indicator columns
## and perform intersections on these.  R allows using the set operations
## directly, but some might find this approach more intuitive.
##

universe <- unique(c(GroupA,GroupB,GroupC,GroupD))
GroupA.l <-universe %in% GroupA
GroupB.l <-universe %in% GroupB
GroupC.l <-universe %in% GroupC
GroupD.l <-universe %in% GroupD

## Genes that are in GroupA and in GroupB but not in GroupD (unification
## of sets III0 and II00 in the venn diagram:
universe[GroupA.l & GroupB.l & !GroupD.l]

##
## Alternatively: construct a function to test for the pattern you want.
##
test <- function(x) (x %in% GroupA) & (x %in% GroupB) & !(x %in% GroupC)
universe[ test(universe) ]