Man pages for MOFA2
Multi-Omics Factor Analysis v2
| calculate_variance_explained | Calculate variance explained by the model |
| cluster_samples | K-means clustering on samples based on latent factors |
| compare_elbo | Compare different trained 'MOFA' objects in terms of the... |
| compare_factors | Plot the correlation of factors between different models |
| correlate_factors_with_covariates | Plot correlation of factors with external covariates |
| 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 |
| 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_data | Get data |
| get_default_data_options | Get default data options |
| 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_imputed_data | Get imputed data |
| 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 |
| load_model | Load a trained MOFA |
| make_example_data | Simulate a data set using the generative model of MOFA |
| MOFA | Class to store a mofa model |
| pipe | Re-exporting the pipe operator See 'magrittr::%>%' for... |
| 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_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_top_weights | Plot top weights |
| plot_variance_explained | Plot variance explained by 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 |
| 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... |
| 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 |
