DPBBM (version 0.2.5)

bbm_data_generate: bbm_data_generate

Description

This is to generate the simulation data based on Beta-bionomial mixture model

Usage

bbm_data_generate(S=3, G=50, K=3, prob=rep(1,times=3), alpha_band=c(2,6), beta_band=c(2,6), nb_mu=100,nb_size=0.2, plotf = FALSE,  max_cor=0.5)

Arguments

S

Number of samples in the simulated data

G

Number of sites in the simulated data

K

Number of clusters that exist in the simulated data

prob

the cluster weight for each cluster

alpha_band

the region used to generate the parameter of beta distribution alpha

beta_band

the region used to generate the parameter of beta distribution beta

nb_mu

alternative parametrization via mean for Negative Binomial distribution

nb_size

target for number of successful trials, or dispersion parameter (the shape parameter of the gamma mixing distribution) for Negative binomial distrition. Must be strictly positive, need not be integer.

plotf

option for whether plot the generated data according to clusters or not

max_cor

The maximized correlation allowed for the simulated data, which used to guarantee the data is not highly correlated.

Value

The function returns simulation data generated based on beta binomial mixture model

Details

The Dirichlet Process based beta-binomial mixture model clustering

References

Reference coming soon!

Examples

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) 
table(mat$gamma)
pie(mat$gamma)
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")