MOFAmodel: Class to store a Multi-Omics Factor Analysis (MOFA) model
In MOFA: Multi-Omics Factor Analysis (MOFA)

The MOFAmodel is an S4 class used to store all relevant data to analyse a MOFA model.

InputData: the input data before being parsed to Training Data. Either a MultiAssayExperiment object or a list of matrices, one per view.
TrainData: the parsed data used to fit the MOFA model A list with one matrix per view.
ImputedData: the parsed data with the missing values imputed using the MOFA model. A list with one matrix per view.
Expectations: expected values of the different variables of the model. A list of matrices, one per variable. The most relevant are "W" for weights and "Z" for factors.
TrainStats: list with training statistics such as evidence lower bound (ELBO), number of active factors, etc.
DataOptions: list with the data processing options such as whether to center or scale the data.
TrainOptions: list with the training options such as maximum number of iterations, tolerance for convergence, etc.
ModelOptions: list with the model options such as likelihoods, number of factors, etc.
FeatureIntercepts: list with the feature-wise intercepts. Only used internally.
Dimensions: list with the relevant dimensionalities of the model. N for the number of samples, M for the number of views, D for the number of features of each view and K for the number of infered latent factors.
Status: Auxiliary variable indicating whether the model has been trained.