ISA-Biclust conversion {eisa} | R Documentation

## Convert ISA modules to a Biclust object, or the opposite

### Description

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.

### Usage

    annotate(biclusters, data)

### Arguments

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

### Details

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

        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:

        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.

### Value

`annotate` returns a `Biclust` object.

### Author(s)

Gabor Csardi csardi.gabor@gmail.com

### References

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

### Examples

    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

    }

[Package eisa version 1.36.0 Index]