MOFAcellulaR

<!-- README.md is generated from README.Rmd. Please edit that file -->

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 and MOFA+ 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

<img src="https://www.dropbox.com/s/4d7xbtz2lc252r3/Fig1.png?raw=1" align="center" width="800">

Installation

You can install the latest stable and development versions from GitHub with remotes:

• stable

# 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:

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

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