## Simulate toy data, n = 300 observations
 set.seed(12345)
 countmat <- matrix(runif(300*4, min=0, max=500), nrow=300, ncol=4)
 countmat <- countmat[which(rowSums(countmat) > 0),]
 profiles <- transformRNAseq(countmat, norm="none",
                             transformation="arcsin")$tcounts
 
 conds <- rep(c("A","B","C","D"), each=2)
 
 ## Run the Normal mixture model for K = 2,3
 ## Object of class coseqResults
 run <- NormMixClus(y=profiles, K=2:3, iter=5)
 run
 
 ## Run the Normal mixture model for K=2
 ## Object of class SummarizedExperiment0
 run2 <- NormMixClusK(y=profiles, K=2, iter=5)
 
 ## Summary of results
 summary(run)
 
 ## Re-estimate mixture parameters for the model with K=2 clusters
 param <- NormMixParam(run, y_profiles=profiles)