melissa_gibbs {Melissa} | R Documentation

## Gibbs sampling algorithm for Melissa model

### Description

`melissa_gibbs` 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. NOTE: that Gibbs sampling is really slow and we recommend using the VB implementation: `melissa`.

### Usage

    melissa_gibbs(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 = 1000, gibbs_burn_in = 200, 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 from BPRMeth package
`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

### Value

An object of class `melissa_gibbs`.

### Author(s)

C.A.Kapourani C.A.Kapourani@ed.ac.uk

### See Also

`melissa`, `create_melissa_data_obj`, `partition_dataset`, `filter_regions`

### Examples

    # Example of running Melissa Gibbs on synthetic data

    # Create RBF basis object with 4 RBFs
    basis_obj <- BPRMeth::create_rbf_object(M = 4)

    set.seed(15)
    # Run Melissa Gibbs
    melissa_obj <- melissa_gibbs(X = melissa_synth_dt$met, K = 2, basis = basis_obj,
       gibbs_nsim = 10, gibbs_burn_in = 5, is_parallel = FALSE, is_verbose = FALSE)

    # Extract mixing proportions
    print(melissa_obj$pi_k)

[Package Melissa version 1.0.0 Index]