MultiVI: Deep Generative Model for the Integration of Multimodal Data

Overview

Journal Nat Methods

Specialties Biomedical Engineering
Pathology

Date 2023 Jun 29

PMID 37386189

Authors

Tal Ashuach

Mariano I Gabitto

Rohan V Koodli

Giuseppe-Antonio Saldi

Michael I Jordan

Nir Yosef

Affiliations

Soon will be listed here.

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 .

Citing Articles

Infusing structural assumptions into dimensionality reduction for single-cell RNA sequencing data to identify small gene sets.

Hackenberg M, Brunn N, Vogel T, Binder H Commun Biol. 2025; 8(1):414.

PMID: 40069486 PMC: 11897155. DOI: 10.1038/s42003-025-07872-9.

Combining single-cell ATAC and RNA sequencing for supervised cell annotation.

Gill J, Dasgupta A, Manry B, Markuzon N BMC Bioinformatics. 2025; 26(1):67.

PMID: 40011801 PMC: 11863512. DOI: 10.1186/s12859-025-06084-6.

Cell signaling pathways discovery from multi-modal data.

He C, Simpson C, Cossentino I, Zhang B, Tkachev S, Eddins D bioRxiv. 2025; .

PMID: 39975141 PMC: 11839107. DOI: 10.1101/2025.02.06.636961.

EpiFoundation: A Foundation Model for Single-Cell ATAC-seq via Peak-to-Gene Alignment.

Wu J, Wan C, Ji Z, Zhou Y, Hou W bioRxiv. 2025; .

PMID: 39975086 PMC: 11839112. DOI: 10.1101/2025.02.05.636688.

scCobra allows contrastive cell embedding learning with domain adaptation for single cell data integration and harmonization.

Zhao B, Song K, Wei D, Xiong Y, Ding J Commun Biol. 2025; 8(1):233.

PMID: 39948393 PMC: 11825689. DOI: 10.1038/s42003-025-07692-x.

References

Tasic B, Menon V, Nguyen T, Kim T, Jarsky T, Yao Z . Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat Neurosci. 2016; 19(2):335-46. PMC: 4985242. DOI: 10.1038/nn.4216. View

Buenrostro J, Wu B, Chang H, Greenleaf W . ATAC-seq: A Method for Assaying Chromatin Accessibility Genome-Wide. Curr Protoc Mol Biol. 2015; 109:21.29.1-21.29.9. PMC: 4374986. DOI: 10.1002/0471142727.mb2129s109. View

Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N . mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods. 2009; 6(5):377-82. DOI: 10.1038/nmeth.1315. View

Jaitin D, Kenigsberg E, Keren-Shaul H, Elefant N, Paul F, Zaretsky I . Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science. 2014; 343(6172):776-9. PMC: 4412462. DOI: 10.1126/science.1247651. View

Buenrostro J, Wu B, Litzenburger U, Ruff D, Gonzales M, Snyder M . Single-cell chromatin accessibility reveals principles of regulatory variation. Nature. 2015; 523(7561):486-90. PMC: 4685948. DOI: 10.1038/nature14590. View

Argelaguet R, Arnol D, Bredikhin D, Deloro Y, Velten B, Marioni J . MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data. Genome Biol. 2020; 21(1):111. PMC: 7212577. DOI: 10.1186/s13059-020-02015-1. View

Singh R, Hie B, Narayan A, Berger B . Schema: metric learning enables interpretable synthesis of heterogeneous single-cell modalities. Genome Biol. 2021; 22(1):131. PMC: 8091541. DOI: 10.1186/s13059-021-02313-2. View

DeTomaso D, Jones M, Subramaniam M, Ashuach T, Ye C, Yosef N . Functional interpretation of single cell similarity maps. Nat Commun. 2019; 10(1):4376. PMC: 6763499. DOI: 10.1038/s41467-019-12235-0. View

Gong B, Zhou Y, Purdom E . Cobolt: integrative analysis of multimodal single-cell sequencing data. Genome Biol. 2021; 22(1):351. PMC: 8715620. DOI: 10.1186/s13059-021-02556-z. View

10.

Lopez R, Regier J, Cole M, Jordan M, Yosef N . Deep generative modeling for single-cell transcriptomics. Nat Methods. 2018; 15(12):1053-1058. PMC: 6289068. DOI: 10.1038/s41592-018-0229-2. View

11.

Gayoso A, Steier Z, Lopez R, Regier J, Nazor K, Streets A . Joint probabilistic modeling of single-cell multi-omic data with totalVI. Nat Methods. 2021; 18(3):272-282. PMC: 7954949. DOI: 10.1038/s41592-020-01050-x. View

12.

Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K . Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods. 2019; 16(12):1289-1296. PMC: 6884693. DOI: 10.1038/s41592-019-0619-0. View

13.

Satpathy A, Granja J, Yost K, Qi Y, Meschi F, McDermott G . Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion. Nat Biotechnol. 2019; 37(8):925-936. PMC: 7299161. DOI: 10.1038/s41587-019-0206-z. View

14.

Ding J, Adiconis X, Simmons S, Kowalczyk M, Hession C, Marjanovic N . Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. Nat Biotechnol. 2020; 38(6):737-746. PMC: 7289686. DOI: 10.1038/s41587-020-0465-8. View

15.

Granja J, Corces M, Pierce S, Bagdatli S, Choudhry H, Chang H . ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat Genet. 2021; 53(3):403-411. PMC: 8012210. DOI: 10.1038/s41588-021-00790-6. View

16.

Traag V, Waltman L, van Eck N . From Louvain to Leiden: guaranteeing well-connected communities. Sci Rep. 2019; 9(1):5233. PMC: 6435756. DOI: 10.1038/s41598-019-41695-z. View