1d:T7af,# A function to measure cluster purity 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. ```r ClusterPurity(ComputedClusters, TrueClasses) ``` ## Arguments - `ComputedClusters`: Clustering assignment determined by integrative NMF algorithm - `TrueClasses`: True clustering assignment of the subjects ## Returns Cluster purity index value ## Author(s) 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 ```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$double.eps) dat3 <- dat3/max(dat3) # The function nmf.mnnals requires the samples to be on rows and variables on columns. dat <- list(dat1,dat2,dat3) fit <- nmf.mnnals(dat=dat,k=length(prop),maxiter=200,st.count=20,n.ini=15,ini.nndsvd=TRUE, seed=TRUE) ClusterPurity(ComputedClusters=fit$clusters, TrueClasses=true.cluster.assignment$cluster.id) ```

ClusterPurity function