Package: MOFA2 1.17.0

Ricard Argelaguet

MOFA2: Multi-Omics Factor Analysis v2

The MOFA2 package contains a collection of tools for training and analysing multi-omic factor analysis (MOFA). MOFA is a probabilistic factor model that aims to identify principal axes of variation from data sets that can comprise multiple omic layers and/or groups of samples. Additional time or space information on the samples can be incorporated using the MEFISTO framework, which is part of MOFA2. Downstream analysis functions to inspect molecular features underlying each factor, vizualisation, imputation etc are available.

Authors:Ricard Argelaguet [aut, cre], Damien Arnol [aut], Danila Bredikhin [aut], Britta Velten [aut]

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MOFA2.pdf |MOFA2.html✨
MOFA2/json (API)

# Install 'MOFA2' in R:
install.packages('MOFA2', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/biofam/mofa2/issues

On BioConductor:MOFA2-1.17.0(bioc 3.21)MOFA2-1.16.0(bioc 3.20)

dimensionreductionbayesianvisualizationfactor-analysismofamulti-omics

10.17 score 308 stars 482 scripts 888 downloads 2 mentions 92 exports 82 dependencies

Last updated 2 months agofrom:12bde390fe. Checks:OK: 1 WARNING: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 29 2024
R-4.5-winWARNINGNov 29 2024
R-4.5-linuxWARNINGNov 29 2024
R-4.4-winWARNINGNov 29 2024
R-4.4-macWARNINGNov 29 2024
R-4.3-winWARNINGNov 29 2024
R-4.3-macWARNINGNov 29 2024

Exports:%>%add_mofa_factors_to_seuratcalculate_contribution_scorescalculate_variance_explainedcalculate_variance_explained_per_samplecluster_samplescompare_elbocompare_factorscorrelate_factors_with_covariatescovariates_namescovariates_names<-create_mofacreate_mofa_from_dfcreate_mofa_from_matrixcreate_mofa_from_MultiAssayExperimentcreate_mofa_from_Seuratcreate_mofa_from_SingleCellExperimentfactors_namesfactors_names<-features_metadatafeatures_metadata<-features_namesfeatures_names<-get_covariatesget_dataget_default_data_optionsget_default_mefisto_optionsget_default_model_optionsget_default_stochastic_optionsget_default_training_optionsget_dimensionsget_elboget_expectationsget_factorsget_group_kernelget_imputed_dataget_interpolated_factorsget_lengthscalesget_scalesget_variance_explainedget_weightsgroups_namesgroups_names<-imputeinterpolate_factorsload_modelmake_example_dataplot_alignmentplot_ascii_dataplot_data_heatmapplot_data_overviewplot_data_scatterplot_data_vs_covplot_dimredplot_enrichmentplot_enrichment_detailedplot_enrichment_heatmapplot_factorplot_factor_corplot_factorsplot_factors_vs_covplot_group_kernelplot_interpolation_vs_covariateplot_sharednessplot_smoothnessplot_top_weightsplot_variance_explainedplot_variance_explained_by_covariatesplot_variance_explained_per_featureplot_weightsplot_weights_heatmapplot_weights_scatterpredictprepare_mofarun_enrichmentrun_mofarun_tsnerun_umapsamples_metadatasamples_metadata<-samples_namessamples_names<-select_modelset_covariatessubset_factorssubset_featuressubset_groupssubset_samplessubset_viewssummarise_factorsviews_namesviews_names<-

Dependencies:abindbasiliskbasilisk.utilsBHBiocGenericsclicolorspacecorrplotcowplotcpp11crayonDelayedArraydir.expirydplyrdqrngfansifarverfilelockFNNforcatsgenericsggplot2ggrepelgluegtableHDF5ArrayhereIRangesirlbaisobandjsonlitelabelinglatticelifecyclemagrittrMASSMatrixMatrixGenericsmatrixStatsmgcvmunsellnlmepheatmappillarpkgconfigplyrpngpurrrR6rappdirsRColorBrewerRcppRcppAnnoyRcppEigenRcppProgressRcppTOMLreshape2reticulaterhdf5rhdf5filtersRhdf5librlangrprojrootRSpectraRtsneS4ArraysS4VectorsscalessitmoSparseArraystringistringrtibbletidyrtidyselectutf8uwotvctrsviridisLitewithrXVectorzlibbioc

MOFA+: downstream analysis in R

Ricard Argelaguet, Britta Velten

Rendered fromdownstream_analysis.Rmdusingknitr::rmarkdownon Nov 29 2024.

Last update: 2020-10-11
Started: 2020-10-01

Illustration of MEFISTO on simulated data with a temporal covariate

Britta Velten

Rendered fromMEFISTO_temporal.Rmdusingknitr::rmarkdownon Nov 29 2024.

Last update: 2022-10-07
Started: 2020-11-24

MOFA2: training a model in R

Ricard Argelaguet, Britta Velten

Rendered fromgetting_started_R.Rmdusingknitr::rmarkdownon Nov 29 2024.

Last update: 2023-03-19
Started: 2020-10-01

Readme and manuals

Help Manual

Help pageTopics
Re-exporting the pipe operator See 'magrittr::%>%' for details.%>%
Function to add the MOFA representation onto a Seurat objectadd_mofa_factors_to_seurat
Calculate contribution scores for each view in each samplecalculate_contribution_scores
Calculate variance explained by the modelcalculate_variance_explained
Calculate variance explained by the MOFA factors for each samplecalculate_variance_explained_per_sample
K-means clustering on samples based on latent factorscluster_samples
Compare different trained 'MOFA' objects in terms of the final value of the ELBO statistics and number of inferred factorscompare_elbo
Plot the correlation of factors between different modelscompare_factors
Plot correlation of factors with external covariatescorrelate_factors_with_covariates
covariates_names: set and retrieve covariate namescovariates,MOFA-method covariates_names covariates_names,MOFA-method covariates_names<- covariates_names<-,MOFA,vector-method
create a MOFA objectcreate_mofa
create a MOFA object from a data.frame objectcreate_mofa_from_df
create a MOFA object from a a list of matricescreate_mofa_from_matrix
create a MOFA object from a MultiAssayExperiment objectcreate_mofa_from_MultiAssayExperiment
create a MOFA object from a Seurat objectcreate_mofa_from_Seurat
create a MOFA object from a SingleCellExperiment objectcreate_mofa_from_SingleCellExperiment
factors_names: set and retrieve factor namesfactors_names factors_names,MOFA-method factors_names<- factors_names<-,MOFA,vector-method
features_metadata: set and retrieve feature metadatafeatures_metadata features_metadata,MOFA-method features_metadata<- features_metadata<-,MOFA,data.frame-method
features_names: set and retrieve feature namesfeatures_names features_names,MOFA-method features_names<- features_names<-,MOFA,list-method
Get sample covariatesget_covariates
Get dataget_data
Get default data optionsget_default_data_options
Get default options for MEFISTO covariatesget_default_mefisto_options
Get default model optionsget_default_model_options
Get default stochastic optionsget_default_stochastic_options
Get default training optionsget_default_training_options
Get dimensionsget_dimensions
Get ELBOget_elbo
Get expectationsget_expectations
Get factorsget_factors
Get group covariance matrixget_group_kernel
Get imputed dataget_imputed_data
Get interpolated factor valuesget_interpolated_factors
Get lengthscalesget_lengthscales
Get scalesget_scales
Get variance explained valuesget_variance_explained
Get weightsget_weights
groups_names: set and retrieve group namesgroups_names groups_names,MOFA-method groups_names<- groups_names<-,MOFA,character-method
Impute missing values from a fitted MOFAimpute
Interpolate factors in MEFISTO based on new covariate valuesinterpolate_factors
Load a trained MOFAload_model
Simulate a data set using the generative model of MOFAmake_example_data
Class to store a mofa modelMOFA MOFA-class
Plot covariate alignment acorss groupsplot_alignment
Visualize the structure of the data in the terminalplot_ascii_data
Plot heatmap of relevant featuresplot_data_heatmap
Overview of the input dataplot_data_overview
Scatterplots of feature values against latent factorsplot_data_scatter
Scatterplots of feature values against sample covariatesplot_data_vs_cov
Plot dimensionality reduction based on MOFA factorsplot_dimred
Plot output of gene set Enrichment Analysisplot_enrichment
Plot detailed output of the Feature Set Enrichment Analysisplot_enrichment_detailed
Heatmap of Feature Set Enrichment Analysis resultsplot_enrichment_heatmap
Beeswarm plot of factor valuesplot_factor
Plot correlation matrix between latent factorsplot_factor_cor
Scatterplots of two factor valuesplot_factors
Scatterplots of a factor's values againt the sample covariatesplot_factors_vs_cov
Heatmap plot showing the group-group correlations per factorplot_group_kernel
Plot interpolated factors versus covariate (1-dimensional)plot_interpolation_vs_covariate
Barplot showing the sharedness per factorplot_sharedness
Barplot showing the smoothness per factorplot_smoothness
Plot top weightsplot_top_weights
Plot variance explained by the modelplot_variance_explained
Plot variance explained by the smooth components of the modelplot_variance_explained_by_covariates
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 axisplot_variance_explained_per_feature
Plot distribution of feature weights (weights)plot_weights
Plot heatmap of the weightsplot_weights_heatmap
Scatterplots of weightsplot_weights_scatter
Do predictions using a fitted MOFApredict
Prepare a MOFA for trainingprepare_mofa
Run feature set Enrichment Analysisrun_enrichment
Train a MOFA modelrun_mofa
Run t-SNE on the MOFA factorsrun_tsne
Run UMAP on the MOFA factorsrun_umap
samples_metadata: retrieve sample metadatasamples_metadata samples_metadata,MOFA-method samples_metadata<- samples_metadata<-,MOFA,data.frame-method
samples_names: set and retrieve sample namessamples_names samples_names,MOFA-method samples_names<- samples_names<-,MOFA,list-method
Select a model from a list of trained 'MOFA' objects based on the best ELBO valueselect_model
Add covariates to a MOFA modelset_covariates
Subset factorssubset_factors
Subset featuressubset_features
Subset groupssubset_groups
Subset samplessubset_samples
Subset viewssubset_views
Summarise factor values using external groupssummarise_factors
views_names: set and retrieve view namesviews_names views_names,MOFA-method views_names<- views_names<-,MOFA,character-method