# Multi-Omics Factor Analysis v2

## Documentation for package ‘MOFA2’ version 1.23.1

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package README

## Help Pages

A C F G I L M P R S V misc

## -- A --

add_mofa_factors_to_seurat | Function to add the MOFA representation onto a Seurat object

## -- C --

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-method | covariates_names: set and retrieve covariate names
covariates_names | covariates_names: set and retrieve covariate names
covariates_names-method | covariates_names: set and retrieve covariate names
covariates_names<- | covariates_names: set and retrieve covariate names
covariates_names<--method | covariates_names: set and retrieve covariate names
create_mofa | create a MOFA 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
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

## -- F --

factors_names | factors_names: set and retrieve factor names
factors_names-method | factors_names: set and retrieve factor names
factors_names<- | factors_names: set and retrieve factor names
factors_names<--method | factors_names: set and retrieve factor names
features_metadata | features_metadata: set and retrieve feature metadata
features_metadata-method | features_metadata: set and retrieve feature metadata
features_metadata<- | features_metadata: set and retrieve feature metadata
features_metadata<--method | features_metadata: set and retrieve feature metadata
features_names | features_names: set and retrieve feature names
features_names-method | features_names: set and retrieve feature names
features_names<- | features_names: set and retrieve feature names
features_names<--method | features_names: set and retrieve feature names

## -- G --

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
groups_names-method | groups_names: set and retrieve group names
groups_names<- | groups_names: set and retrieve group names
groups_names<--method | groups_names: set and retrieve group names

## -- I --

impute | Impute missing values from a fitted MOFA
interpolate_factors | Interpolate factors in MEFISTO based on new covariate values

## -- L --

load_model | Load a trained MOFA

## -- M --

make_example_data | Simulate a data set using the generative model of MOFA
MOFA | Class to store a mofa model
MOFA-class | Class to store a mofa model
mofa2 | One-liner wrapper to create a MOFA2 object from multiple input objects and ready for training

## -- P --

plot_alignment | Plot covariate alignment across 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_factors | Scatterplots of two factor values
plot_factors_vs_cov | Scatterplots of a factor's values against the sample covariates
plot_factor_cor | Plot correlation matrix between latent factors
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
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

## -- R --

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

## -- S --

samples_metadata | samples_metadata: retrieve sample metadata
samples_metadata-method | samples_metadata: retrieve sample metadata
samples_metadata<- | samples_metadata: retrieve sample metadata
samples_metadata<--method | samples_metadata: retrieve sample metadata
samples_names | samples_names: set and retrieve sample names
samples_names-method | samples_names: set and retrieve sample names
samples_names<- | samples_names: set and retrieve sample names
samples_names<--method | 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

## -- V --

views_names | views_names: set and retrieve view names
views_names-method | views_names: set and retrieve view names
views_names<- | views_names: set and retrieve view names
views_names<--method | views_names: set and retrieve view names

## -- misc --

%>% | Re-exporting the pipe operator See 'magrittr::%>%' for details.