BCs4vd: Robust biclustering by sparse singular value decomposition...

In s4vd: Biclustering via Sparse Singular Value Decomposition Incorporating Stability Selection

Description

The function performs biclustering of the data matrix by sparse singular value decomposition with nested stability selection.

Usage

## S4 method for signature 'matrix,BCs4vd'
biclust(x, method=BCs4vd(),
		steps = 100,
		pcerv = 0.05,
		pceru = 0.05,
		ss.thr = c(0.6,0.65),
		size = 0.632,
		gamm = 0,
		iter = 100,
		nbiclust = 10,
		merr = 10^(-4),
		cols.nc=FALSE,
		rows.nc=TRUE,
		row.overlap=TRUE,
		col.overlap=TRUE,
		row.min=4,
		col.min=4,
		pointwise=TRUE,
		start.iter=0,
		savepath=FALSE)

Arguments

x	The matrix to be clustered.
method	calls the BCs4vd() method
steps	Number of subsamples used to perform the stability selection.
pcerv	Per comparsion wise error rate to control the number of falsely selected right singular vector coefficients (columns/samples).
pceru	Per comparsion wise error rate to control the number of falsely selected left singular vector coefficients (rows/genes).
ss.thr	Range of the cutoff threshold (relative selection frequency) for the stability selection.
size	Size of the subsamples used to perform the stability selection.
gamm	Weight parameter for the adaptive LASSO, nonnegative constant (default = 0, LASSO).
iter	Maximal number of iterations to fit a single bicluster.
nbiclust	Maximal number of biclusters.
merr	Threshold to decide convergence.
cols.nc	Allow for negative correlation of columns (samples) over rows (genes).
rows.nc	Allow for negative correlation of rows (genes) over columns (samples).
row.overlap	Allow rows to overlap between biclusters.
col.overlap	Allow columns to overlap between biclusters.
row.min	Minimal number of rows.
col.min	Minimal number of columns.
pointwise	If TRUE performs a fast pointwise stability selection instead of calculating the complete stability path.
start.iter	Number of starting iterations in which the algorithm is not allowed to converge.
savepath	Saves the stability path in order plot the path with the stabpathplot function. Note that pointwise needs to be TRUE to save the path. For extreme high dimensional data sets (e.g. the lung cancer example) the resulting biclust object may exceed the available memory.

Value

Returns an object of class Biclust.

Author(s)

Martin Sill \ [e-mail]

References

Martin Sill, Sebastian Kaiser, Axel Benner and Annette Kopp-Schneider "Robust biclustering by sparse singular value decomposition incorporating stability selection", Bioinformatics, 2011

See Also

biclust, Biclust

Examples

# example data set according to the simulation study in Lee et al. 2010
# generate artifical data set and a correspondig biclust object
u <- c(10,9,8,7,6,5,4,3,rep(2,17),rep(0,75))
v <- c(10,-10,8,-8,5,-5,rep(3,5),rep(-3,5),rep(0,34))
u <- u/sqrt(sum(u^2)) 
v <- v/sqrt(sum(v^2))
d <- 50
set.seed(1)
X <- (d*u%*%t(v)) + matrix(rnorm(100*50),100,50)
params <- info <- list()
RowxNumber <- matrix(rep(FALSE,100),ncol=1)
NumberxCol <- matrix(rep(FALSE,50),nrow=1)
RowxNumber[u!=0,1] <- TRUE 
NumberxCol[1,v!=0] <- TRUE
Number <- 1
ressim <- BiclustResult(params,RowxNumber,NumberxCol,Number,info)

#perform s4vd biclustering 
ress4vd <- biclust(X,method=BCs4vd,pcerv=0.5,pceru=0.5,pointwise=FALSE,nbiclust=1,savepath=TRUE)
#perform s4vd biclustering with fast pointwise stability selection
ress4vdpw <- biclust(X,method=BCs4vd,pcerv=0.5,pceru=0.5,pointwise=TRUE,nbiclust=1)
#perform ssvd biclustering
resssvd <- biclust(X,BCssvd,K=1)
#agreement of the results with the simulated bicluster
jaccardind(ressim,ress4vd)
jaccardind(ressim,ress4vdpw)
jaccardind(ressim,resssvd)

s4vd documentation built on May 2, 2019, 4:47 p.m.