| isa.biclust {isa2} | R Documentation |
This function converts the object with ISA modules to a Biclust
object, so all the functions in the biclust package can be used
on it.
isa.biclust(modules)
modules |
The ISA modules, as returned by the isa or some
other function. |
biclust is an R package that implements many biclustering
algorithms in a unified framework. This function converts a set of ISA
biclusters to a Biclust object, this class is used to store all
biclustering results by the biclust package.
The Biclust class only supports binary biclusters, so the ISA
modules are binarized during the conversion.
A Biclust object.
Gabor Csardi Gabor.Csardi@unil.ch
## You need the biclust package for this
if (require(biclust)) {
set.seed(1)
data <- isa.in.silico()
modules <- isa(data[[1]])
bc <- isa.biclust(modules)
## A heatmap
drawHeatmap(data[[1]], bc, 1)
## A "bubble" plot
bubbleplot(data[[1]], bc)
## Compare values inside and outside the bicluster
plotclust(bc, data[[1]])
## Plot profiles of bicluster elements
parallelCoordinates(data[[1]], bc, number=1)
## Coherence measures vs. ISA robustness
cV <- sapply(seq(bc@Number), function(x)
constantVariance(data[[1]], bc, x, dimension="both"))
aV <- sapply(seq(bc@Number), function(x)
additiveVariance(data[[1]], bc, x, dimension="both"))
mV <- sapply(seq(bc@Number), function(x)
multiplicativeVariance(data[[1]], bc, x, dimension="both"))
sV <- sapply(seq(bc@Number), function(x)
signVariance(data[[1]], bc, x, dimension="both"))
rob <- robustness(isa.normalize(data[[1]]), modules$rows,
modules$columns)
cor( cbind(cV, aV, mV, sV, rob) )
}