MultiVI: deep generative model for the integration of multimodal data

Tal Ashuach(University of California, Berkeley), Mariano I. Gabitto(Berkeley College), Rohan V. Koodli(University of California, Berkeley), Giuseppe-Antonio Saldi(Allen Institute for Brain Science), Michael I. Jordan(University of California, Berkeley), Nir Yosef(Weizmann Institute of Science)
Nature Methods
June 29, 2023
Cited by 299Open Access
Full Text

Abstract

Jointly profiling the transcriptome, chromatin accessibility and other molecular properties of single cells offers a powerful way to study cellular diversity. Here we present MultiVI, a probabilistic model to analyze such multiomic data and leverage it to enhance single-modality datasets. MultiVI creates a joint representation that allows an analysis of all modalities included in the multiomic input data, even for cells for which one or more modalities are missing. It is available at scvi-tools.org .


Related Papers

From Louvain to Leiden: guaranteeing well-connected communities
V. A. Traag, L. Waltman, N. J. van Eck|Scientific Reports|2019|5.1k
Variational Inference: A Review for Statisticians
David M. Blei, Alp Kucukelbir, Jon D. McAuliffe|Journal of the American Statistical Association|2017|3.7k