eisa.biclust: Convert ISA modules to a Biclust object, or the opposite
In eisa: Expression data analysis via the Iterative Signature Algorithm

Description Usage Arguments Details Value Author(s) References Examples

The biclust package implements several biclustering algorithms in a unified framework. The result of the biclustering is a Biclust object. These functions allow the conversion between Biclust and ISAModules objects.

1	annotate(biclusters, data)

`biclusters`	A `Biclust` object.
`data`	An `ExpressionSet` object.

To convert an ISAModules object (mods) to a Biclust object (bc), you can do:

1 2	bc <- as(mods, "Biclust")

The seed data and run data of the ISAModules object is stored in the Parameters slot of the Biclust object. The ISA scores are binarized by the conversion.

To convert a Biclust object (bc) to an ISAModules object (mods), you can call:

1 2	mods <- as(bc, "ISAModules")

The Parameters slot of the Biclust object is used as the run data of the ISAModules object. The seed data of the new object will be an empty data frame.

The annotate function puts biological annotation into a Biclust object. It is suggested to use it before converting the Biclust object to ISAModules, so that ISA visualization functions and enrichment calculations can make use of this information.

annotate returns a Biclust object.

Gabor Csardi csardi.gabor@gmail.com

Bergmann S, Ihmels J, Barkai N: Iterative signature algorithm for the analysis of large-scale gene expression data Phys Rev E Stat Nonlin Soft Matter Phys. 2003 Mar;67(3 Pt 1):031902. Epub 2003 Mar 11.

Sebastian Kaiser, Rodrigo Santamaria, Roberto Theron, Luis Quintales and Friedrich Leisch. (2009). biclust: BiCluster Algorithms. R package version 0.8.1. http://CRAN.R-project.org/package=biclust

if (require(biclust)) {

  library(ALL)
  data(ALL)
  ALL.filtered <- ALL[sample(1:nrow(ALL), 1000),]

  # Biclust -> ISAModules
  set.seed(1)
  Bc <- biclust(exprs(ALL.filtered), BCPlaid(),
                fit.model = ~m + a + b, verbose = FALSE)
  Bc <- annotate(Bc, ALL.filtered)
  modules <- as(Bc, "ISAModules")
  Bc
  modules
  getNoFeatures(modules)
  getNoSamples(modules)

  # ISAModules -> Biclust
  data(ALLModulesSmall)
  Bc2 <- as(ALLModulesSmall, "Biclust")
  ALLModulesSmall
  getNoFeatures(ALLModulesSmall)
  getNoSamples(ALLModulesSmall)
  Bc2

}