predict: Do predictions using a fitted MOFA

In bioFAM/MOFA2: Multi-Omics Factor Analysis v2

Do predictions using a fitted MOFA

Description

This function uses the latent factors and the weights to do data predictions.

Usage

predict(
  object,
  views = "all",
  groups = "all",
  factors = "all",
  add_intercept = TRUE
)

Arguments

object	a MOFA object.
views	character vector with the view name(s), or numeric vector with the view index(es). Default is "all".
groups	character vector with the group name(s), or numeric vector with the group index(es). Default is "all".
factors	character vector with the factor name(s) or numeric vector with the factor index(es). Default is "all".
add_intercept	add feature intercepts to the prediction (default is TRUE).

Details

MOFA generates a denoised and condensed low-dimensional representation of the data that captures the main sources of heterogeneity of the data. This representation can be used to reconstruct a denoised representation of the data, simply using the equation Y = WX. For more mathematical details read the supplementary methods of the manuscript.

Value

Returns a list with the data reconstructed by the model predictions.

Examples

# Using an existing trained model on simulated data
file <- system.file("extdata", "model.hdf5", package = "MOFA2")
model <- load_model(file)

# Predict observations for all data modalities
predictions <- predict(model)

bioFAM/MOFA2 documentation built on Feb. 1, 2024, 6:41 a.m.