DOI: 10.18129/B9.bioc.MOFA2  

This package is for version 3.17 of Bioconductor; for the stable, up-to-date release version, see MOFA2.

Multi-Omics Factor Analysis v2

Bioconductor version: 3.17

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.

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

Maintainer: Ricard Argelaguet <ricard.argelaguet at>

Citation (from within R, enter citation("MOFA2")):


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HTML R Script Downstream analysis: Overview
HTML R Script MEFISTO on simulated data (temporal)
HTML R Script MOFA2: How to train a model in R
PDF   Reference Manual


biocViews Bayesian, DimensionReduction, Software, Visualization
Version 1.10.0
In Bioconductor since BioC 3.12 (R-4.0) (3 years)
License file LICENSE
Depends R (>= 4.0)
Imports rhdf5, dplyr, tidyr, reshape2, pheatmap, ggplot2, methods, RColorBrewer, cowplot, ggrepel, reticulate, HDF5Array, grDevices, stats, magrittr, forcats, utils, corrplot, DelayedArray, Rtsne, uwot, basilisk, stringi
Suggests knitr, testthat, Seurat, ggpubr, foreach, psych, MultiAssayExperiment, SummarizedExperiment, SingleCellExperiment, ggrastr, mvtnorm, GGally, rmarkdown, data.table, tidyverse, BiocStyle, Matrix, markdown
SystemRequirements Python (>=3), numpy, pandas, h5py, scipy, argparse, sklearn, mofapy2
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