predict {MOFA} | R Documentation |
This function uses the factors and the corresponding weights to do data predictions.
predict(object, views = "all", factors = "all", type = c("inRange", "response", "link"))
object |
a |
views |
character vector with the view name(s), or numeric vector with the view index(es). Default is "all". |
factors |
character vector with the factor name(s) or numeric vector with the factor index(es). Default is "all". |
type |
type of prediction returned, either:
|
Matrix factorization models generate a denoised and condensed low-dimensional representation
of the data which capture the main sources of heterogeneity of the data.
Such representation can be used to do predictions (data reconstruction) and imputation (see impute
).
For mathematical details, see the Methods section of the MOFA article.
Returns a list with data predictions.
library(ggplot2) # Example on the CLL data filepath <- system.file("extdata", "CLL_model.hdf5", package = "MOFAdata") MOFA_CLL <- loadModel(filepath) # predict drug response data using all factors predictedDrugs <- predict(MOFA_CLL, view="Drugs") # predict all views using all factors (default) predictedAll <- predict(MOFA_CLL) # predict Mutation data using all factors, returning Bernoulli probabilities predictedMutations <- predict(MOFA_CLL, view="Mutations", type="response") # predict Mutation data using all factors, returning binary classes predictedMutationsBinary <- predict(MOFA_CLL, view="Mutations", type="inRange") # Compare the predictions with the true data pred <- as.numeric(predictedAll$Drugs) true <- as.numeric(getTrainData(MOFA_CLL)$Drugs) qplot(pred,true) + geom_hex(bins=100) + coord_equal() + geom_abline(intercept=0, slope=1, col="red") # Example on the scMT data filepath <- system.file("extdata", "scMT_model.hdf5", package = "MOFAdata") MOFA_scMT <- loadModel(filepath) # Predict all views using all factors (default) predictedAll <- predict(MOFA_scMT) # Compare the predictions with the true data view <- "RNA expression" pred <- as.numeric(predictedAll[[view]]) true <- as.numeric(getTrainData(MOFA_scMT)[[view]]) qplot(pred,true) + geom_hex(bins=100) + coord_equal() + geom_abline(intercept=0, slope=1, col="red")