# dpbbm_mc_iterations This is the Markov Chain Monte Carlo iterations for DPBBM ```r dpbbm_mc_iterations(x, size.x, m = 1, max_iter = 2000, a = 0.1, b = 1, tau = 1, sig_alpha = 25/9, sig_beta = 25/9, tau.method = "auto", debug = FALSE) ``` ## Arguments - `x`: a matrix of k for clustering, referring to IP reads in m6A seq data - `size.x`: a matrix of n for clustering, referring to the summation of IP reads and input reads in m6A seq data - `m`: a value indicating the auxiliary clusters used in DPBBM - `max_iter`: maximized iterations in DPBBM - `a`: Hyperparameter a for tau - `b`: Hyperparameter b for tau - `tau`: Prior for tau - `sig_alpha`: variation for parameter alpha of beta distribution - `sig_beta`: variation for parameter beta of beta distribution - `tau.method`: tau.method should be set to "auto" or "stable", refer to tau for detail description. - `debug`: whether DPBBM print the debug info or not. Default: FALSE ## Returns The function returns the cluster label withdrawn by DPBBM ## Details The Dirichlet Process based beta-binomial mixture model clustering ## References Reference coming soon! ## Author(s) Lin Zhang, PhD ## Examples ```r # generate a simulated dataset 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 = FALSE, max_cor=0.5) # check generated data 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. ## You are suggested to check the pre-prepared example for a quick start F=system.file("extdata", "DPBBM_example.html", package="DPBBM") browseURL(url=F) ## Alternatively # cluster_label <- dpbbm_mc_iterations(mat$k, mat$n) # # Show the clustering result. # table(cluster_label) # pie(table(mat$gamma)) # # # Compare the clustering result with the true clustering IDs. # id <- order(mat$gamma); # c <- cluster_label # r <- rainbow(3, start = 0, end = 0.3) # mat_ratio <- (mat$k+1)/(mat$n+1); # heatmap(mat_ratio[id,], Rowv = NA, Colv = NA, scale="none", # RowSideColors = as.character(cluster_label[id]), # margins = c(3,25)) ```

dpbbm_mc_iterations function