# 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

 ```1 2``` ```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 (pierre.gestraud@curie.fr)

## 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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```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 April 29, 2020, 4:56 a.m.