sc_bayes_bpr_fdmm_old: Gibbs sampling algorithm for sc-Bayesian BPR finite mixture...
In andreaskapou/BPRMeth-devel: Model higher-order methylation profiles

Description Usage Arguments

sc_bayes_bpr_fdmm implements the Gibbs sampling algorithm for performing clustering of single cells based on their DNA methylation profiles, where the observation model is the Bernoulli distributed Probit Regression likelihood.

sc_bayes_bpr_fdmm_old(x, K = 2, pi_k = rep(1/K, K), w = NULL,
  basis = NULL, w_0_mean = NULL, w_0_cov = NULL, dir_a = rep(1/K, K),
  lambda = 1/2, gibbs_nsim = 5000, gibbs_burn_in = 1000,
  inner_gibbs = FALSE, gibbs_inner_nsim = 50, is_parallel = TRUE,
  no_cores = NULL, is_verbose = TRUE)

`x`	A list of length I, where I are the total number of cells. Each element of the list contains another list of length N, where N is the total number of genomic regions. Each element of the inner list is an L x 2 matrix of observations, where 1st column contains the locations and the 2nd column contains the methylation level of the corresponding CpGs.
`K`	Integer denoting the number of clusters K.
`pi_k`	Vector of length K, denoting the mixing proportions.
`w`	A N x M x K array, where each column contains the basis function coefficients for the corresponding cluster.
`basis`	A 'basis' object. E.g. see `create_rbf_object`
`w_0_mean`	The prior mean hyperparameter for w
`w_0_cov`	The prior covariance hyperparameter for w
`dir_a`	The Dirichlet concentration parameter, prior over pi_k
`lambda`	The complexity penalty coefficient for penalized regression.
`gibbs_nsim`	Argument giving the number of simulations of the Gibbs sampler.
`gibbs_burn_in`	Argument giving the burn in period of the Gibbs sampler.
`inner_gibbs`	Logical, indicating if we should perform Gibbs sampling to sample from the augmented BPR model.
`gibbs_inner_nsim`	Number of inner Gibbs simulations.
`is_parallel`	Logical, indicating if code should be run in parallel.
`no_cores`	Number of cores to be used, default is max_no_cores - 1.
`is_verbose`	Logical, print results during EM iterations