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
| Maintainer | Ricard Argelaguet <ricard.argelaguet@gmail.com> |
| Author | Ricard 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>) |
| License | file LICENSE |
| URL | https://biofam.github.io/MOFA2/index.html |
| Bug Reports | https://github.com/bioFAM/MOFA2 |
| System Requirements | Python (>=3), numpy, pandas, h5py, scipy, argparse, sklearn, mofapy2 |
| Downloads rank | 1416 |
| Source branch | RELEASE_3_23 |
| biocViews | Bayesian, DimensionReduction, Software, Visualization |
Documentation
- MOFA+: downstream analysis in R
- Illustration of MEFISTO on simulated data with a temporal covariate
- MOFA2: training a model in R
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