RT Journal Article
SR Electronic
T1 MultiVI: deep generative model for the integration of multi-modal data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.08.20.457057
DO 10.1101/2021.08.20.457057
A1 Tal Ashuach
A1 Mariano I. Gabitto
A1 Michael I. Jordan
A1 Nir Yosef
YR 2021
UL http://biorxiv.org/content/early/2021/08/20/2021.08.20.457057.abstract
AB The ability to jointly profile the transcriptional and chromatin land-scape of single-cells has emerged as a powerful technique to identify cellular populations and shed light on their regulation of gene expression. Current computational methods analyze jointly profiled (paired) or individual data modalities (unpaired), but do not offer a principled method to analyze both paired and unpaired samples jointly. Here we present MultiVI, a probabilistic framework that leverages deep neural networks to jointly analyze scRNA, scATAC and multiomic (scRNA + scATAC) data. MultiVI creates an informative low-dimensional latent space that accurately reflects both chromatin and transcriptional properties of the cells even when one of the modalities is missing. MultiVI accounts for technical effects in both scRNA and scATAC-seq while correcting for batch effects in both data modalities. We use public datasets to demonstrate that MultiVI is stable, easy to use, and outperforms current approaches for the joint analysis of paired and unpaired data. MultiVI is available as an open source package, implemented in the scvi-tools frame-work: https://docs.scvi-tools.org/.Competing Interest StatementThe authors have declared no competing interest.