%>%                     Re-exporting the pipe operator See
                        'magrittr::%>%' for details.
MOFA                    Class to store a mofa model
add_mofa_factors_to_seurat
                        Function to add the MOFA representation onto a
                        Seurat object
calculate_contribution_scores
                        Calculate contribution scores for each view in
                        each sample
calculate_variance_explained
                        Calculate variance explained by the model
calculate_variance_explained_per_sample
                        Calculate variance explained by the MOFA
                        factors for each sample
cluster_samples         K-means clustering on samples based on latent
                        factors
compare_elbo            Compare different trained 'MOFA' objects in
                        terms of the final value of the ELBO statistics
                        and number of inferred factors
compare_factors         Plot the correlation of factors between
                        different models
correlate_factors_with_covariates
                        Plot correlation of factors with external
                        covariates
covariates_names        covariates_names: set and retrieve covariate
                        names
create_mofa             create a MOFA object
create_mofa_from_MultiAssayExperiment
                        create a MOFA object from a
                        MultiAssayExperiment object
create_mofa_from_Seurat
                        create a MOFA object from a Seurat object
create_mofa_from_SingleCellExperiment
                        create a MOFA object from a
                        SingleCellExperiment object
create_mofa_from_df     create a MOFA object from a data.frame object
create_mofa_from_matrix
                        create a MOFA object from a a list of matrices
factors_names           factors_names: set and retrieve factor names
features_metadata       features_metadata: set and retrieve feature
                        metadata
features_names          features_names: set and retrieve feature names
get_covariates          Get sample covariates
get_data                Get data
get_default_data_options
                        Get default data options
get_default_mefisto_options
                        Get default options for MEFISTO covariates
get_default_model_options
                        Get default model options
get_default_stochastic_options
                        Get default stochastic options
get_default_training_options
                        Get default training options
get_dimensions          Get dimensions
get_elbo                Get ELBO
get_expectations        Get expectations
get_factors             Get factors
get_group_kernel        Get group covariance matrix
get_imputed_data        Get imputed data
get_interpolated_factors
                        Get interpolated factor values
get_lengthscales        Get lengthscales
get_scales              Get scales
get_variance_explained
                        Get variance explained values
get_weights             Get weights
groups_names            groups_names: set and retrieve group names
impute                  Impute missing values from a fitted MOFA
interpolate_factors     Interpolate factors in MEFISTO based on new
                        covariate values
load_model              Load a trained MOFA
make_example_data       Simulate a data set using the generative model
                        of MOFA
plot_alignment          Plot covariate alignment acorss groups
plot_ascii_data         Visualize the structure of the data in the
                        terminal
plot_data_heatmap       Plot heatmap of relevant features
plot_data_overview      Overview of the input data
plot_data_scatter       Scatterplots of feature values against latent
                        factors
plot_data_vs_cov        Scatterplots of feature values against sample
                        covariates
plot_dimred             Plot dimensionality reduction based on MOFA
                        factors
plot_enrichment         Plot output of gene set Enrichment Analysis
plot_enrichment_detailed
                        Plot detailed output of the Feature Set
                        Enrichment Analysis
plot_enrichment_heatmap
                        Heatmap of Feature Set Enrichment Analysis
                        results
plot_factor             Beeswarm plot of factor values
plot_factor_cor         Plot correlation matrix between latent factors
plot_factors            Scatterplots of two factor values
plot_factors_vs_cov     Scatterplots of a factor's values againt the
                        sample covariates
plot_group_kernel       Heatmap plot showing the group-group
                        correlations per factor
plot_interpolation_vs_covariate
                        Plot interpolated factors versus covariate
                        (1-dimensional)
plot_sharedness         Barplot showing the sharedness per factor
plot_smoothness         Barplot showing the smoothness per factor
plot_top_weights        Plot top weights
plot_variance_explained
                        Plot variance explained by the model
plot_variance_explained_by_covariates
                        Plot variance explained by the smooth
                        components of the model
plot_variance_explained_per_feature
                        Plot variance explained by the model for a set
                        of features Returns a tile plot with a group on
                        the X axis and a feature along the Y axis
plot_weights            Plot distribution of feature weights (weights)
plot_weights_heatmap    Plot heatmap of the weights
plot_weights_scatter    Scatterplots of weights
predict                 Do predictions using a fitted MOFA
prepare_mofa            Prepare a MOFA for training
run_enrichment          Run feature set Enrichment Analysis
run_mofa                Train a MOFA model
run_tsne                Run t-SNE on the MOFA factors
run_umap                Run UMAP on the MOFA factors
samples_metadata        samples_metadata: retrieve sample metadata
samples_names           samples_names: set and retrieve sample names
select_model            Select a model from a list of trained 'MOFA'
                        objects based on the best ELBO value
set_covariates          Add covariates to a MOFA model
subset_factors          Subset factors
subset_features         Subset features
subset_groups           Subset groups
subset_samples          Subset samples
subset_views            Subset views
summarise_factors       Summarise factor values using external groups
views_names             views_names: set and retrieve view names
