bpr_diff_test_wrap: Perform likelihood ratio test between methylation samples

In andreaskapou/BPRMeth-devel: Model higher-order methylation profiles

Description

bpr_diff_test_wrap is a function that wraps all the necessary subroutines for performing statistical testing between methylation samples using the likelihood ratio test.

Usage

bpr_diff_test_wrap(x, w = NULL, basis = NULL, opt_method = "CG",
  opt_itnmax = 100, is_parallel = TRUE, no_cores = NULL)

Arguments

x	The binomial distributed observations. A list containing two lists for control and treatment samples. Each list has elements of length N, where each element is an L x 3 matrix of observations, where 1st column contains the locations. The 2nd and 3rd columns contain the total reads and number of successes at the corresponding locations, repsectively. See process_haib_caltech_wrap on a possible way to get this data structure.
w	Optional vector of initial parameter / coefficient values.
basis	Optional basis function object, default is an 'rbf' object, see create_rbf_object.
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 - 2.

Value

A 'bpr_test' object which, in addition to the input parameters, consists of the following variables:

• W_opt: An Nx(2M+2) matrix with the optimized parameter values. Each row of the matrix corresponds to the concatenated coefficients of the methylation profiles from both samples. The columns are of the same length as the concatenated parameter vector [w_contr, w_treat] (i.e. number of basis functions).

• Mus: A list containing two matrices of size N x M with the RBF centers for each sample, if basis object is create_rbf_object, otherwise NULL.

• train: The training data.

• test: The test data.

• gex_model: The fitted regression model.

• train_pred The predicted values for the training data.

• test_pred The predicted values for the test data.

• train_errors: The training error metrics.

• test_errors: The test error metrics.

Author(s)

C.A.Kapourani [e-mail]

See Also

bpr_optimize, create_basis, eval_functions, train_model_gex, predict_model_gex

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