iBBiG-package {iBBiG} | R Documentation

## iBBiG performs bi-clustering of binary matrices

### Description

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.

### Details

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.

### Author(s)

Aedin Culhane, Daniel Gusenleitner

Maintainer: Aedin <aedin@jimmy.harvard.edu>

### References

Daniel Gusenleitner, Eleanor A Howe, Stefan Bentink, John Quackenbush and Aedin C Culhane iBBiG: Iterative Binary Bi-clustering of Gene Sets Bioinformatics. In review.

### See Also

Also see `biclust` ~~

### Examples

    #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]

[Package iBBiG version 1.36.0 Index]