# Biclustering via sparse singular value decomposition The function performs a biclustering of the data matrix by sparse singular value decomposition. ```r ## S4 method for signature 'matrix,BCssvd' biclust(x,method=BCssvd(), K=10, threu = 1, threv = 1, gamu = 0, gamv =0, u0 = svd(X)$u[,1], v0 = svd(X)$v[,1], merr = 10^(-4), niter = 100) ``` ## Arguments - `x`: the matrix to be clustered - `method`: calls the BCssvd() method - `K`: number of SSVD-layers - `threu`: type of penalty (thresholding rule) for the left singular vector, 1 = (Adaptive) LASSO (default) 2 = hard thresholding - `threv`: type of penalty (thresholding rule) for the right singular vector, 1 = (Adaptive) LASSO (default) 2 = hard thresholding - `gamu`: weight parameter in Adaptive LASSO for the left singular vector, nonnegative constant (default = 0, LASSO) - `gamv`: weight parameter in Adaptive LASSO for the right singular vector, nonnegative constant (default = 0, LASSO) - `u0`: initial left singular vector - `v0`: initial right singular vector - `merr`: threshold to decide convergence - `niter`: maximum number of iterations ## Returns Returns an Biclust object. ## References Mihee Lee, Haipeng Shen, Jianhua Z. Huang and J. S. Marron1 "Biclustering via Sparse Singular Value Decomposition", Biometrics, 2010 ## Author(s) Adaptation of original code from Mihee Lee by Martin Sill \ m.sill@dkfz.de ## See Also `biclust`, `Biclust` ## Examples ```r # 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) ```

BCssvd function