Multi-Omics Factor Analysis v2

Documentation for package ‘MOFA2’ version 1.2.0

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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

-- P --

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_factors	Scatterplots of two factor values
plot_factors_vs_cov	Scatterplots of a factor's values againt 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 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

-- 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.