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iBBiG: Iterative Binary Bi-clustering of Gene Sets

Daniel Gusenleitner, Eleanor A Howe, Stefan Bentink, John Quackenbush, and Aedin C Culhane

iBBiG is a bi-clustering algorithm which we apply to meta-gene set analysis of large numbers of gene expression datasets. The iterative algorithm extracts groups of phenotypes from multiple studies that are associated with similar gene sets. iBBiG does not require prior knowledge of the number or scale of clusters and allows discovery of clusters with diverse sizes. It is robust in the presence of noise and on simulated data, iBBiG outperformed commonly used clustering methods.

  • Supplementary Materials to the manuscript (pdf)
  • Contact aedin at jimmy.harvard.edu

Running iBBiG

Prepare a binary matrix. We run GSEAlm on each dataset and then discretize the p-values p<0.05 into 1 or 0. To run iBBiG bicluster analysis, source the scripts below and call the function 

## Load iBBiG library 
library(iBBiG)
#Create simulated dataset
simData<-makeArtificial()
binaryMatrix<-simData@Seeddata

# Run iBBiG biclustering
clusters<-iBBiG(binaryMatrix,nModules=8)     ## nModules is the number of clusters
plot(clusters, reorder=TRUE)
summary(clusters)

The iBBiG class is an extension of biclust and contains the slots

  • Clusterscore: Overall score of each cluster.
  • RowScorexNumber: Score for each row (geneset) in each module, this can be used to rank gene set importance to each module
  • Seeddata: Matrix of binary data (optional) not normally used except for creation of simulated data
in addition to the biclust slots
  • RowxNumber: RowScoreNumber are discretized into a binary vector. The scores are typically more useful than the binary vector, but this is included to enable easy comparision to biclust and fabia functions
  • NumberxCol: Number of covariates (columns) in each module
  • Number: Number of clusters

download

Download iBBiG R package from Bioconductor 

  
source("http://bioconductor.org/biocLite.R")
biocLite("iBBiG")