makeArtificial {iBBiG} | R Documentation

## Create a 400x400 simulated binary matrix for testing iBBiG and other binary biclustering methods

### Description

Create a binary matrix of 400 rows x 400 columns, where 1 is a positive association. This matrix is seeded with 7 modules of various size and with various levels of noise as described by Gusenleitner et al.,

### Usage

    makeArtificial(nRow = 400, nCol = 400, noise = 0.1, verbose = TRUE, dM = makeSimDesignMat(verbose = verbose), seed=123)

### Arguments

`nRow` | Numeric `nRow` number of rows
`nCol` | Numeric `nRow` number of columns
`noise` | Numeric. Value between 0-1. Default is 10 percent random noise (1) introduced into the spare binary matrix
`verbose` | Verbose output. Default is TRUE
`dM` | A design matrix specifying where the columns are. The function makeSimDesignMat create the matrix which specifies the design matrix
`seed` | Integer, passed to function set.seed() the random-number generator function, so that the articical simulated data is reproduced. If you wish to generate a random simulated data set use seed=NULL

### Details

See Guesnleitner et al, for more information

### Value

Output is a class of Biclust.

### Author(s)

Aedin Culhane, Daniel Gusenleitner

### 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

Further functions for viewing and clustering binaray data are available in the package biclust. We have written iBBiG and its classes so that it is compatible with biclust, and the class iBBiG inherits Biclust-class.

### Examples

    ##---- Should be DIRECTLY executable !! ----
    ##-- ==>  Define data, use random,
    ##--	or do  help(data=index)  for the standard data sets.

    ## The function is currently defined as
    arti<-makeArtificial()
    plot(arti)

[Package iBBiG version 1.30.0 Index]