Getting started with DPBBM pcakge"

In DPBBM: Dirichlet Process Beta-Binomial Mixture

Load the package and generate a dataset.

library(DPBBM)
set.seed(123455)
S <- 4
G <- 100
K <- 3
nb_mu <- 100
nb_size <- 0.8
prob <- c(1,1,1)
mat <- bbm_data_generate(S=S,G=G,K=K,prob=prob,alpha_band=c(2,6),beta_band=c(2,6),
                         nb_mu=nb_mu,nb_size=nb_size, plotf = TRUE, max_cor=0.5) 

check the generated data. The color on the left shows the true clustering IDs of the site.

id <- order(mat$gamma);
c <- mat$gamma[id]
mat_ratio <- (mat$k+1)/(mat$n+1);
heatmap(mat_ratio[id,], Rowv = NA, Colv = NA, scale="none", RowSideColors=as.character(c), 
        xlab = "4 samples", ylab="100 RNA methylation sites")

Run the DPBBM result. This step takes a really long time.

cluster_label <- dpbbm_mc_iterations(mat$k, mat$n)

Show the cluster sizes.

table(cluster_label)
table(mat$gamma)

Compare the clustering result with the true clustering IDs.

id <- order(mat$gamma);
c <- cluster_label;
mat_ratio <- (mat$k+1)/(mat$n+1);
heatmap(mat_ratio[id,], Rowv = NA, Colv = NA, scale="none",
          RowSideColors = as.character(cluster_label[id]), 
          xlab = "4 samples", ylab="100 RNA methylation sites")

As is shown, clustering results are consistent for most of the sites, but there exist a few misclassied sites as well.

DPBBM documentation built on May 1, 2019, 10:25 p.m.