# Fit sparse biclustering and matrix-variate normal biclustering This package is called sparseBC, for "Sparse biclustering". It implements two methods:Sparse biclustering and matrix-variate normal biclustering. All are described in the paper "Sparse biclustering of tranposable data", by KM Tan and D Witten (2014), **Journal of Computational and Graphical Statistics**. The main functions are as follows: (1) sparseBC (2) matrixBC The first function, sparseBC, performs sparse biclustering. matrixBC performs matrix-variate normal biclustering. There are also cross-validation functions for tuning parameter that controls the sparsity level of the estimated mean matrix: sparseBC.BIC and matrixBC.BIC. Function that choose the number of biclusters K and R are also included for sparseBC, called sparseBC.choosekr. package ## Details ||| |:--|:--| |Package:|sparseBC| |Type:|Package| |Version:|1.1| |Date:|2015-02-09| |License:|GPL (>=2.0)| |LazyLoad:|yes| The package includes the following functions: ||| |:--|:--| |`sparseBC` :|Perform sparse biclustering| |`sparseBC.choosekr` :|Cross-validation to select the number of row and column clusters| |`sparseBC.BIC` :|Select sparsity tuning parameter for sparseBC| |`summary.sparseBC` :|Display information for the object sparseBC| |`image.sparseBC` :|Image plot for the estimated bicluster mean matrix| |`matrixBC` :|Perform matrix-variate normal biclustering| |`matrixBC.BIC` :|Select sparsity tuning parameter for matrixBC| ## Author(s) Kean Ming Tan Maintainer: Kean Ming Tan ## References KM Tan and D Witten (2014) Sparse biclustering of transposable data. **Journal of Computational and Graphical Statistics** 23(4):985-1008. ## See Also `sparseBC` `matrixBC` ## Examples ```r # An example that violates the assumption of contiguous biclusters # Create mean matrix and the data matrix #set.seed(5) #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 #mus<-d*tcrossprod(u,v) #binaryX<-(mus!=0)*1 #X<-mus+matrix(rnorm(100*50),100,50) #X<-X-mean(X) # The number of biclusters are chosen automatically # Commented out for short run-time #KR<-sparseBC.choosekr(X,1:6,1:6,0,0.1,trace=TRUE) #k<-KR$estimated_kr[1] #r<-KR$estimated_kr[2] # The value of lambda is chosen automatically #lambda<-sparseBC.BIC(X,k,r,c(0,10,20,30,40,50))$lambda # Perform sparse biclustering using the K, R, and lambda chosen #biclustering<-sparseBC(X,k,r,lambda) # Display some information on the object sparseBC #summary(biclustering) # Plot the estimated mean matrix from sparseBC #image(biclustering) ```

sparseBC-package