infer_profiles_mle {BPRMeth} | R Documentation

## Infer methylation profiles using MLE

### Description

General purpose functions for inferring latent profiles for different observation models using maximum likelihood estimation (MLE). Current observation models are: 'bernoulli', 'binomial', 'beta' or 'gaussian'. For most models we cannot obtain an analytically tractable solution, hence an optimization procedure is used. The `optim` package is used for performing optimization.

### Usage

    infer_profiles_mle(X, model = NULL, basis = NULL, H = NULL,
      lambda = 0.5, w = NULL, beta_dispersion = 5, opt_method = "CG",
      opt_itnmax = 100, is_parallel = FALSE, no_cores = NULL, ...)

### Arguments

`X` | The input data, either a `matrix` or a `list` of elements of length N, where each element is an `L X C` matrix, where L are the total number of observations. The first column contains the input observations x (i.e. CpG locations). If "binomial" model then C=3, and 2nd and 3rd columns contain total number of trials and number of successes respectively. If "bernoulli" or "gaussian" model, then C=2 containing the output y (e.g. methylation level). If "beta" model, then C=3, where 2nd column contains output y and 3rd column the dispersion parameter.
`model` | Observation model name as character string. It can be either 'bernoulli', 'binomial', 'beta' or 'gaussian'.
`basis` | A 'basis' object. E.g. see `create_basis`. If NULL, will an RBF object will be created.
`H` | Optional, design matrix of the input data X. If NULL, H will be computed inside the function.
`lambda` | The complexity penalty coefficient for ridge regression.
`w` | A vector of initial parameters (i.e. coefficients of the basis functions).
`beta_dispersion` | Dispersion parameter, only used for Beta distribution and will be the same for all observations.
`opt_method` | The optimization method to be used. See `optim` for possible methods. Default is "CG".
`opt_itnmax` | Optional argument giving the maximum number of iterations for the corresponding method. See `optim` for details.
`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.
`...` | Additional parameters.

### Value

An object of class `infer_profiles_mle_`"obs_model" with the following elements:

- `W`: An Nx(M+1) matrix with the optimized parameter values. Each row of the matrix corresponds to each element of the list X; if X is a matrix, then N = 1. The columns are of the same length as the parameter vector w (i.e. number of basis functions).

- `basis`: The basis object.

- `nll_feat`: NLL fit feature.

- `rmse_feat`: RMSE fit feature.

- `coverage_feat`: CpG coverage feature.

### Details

The beta regression model is based on alternative parameterization of the beta density in terms of the mean and dispersion parameter: https://cran.r-project.org/web/packages/betareg/ . For modelling details for Binomial/Bernoulli observation model check the paper for BPRMeth: https://academic.oup.com/bioinformatics/article/32/17/i405/2450762 .

### Author(s)

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

### See Also

`create_basis`, `infer_profiles_vb`, `infer_profiles_gibbs`, `create_region_object`

### Examples

    # Example of optimizing parameters for synthetic data using 3 RBFs
    basis <- create_rbf_object(M=3)
    out <- infer_profiles_mle(X = binomial_data, model = "binomial",
       basis = basis, is_parallel = FALSE, opt_itnmax = 10)

    #-------------------------------------

    basis <- create_rbf_object(M=3)
    out <- infer_profiles_mle(X = beta_data, model = "beta",
       basis = basis, is_parallel = FALSE, opt_itnmax = 10)

    #-------------------------------------

    basis <- create_rbf_object(M=3)
    out <- infer_profiles_mle(X = gaussian_data[[1]], model = "gaussian",
       basis = basis, is_parallel = FALSE, opt_itnmax = 10)

[Package BPRMeth version 1.6.0 Index]