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MOFAmodel object

MOFAmodel it is the main S4 class used to store all relevant data to analyse a MOFA model. Its slots are the following (accessible using @ or the correspon):

  • InputData: input data, either a list of matrices or a MultiAssayExperiment
  • TrainData: training data, a list of matrices with processed data (centered, scaled, etc.)
  • TrainOptions: training options
  • DataOptions: data processing options
  • ModelOptions: model options
  • TrainStats: training statistics
  • Expectations: expectations of the different random variables
  • Status: trained/untrained
  • Dimensions: Number of views (M), samples (N), features per view (D) and factors (K)
  • ImputedData: imputed data (filled by running impute on the object)

List of relevant functions

Prepare and run MOFA

  • createMOFAobject: first function to create an untrained MOFA model from input multi-omics data
  • prepareMOFA: prepare an untrained MOFA, always run it after createMOFAobject and before runMOFA
  • runMOFA: function to train an untrained MOFA model. This calls the Python framework
  • loadModel: load a trained MOFA model from an hdf5 file stored in disk

get functions

  • factorNames: get or set factor names
  • featureNames: get or set feature names
  • sampleNames: get or set sample names
  • viewNames: get or set view names
  • getDimensions: get dimensions (number of samples, features, etc.)
  • getFactors: get model factors
  • getWeights: get model weights
  • getTrainData: get training data
  • getImputedData: get imputed data

Disentangle sources of variation

  • plotVarianceExplained: plot the variance explained by each factor in each view. This is the key plot of MOFA and should always be done before inspecting factors or weights.
  • calculateVarianceExplained: calculate and return the variance explained by each factor in each view.

Inspect loadings

  • plotTopWeights: plot the top loadings for a given factor and view
  • plotWeights: plot all loadings for a given factor and view
  • plotWeightsHeatmap: plot a heatmap of the loadings from multiple factors in a given view

Inspect factors

  • plotFactorCor: correlation plot between factors. Ideally, they should be uncorrelated
  • plotFactorScatter: scatterplot between two factors, this is similar to doing a PCA plot
  • plotFactorScatters: pairwise combination of scatterplots between multiple factors
  • plotFactorBeeswarm: beeswarm plot for a single factor

Inspect the data

  • plotDataOverview: plot overview of the input data, including the number of samples, views, features, and the missing assays.
  • plotDataHeatmap: heatmap of the training data using only top features for a given factor. This is very useful to map the factors and features back to the original data
  • plotDataScatter: scatterplot of the data using only top features for a given factor

Feature set enrichment analysis

  • runEnrichmentAnalysis: do feature set enrichment analysis. Takes a bit amount of options, check the example on the vignette
  • plotEnrichment: plot the top most enriched feature sets per factor
  • plotEnrichmentBars: plot the number of enriched feature sets per factor as a barplot

Clustering

  • clusterSamples: k-means clustering of samples on the factor space

Compare and select models

  • compareModels: compare MOFAmodel objects from multiple runs in terms of number of factors and ELBO statistics (for model selection)
  • compareFactors: compare MOFAmodel objects from multiple runs in terms of their factors
  • selectModel: select the best MOFAmodel object from multiple MOFAmodel objects based on the ELBO

Predictions and imputation

  • prediction: predict observations
  • impute: impute missing data

Subset

  • subsetSamples: subset samples
  • subsetViews: subset views
  • subsetFactors: subset factors

Examples

  • makeExampleData: make example MOFAmodel object with simulated data