get_expectations: Get expectations

In MOFA2: Multi-Omics Factor Analysis v2

Description

Function to extract the expectations from the (variational) posterior distributions of a trained MOFA object.

Usage

get_expectations(object, variable, as.data.frame = FALSE)

Arguments

object	a trained MOFA object.
variable	variable name: 'Z' for factors and 'W' for weights.
as.data.frame	logical indicating whether to output the result as a long data frame, default is FALSE.

Details

Technical note: MOFA is a Bayesian model where each variable has a prior distribution and a posterior distribution. In particular, to achieve scalability we used the variational inference framework, thus true posterior distributions are replaced by approximated variational distributions. This function extracts the expectations of the variational distributions, which can be used as final point estimates to analyse the results of the model.
The priors and variational distributions of each variable are extensively described in the supplementary methods of the original paper.

Value

the output varies depending on the variable of interest:

• "Z"a matrix with dimensions (samples,factors). If as.data.frame is TRUE, a long-formatted data frame with columns (sample,factor,value)

• "W"a list of length (views) where each element is a matrix with dimensions (features,factors). If as.data.frame is TRUE, a long-formatted data frame with columns (view,feature,factor,value)

Examples

# Using an existing trained model
file <- system.file("extdata", "model.hdf5", package = "MOFA2")
model <- load_model(file)
factors <- get_expectations(model, "Z")
weights <- get_expectations(model, "W")

MOFA2 documentation built on Nov. 8, 2020, 7:28 p.m.