ConsensusMatPlot: A function to create image plot of the consensus matrix

Description Usage Arguments Value Author(s) References Examples

Description

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.

Usage

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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.

Value

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

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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)
 


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