melissa {Melissa} | R Documentation

## Cluster and impute single cell methylomes using VB

### Description

`melissa` clusters and imputes single cells based on their methylome landscape on specific genomic regions, e.g. promoters, using the Variational Bayes (VB) EM-like algorithm.

### Usage

    melissa(X, K = 3, basis = NULL, delta_0 = NULL, w = NULL,
      alpha_0 = 0.5, beta_0 = NULL, vb_max_iter = 300,
      epsilon_conv = 1e-05, is_kmeans = TRUE, vb_init_nstart = 10,
      vb_init_max_iter = 20, is_parallel = FALSE, no_cores = 3,
      is_verbose = TRUE)

### Arguments

`X` | The input data, which has to be a list of elements of length N, where N are the total number of cells. Each element in the list contains another list of length M, where M is the total number of genomic regions, e.g. promoters. Each element in the inner list is an `I X 2` matrix, where I are the total number of observations. The first column contains the input observations x (i.e. CpG locations) and the 2nd columns contains the corresponding methylation level.
`K` | Integer denoting the total number of clusters K.
`basis` | A 'basis' object. E.g. see create_basis function from BPRMeth package. If NULL, will an RBF object with 3 basis functions will be created.
`delta_0` | Parameter vector of the Dirichlet prior on the mixing proportions pi.
`w` | Optional, an Mx(D)xK array of the initial parameters, where first dimension are the genomic regions M, 2nd the number of covariates D (i.e. basis functions), and 3rd are the clusters K. If NULL, will be assigned with default values.
`alpha_0` | Hyperparameter: shape parameter for Gamma distribution. A Gamma distribution is used as prior for the precision parameter tau.
`beta_0` | Hyperparameter: rate parameter for Gamma distribution. A Gamma distribution is used as prior for the precision parameter tau.
`vb_max_iter` | Integer denoting the maximum number of VB iterations.
`epsilon_conv` | Numeric denoting the convergence threshold for VB.
`is_kmeans` | Logical, use Kmeans for initialization of model parameters.
`vb_init_nstart` | Number of VB random starts for finding better initialization.
`vb_init_max_iter` | Maximum number of mini-VB iterations.
`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 VB iterations.

### Value

An object of class `melissa` with the following elements:

- `W`: An (M+1) X K matrix with the optimized parameter values for each cluster, M are the number of basis functions. Each column of the matrix corresponds a different cluster k.

- `W_Sigma`: A list with the covariance matrices of the posterior parmateter W for each cluster k.

- `r_nk`: An (N X K) responsibility matrix of each observations being explained by a specific cluster.

- `delta`: Optimized Dirichlet paramter for the mixing proportions.

- `alpha`: Optimized shape parameter of Gamma distribution.

- `beta`: Optimized rate paramter of the Gamma distribution

- `basis`: The basis object.

- `lb`: The lower bound vector.

- `labels`: Cluster assignment labels.

- `pi_k`: Expected value of mixing proportions.

### Details

The modelling and mathematical details for clustering profiles using mean-field variational inference are explained here: http://rpubs.com/cakapourani/ . More specifically:

- For Binomial/Bernoulli observation model check: http://rpubs.com/cakapourani/vb-mixture-bpr

- For Gaussian observation model check: http://rpubs.com/cakapourani/vb-mixture-lr

### Author(s)

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

### See Also

`create_melissa_data_obj`, `partition_dataset`, `plot_melissa_profiles`, `filter_regions`

### Examples

    # Example of running Melissa on synthetic data

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

    set.seed(15)
    # Run Melissa
    melissa_obj <- melissa(X = melissa_synth_dt$met, K = 2, basis = basis_obj,
       vb_max_iter = 10, vb_init_nstart = 1, vb_init_max_iter = 5,
       is_parallel = FALSE, is_verbose = FALSE)

    # Extract mixing proportions
    print(melissa_obj$pi_k)

[Package Melissa version 1.0.0 Index]