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MOFA2

Multi-Omics Factor Analysis v2

Bioconductor version: 3.23 · Package version: 1.22.0

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, visualisation, imputation etc are available.

Installation

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("MOFA2")

Details

MaintainerRicard Argelaguet <ricard.argelaguet@gmail.com>
AuthorRicard Argelaguet [aut, cre] (ORCID: <https://orcid.org/0000-0003-3199-3722>), Damien Arnol [aut] (ORCID: <https://orcid.org/0000-0003-2462-534X>), Danila Bredikhin [aut] (ORCID: <https://orcid.org/0000-0001-8089-6983>), Britta Velten [aut] (ORCID: <https://orcid.org/0000-0002-8397-3515>)
Licensefile LICENSE
URLhttps://biofam.github.io/MOFA2/index.html
Bug Reportshttps://github.com/bioFAM/MOFA2
System RequirementsPython (>=3), numpy, pandas, h5py, scipy, argparse, sklearn, mofapy2
Downloads rank1416
Source branchRELEASE_3_23
biocViewsBayesian, DimensionReduction, Software, Visualization

Documentation

Download

Dependencies

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, SeuratObject, ggpubr, foreach, psych, MultiAssayExperiment, SummarizedExperiment, SingleCellExperiment, ggrastr, mvtnorm, GGally, rmarkdown, data.table, tidyverse, BiocStyle, Matrix, markdown

Reverse dependencies

Imports Me (1): RFLOMICS

Suggests Me (3): HoloFoodR, SUMO, tidyexposomics