Venn Diagrams
Compute classification counts and draw a Venn diagram.
vennCounts(x, include="both")
vennDiagram(object, include="both", names=NULL, mar=rep(1,4), cex=c(1.5,1,0.7), lwd=1,
circle.col=NULL, counts.col=NULL, show.include=NULL, ...)x |
a |
object |
either a |
include |
character vector specifying whether all differentially expressed genes should be counted, or whether the counts should be restricted to genes changing in a certain direction. Choices are |
names |
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 |
cex |
numerical vector of length 3 giving scaling factors for large, medium and small text on the plot. |
lwd |
numerical value giving the amount by which the circles should be scaled on the plot. See |
circle.col |
vector of colors for the circles. See |
counts.col |
vector of colors for the counts. Of same length as |
show.include |
logical value whether the value of |
... |
any other arguments are passed to |
Each column of x corresponds to a contrast or set, and the entries of x indicate membership of each row in each set or alternatively the significance of each row for each contrast.
In the latter case, the entries can be negative as well as positive to indicate the direction of change.
vennCounts can collate intersection counts for any number of sets.
vennDiagram can plot up to five sets.
vennCounts produces an object of class "VennCounts".
This contains only one slot, which is numerical matrix with 2^ncol{x} rows and ncol(x)+1 columns.
Each row corresponds to a particular combination of set memberships.
The first ncol{x} columns of output contain 1 or 0 indicating membership or not in each set.
The last column called "Counts" gives the number of rows of x corresponding to that combination of memberships.
vennDiagram produces no output but causes a plot to be produced on the current graphical device.
Gordon Smyth, James Wettenhall, Francois Pepin, Steffen Moeller and Yifang Hu
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)
mfrow.old <- par()$mfrow
par(mfrow=c(1,2))
vennDiagram(a)
vennDiagram(results,
include=c("up", "down"),
counts.col=c("red", "blue"),
circle.col = c("red", "blue", "green3"))
par(mfrow=mfrow.old)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.