predict {MOFA} | R Documentation

## Do predictions using a fitted MOFA model

### Description

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

### Usage

    predict(object, views = "all", factors = "all", type = c("inRange",
      "response", "link"))

### Arguments

`object` | a `MOFAmodel` object.
`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: response: gives the response vector, the mean for Gaussian and Poisson, and success probabilities for Bernoulli. link: gives the linear predictions. inRange: rounds the fitted values of integer-valued distributions (Poisson and Bernoulli) to the next integer. This is the default option.

### Details

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.

### Value

Returns a list with data predictions.

### Examples

    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")

[Package MOFA version 1.2.0 Index]