iBBiG-package {iBBiG} | R Documentation |
iBBiG is a bi-clustering algorithm, optimized for module discovery in sparse noisy binary genomics data. We designed iBBiG to have high specificity and thereby minimize the false positive rate when discovering new classes; the iterative approach employed in iBBiG is able to discover weak signals, even if they are potentially masked by stronger ones.
Package: | iBBiG |
Type: | Package |
Version: | 0.99.1 |
Date: | 2012-03-15 |
License: | Free Artistic |
LazyLoad: | yes |
Depends: | methods |
The main functions is iBBiG. This is the biclustering algorithm.
Aedin Culhane, Daniel Gusenleitner
Maintainer: Aedin <aedin@jimmy.harvard.edu>
Daniel Gusenleitner, Eleanor A Howe, Stefan Bentink, John Quackenbush and Aedin C Culhane iBBiG: Iterative Binary Bi-clustering of Gene Sets Bioinformatics. In review.
Also see biclust
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#create simulated datasets binMat<-makeArtificial() binMat plot(binMat) res<- try(iBBiG(binMat@Seeddata, nModules=10)) plot(res) res ## Subset a cluster res[4] res[1:2] ## As iBBiG extends the class Biclust can use Biclust functions on it ## View the rows and columns of an iBBiG object ## Create a list of matrices, one for each cluster Modules<-bicluster(res@Seeddata, res) length(Modules) lapply(Modules, dim) # Or extract a list of a specific cluster M1<-bicluster(res@Seeddata, res, 1) dim(M1[[1]]) str(M1) M1[[1]][1:5,1:3]