# A function to create image plot of the consensus matrix Given the integrative NMF fit object, the function creates image plot of the consensus matrix ordered according to clusters groups. Cleaner block structure indicates stronger clusters. ```r ConsensusMatPlot(fit, rowLab = TRUE, colLab = TRUE) ``` ## Arguments - `fit`: A nmf.mnnals fit object - `rowLab`: If true row label is displayed. Default is TRUE. - `colLab`: If true column label is displayed. Default is TRUE. ## Returns Image plot of the consensus matrix ordered according to cluster groups is returned. ## Author(s) Prabhakar Chalise, Rama Raghavan, Brooke Fridley ## References Brunnet J, Tamayo P Golub, T and Mesirov J (2004) Metagene and molecular pattern discovery using matrix factorization. PNAS, 101, 4164-4169 Monti S, Tamayo P, Mesirov J and Golup T (2003). Consesus Clustering: A resampling based method for class discovery and visualization of gene expression microarray data. Machine Learning J, 52:91-118. ## Examples ```r prop <- c(0.20,0.30,0.27,0.23) effect <- 2.5 sim.D <- InterSIM(n.sample=100,cluster.sample.prop=prop,delta.methyl=effect, delta.expr=effect,delta.protein=effect,p.DMP=0.25,p.DEG=NULL,p.DEP=NULL, do.plot=FALSE, sample.cluster=TRUE, feature.cluster=TRUE) dat1 <- sim.D$dat.methyl dat2 <- sim.D$dat.expr dat3 <- sim.D$dat.protein true.cluster.assignment <- sim.D$clustering.assignment ## Make all data positive by shifting to positive direction. ## Also rescale the datasets so that they are comparable. if (!all(dat1>=0)) dat1 <- pmax(dat1 + abs(min(dat1)), .Machine$double.eps) dat1 <- dat1/max(dat1) if (!all(dat2>=0)) dat2 <- pmax(dat2 + abs(min(dat2)), .Machine$double.eps) dat2 <- dat2/max(dat2) if (!all(dat3>=0)) dat3 <- pmax(dat3 + abs(min(dat3)), .Machine$double.eps) dat3 <- dat3/max(dat3) # The function nmf.mnnals requires the samples to be on rows and variables on columns. dat <- list(dat1,dat2,dat3) fit <- nmf.mnnals(dat=dat,k=length(prop),maxiter=200,st.count=20,n.ini=15,ini.nndsvd=TRUE, seed=TRUE) ConsensusMatPlot(fit,rowLab=TRUE,colLab=TRUE) ```

ConsensusMatPlot function