IntNMF (version 1.3.0)

ClusterPurity: A function to measure cluster purity

Description

Given the true clustering assignment for the subjects, this function calculates cluster purity index comparing with clustering assignment determined by integrative NMF algorithm. Higher value of cluster purity indicates better cluster predictive discrimination.

Usage

ClusterPurity(ComputedClusters, TrueClasses)

Value

Cluster purity index value

Arguments

ComputedClusters

Clustering assignment determined by integrative NMF algorithm

TrueClasses

True clustering assignment of the subjects

Author

Prabhakar Chalise, Rama Raghavan, Brooke Fridley

References

Kim Hyunsoo and Park Haesun (2007). Sparse non-negative matrix factorization via alternating non-negativity constrained least squares for microarray data analysis. Bioinformatics, 23: 1495-1502.

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)
ClusterPurity(ComputedClusters=fit$clusters, TrueClasses=true.cluster.assignment$cluster.id)