Description Usage Arguments Details Value Author(s) References Examples
Find a given number of biclusters using the a modified version of the FLOC algorithm.
1 2 
Data 
an

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) 
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.
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 
Pierre Gestraud (pierre.gestraud@curie.fr)
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):771789, 2005
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

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