DPBBM-package: DPBBM

In DPBBM: Dirichlet Process Beta-Binomial Mixture

Description

This package is developed for the beta-binomial mixture model based clustering

Details

Package:	DPBBM
Type:	Package
Version:	1.0.1
Date:	2016-06-26
License:	GPL-2

References

Coming soon!

Examples

# Please check the main function of the package
?dpbbm_mc_iterations 

Example output

dpbbm_mc_iterations           package:DPBBM            R Documentation

_d_p_b_b_m__m_c__i_t_e_r_a_t_i_o_n_s

_D_e_s_c_r_i_p_t_i_o_n:

     This is the Markov Chain Monte Carlo iterations for DPBBM

_U_s_a_g_e:

     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)
     
_A_r_g_u_m_e_n_t_s:

       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

_D_e_t_a_i_l_s:

     The Dirichlet Process based beta-binomial mixture model clustering

_V_a_l_u_e:

     The function returns the cluster label withdrawn by DPBBM

_A_u_t_h_o_r(_s):

     Lin Zhang, PhD <[e-mail]>

_R_e_f_e_r_e_n_c_e_s:

     Reference coming soon!

_E_x_a_m_p_l_e_s:

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