BicARE (version 1.30.0)

FLOC: Performs the FLOC algorithm

Description

Find a given number of biclusters using the a modified version of the FLOC algorithm.

Usage

FLOC(Data, k = 20, pGene = 0.5, pSample=pGene, r = NULL, N = 8, M
= 6, t = 500, blocGene = NULL, blocSample = NULL)

Arguments

Data

an ExpressionSet or a matrix (with genes on rows and conditions on columns)

k

the number of biclusters searched

pGene

genes initial probability of membership to the biclusters

pSample

samples initial probability of membership to the biclusters

r

the residue threshold

N

minimal number of gene per bicluster

M

minimal number of conditions per bicluster

t

number of iterations

blocGene

a matrix indicating the directed initialisation for the genes (see details)

blocSample

a matrix indicating the directed initialisation for the conditions (see details)

Value

Returns an object of class 'biclustering', a list containing at least :

Call

the matched call.

ExpressionSet

the data used

param

a data.frame with the algorithm parameters

bicRow

a matrix of boolean indicating the belonging of the genes to the biclusters

bicCol

the same as for bicRow but for the conditions

mat.resvol.bic

a matrix describing the biclusters

Details

This biclustering algorithm is based on the FLOC algorithm (FLexible Overlapped biClustering) defined by Yang et al. (see references). It can discover a set of k, possibly overlapping, biclusters. If r is set to NULL, the residue threshold used in the analysis is the residue of Data divided by 10. blocGene and blocSample are matrix of 0 and 1 with the rows representing the features (gene or samples) and the columns the biclusters. A 1 on line i and column j indicates that the feature i (gene or sample) will be include in the bicluster j during the initialisation step and will not be removed from it during the analysis. If the number of columns in these matrices is different from the number of bicluster searched, k is set to the maximal value of these two. See bicluster to extract a bicluster from the biclustering result.

References

J. Yang, H. Wang, W. Wang, and P.S. Yu. An improved biclustering method for analyzing gene expression. International Journal on Artificial Intelligence Tools, 14(5):771-789, 2005

Examples

data(sample.bicData)     ## subset of sample.ExpressionSet from Biobase
residue(sample.bicData)  ##  0.3401921
resBic <- FLOC(sample.bicData, k=10, pGene=0.5,r=0.05,N=8,M=10,t=500)
resBic

## initialising samples of 2 biclusters
iniSample <- matrix(0, ncol=2, nrow=26)
## first bicluster initialised around Female cases
iniSample[pData(sample.bicData)$sex=="Female",1] <- 1
## second bicluster initialised around control cases
iniSample[pData(sample.bicData)$type=="Control",2] <- 1
resBic <- FLOC(sample.bicData, k=10, pGene=0.5, r=0.05, N=8, M=10, t=500, blocSample=iniSample)
resBic