SilhouettePlot {IntNMF}

A function to plot silhouette width

Description

Given the integrative NMF fit object, the function creates silhouette width plot with different colors for different cluster groups.

Usage

SilhouettePlot(fit, cluster.col = NULL)

Arguments

fit

A nmf.mnnals fit object

cluster.col

Colors for the cluster groups. If NULL, default colors are used.

Value

Silhouette width plot is returned together with mean silhouette width for each group, overall silhouette width and summary statistics.

Author(s)

Prabhakar Chalise, Rama Raghavan, Brooke Fridley

References

Rousseeue PJ (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20:53-65

Examples

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)
SilhouettePlot(fit,cluster.col=NULL)