sc_bayes_bpr_fdmm: Gibbs sampling algorithm for sc-Bayesian BPR finite mixture...

Description Usage Arguments

Description

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.

Usage

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sc_bayes_bpr_fdmm(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), 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 = FALSE)

Arguments

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


andreaskapou/BPRMeth-devel documentation built on May 12, 2019, 3:32 a.m.