bpr_bayes_predict_wrap: Predict gene expression from methylation profiles using Gibbs...
In andreaskapou/BPRMeth-devel: Model higher-order methylation profiles

Description Usage Arguments Value See Also Examples

bpr_bayes_predict_wrap is a function that wraps all the necessary subroutines for performing predictions on gene expressions. Initially, it computes the posterior distribution, using Gibbs sampling, of the parameters of the basis functions so as to learn the methylation profiles. Then uses the learned parameters / coefficients of the basis functions as input features for performing linear regression in order to predict/regress the corresponding gene expression data.

bpr_bayes_predict_wrap(formula = NULL, x, y, model_name = "svm", w = NULL,
  basis = NULL, train_ind = NULL, train_perc = 0.7,
  fit_feature = "RMSE", cpg_dens_feat = TRUE, lambda = 1/2,
  w_0_mean = NULL, w_0_cov = NULL, gibbs_nsim = 5000,
  gibbs_burn_in = 1000, opt_method = "CG", opt_itnmax = 50,
  is_parallel = TRUE, no_cores = NULL, is_summary = TRUE)

`formula`	An object of class `formula` needed when calling the `lm` function for performing linear regression. If NULL, the SVM regression method is used.
`x`	The binomial distributed observations, which has to be a list where each element is an L x 3 dimensional matrix.
`y`	Corresponding gene expression data for each element of the list x
`model_name`	A charcter denoting the regression model.
`w`	Optional vector of initial parameter / coefficient values.
`basis`	Optional basis function object, default is `create_rbf_object`
`train_ind`	Optional vector containing the indices for the train set.
`train_perc`	Optional parameter for defining the percentage of the dataset to be used for training set, the remaining will be the test set.
`fit_feature`	Additional feature on how well the profile fits the methylation data.
`cpg_dens_feat`	Additional feature for the CpG density across the promoter region.
`lambda`	The complexity penalty coefficient for penalized regression.
`w_0_mean`	The prior mean hyperparameter for w
`w_0_cov`	The prior covariance hyperparameter for w
`gibbs_nsim`	Optional argument giving the number of simulations of the Gibbs sampler.
`gibbs_burn_in`	Optional argument giving the burn in period of the Gibbs sampler.
`opt_method`	Parameter for defining the method to be used in the optimization procedure, see `optim`.
`opt_itnmax`	Optional parameter for defining the max number of iterations of the optimization procedure, see `optim`.
`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_summary`	Logical, print the summary statistics.

An mpgex object consisting of the following elements:

bpr_optim

obs <- meth_data
y   <- gex_data
basis <- create_rbf_object(M = 5)
out   <- bpr_bayes_predict_wrap(x = obs, y = y, basis = basis,
                                is_parallel = FALSE,
                                gibbs_nsim = 20, gibbs_burn_in = 10)