venn {limma} | R Documentation |
Compute classification counts or plot classification counts in a Venn diagram.
vennCounts(x, include="both") vennDiagram(object, include="both", names, mar=rep(1,4), cex=1.5, lwd=1, circle.col, counts.col, show.include, ...)
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. See details.
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 . |
lwd |
numerical value giving the amount by which the circles should be scaled on the plot. See par . |
circle.col |
optional vector of color specifications defining the colors by which the circles should be drawn. See par . |
counts.col |
optional vector of color specifications defining the colors by which the circles should be drawn. See par . |
show.include |
logical value whether the value of include
should be printed on the plot. Defaults to FALSE if include is a single value and TRUE otherwise |
... |
any other arguments are passed to plot |
If a vennCounts
object is given to vennDiagram
, the include
parameter is ignored. If a TestResults
object is given, then it is possible to set include
as a vector of 2 character strings and both will be shown.
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.
Gordon Smyth James Wettenhall and Francois Pepin
An overview of linear model functions in limma is given by 06.LinearModels.
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) vennDiagram(results,include=c("up","down"),counts.col=c("red","green"))