FLOC: Performs the FLOC algorithm

In BicARE: Biclustering Analysis and Results Exploration

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)

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.

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

Author(s)

Pierre Gestraud ([e-mail])

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

BicARE documentation built on Nov. 8, 2020, 7:47 p.m.