https://github.com/saezlab/mofacellular
R package to infer multicellular programs from single-cell data using multi-omics factor analysis (MOFA)
https://github.com/saezlab/mofacellular
Last synced: over 1 year ago
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R package to infer multicellular programs from single-cell data using multi-omics factor analysis (MOFA)
- Host: GitHub
- URL: https://github.com/saezlab/mofacellular
- Owner: saezlab
- License: gpl-3.0
- Created: 2023-02-24T09:29:44.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-08-17T14:47:12.000Z (almost 3 years ago)
- Last Synced: 2025-03-28T05:51:02.103Z (over 1 year ago)
- Language: R
- Homepage: https://saezlab.github.io/MOFAcellulaR/
- Size: 2.42 MB
- Stars: 9
- Watchers: 1
- Forks: 2
- Open Issues: 4
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
Awesome Lists containing this project
README
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
out.width = "100%"
)
```
## Overview
Cross-condition single-cell atlases are essential in the characterization of human disease. In these complex experimental designs, patient samples are profiled across distinct cell types and clinical conditions to describe disease processes at the cellular level. However, most of the current analysis tools are limited to pairwise cross-condition comparisons, disregarding the multicellular nature of disease processes and the effects of other biological and technical factors in the variation of gene expression. Here we propose a computational framework for an unsupervised analysis of samples from cross-condition single cell atlases and for the identification of multicellular programs associated with disease. Our framework based on probabilistic factor analysis implemented in [MOFA](https://www.embopress.org/doi/full/10.15252/msb.20178124) and [MOFA+](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02015-1) incorporates the variation of patient samples across cell types and allows the joint analysis of independent patient cohorts facilitating study integration.
`MOFAcellulaR` is package that facilitates the implementation of MOFA models to single cell data
## Installation
You can install the latest stable and development versions from GitHub with `remotes`:
- stable
```{r github_install, eval=FALSE}
# install.packages("remotes")
remotes::install_github("saezlab/MOFAcellulaR")
```
## Usage
Start by reading `vignette("MOFAcellulaR")` to learn how to use the helping functions of **MOFAcellulaR** to run your MOFA models.
## Citation
If you use **MOFAcellulaR** for your research please cite the [following publication](https://www.biorxiv.org/content/10.1101/2023.02.23.529642v1):
> Ramirez-Flores RO, Lanzer JD, Dimitrov D, Velten B, Saez-Rodriguez J. “Multicellular factor analysis for a tissue-centric understanding of disease” BioRxiv. 2023. DOI: [10.1101/2023.02.23.529642](https://www.biorxiv.org/content/10.1101/2023.02.23.529642v1)
Also, don't forget to cite MOFA's original publications
>Argelaguet R, Arnol D, Bredikhin D, Deloro Y, Velten B, Marioni JC & Stegle O (2020) MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data. Genome Biol 21: 111
>Argelaguet R, Velten B, Arnol D, Dietrich S, Zenz T, Marioni JC, Buettner F, Huber W & Stegle O (2018) Multi-Omics Factor Analysis-a framework for unsupervised integration of multi-omics data sets. Mol Syst Biol 14: e8124