Package: MOFA2 1.15.0

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]

MOFA2_1.15.0.tar.gz
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MOFA2_1.15.0.tgz(r-4.4-any)MOFA2_1.15.0.tgz(r-4.3-any)
MOFA2_1.15.0.tar.gz(r-4.5-noble)MOFA2_1.15.0.tar.gz(r-4.4-noble)
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MOFA2.pdf |MOFA2.html✨
MOFA2/json (API)

Peer review:

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

On BioConductor:MOFA2-1.15.0(bioc 3.20)MOFA2-1.14.0(bioc 3.19)

bioconductor-package

92 exports 1.24 score 82 dependencies 2 mentions

Last updated 2 months agofrom:591d80c9f4

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