MultiVI: deep generative model for the integration of multi-modal data
Open accessAbstract
Jointly profiling the transcriptional and chromatin accessibility landscapes of single-cells is a powerful technique to characterize cellular populations. Here we present MultiVI, a probabilistic model to analyze such multiomic data and integrate it with single modality datasets. MultiVI creates a joint representation that accurately reflects both chromatin and transcriptional properties of the cells even when one modality is missing. It also imputes missing data, corrects for batch effects and is available in the scvi-tools framework: https://docs.scvi-tools.org/ .
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The study extended transcriptionally defined supertypes across snRNA-seq, snATAC-seq and snMultiome datasets to construct a joint multiomic representation from both neurotypical reference donors and donors with disease.
“We then extended our transcriptionally defined supertypes across snRNA-seq, snATAC–seq and snMultiome datasets to construct a joint multiomic representation49 from both neurotypical reference donors and donors with the disease (Extended Data Fig. 6a–e).”
From Integrated multimodal cell atlas of Alzheimer’s disease (2024) · cites this paper as [49]
StabMap, Cobolt, MultiVI and Bridge Integration in Seurat v5 integrate unmatched datasets using separately collected multi-omics datasets.
“These methods include StabMap170, Cobolt171, MultiVI172 and ‘Bridge Integration’ in Seurat v5 (ref. 173).”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [172]
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Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067 · see citing works
Integrated analysis of multimodal single-cell data
Hao, Y., Hao, S., Andersen-Nissen, E., Mauck, W. M., Zheng, S., Butler, A., Lee, M. J., Wilk, A. J., Darby, C., Zagar, M., et al. (2020) · doi:10.1101/2020.10.12.335331 · Cited by 15,508 · see citing works
Fast, sensitive and accurate integration of single-cell data with Harmony
Korsunsky, I., Millard, N., Fan, J., Slowikowski, K., Zhang, F., Wei, K., Baglaenko, Y., Brenner, M., Loh, P.-R., & Raychaudhuri, S. (2019) · Nature Methods · doi:10.1038/s41592-019-0619-0 · Cited by 10,488 · see citing works
From Louvain to Leiden: guaranteeing well-connected communities
Traag, V. A., Waltman, L., & van Eck, N. J. (2019) · Scientific Reports · doi:10.1038/s41598-019-41695-z · Cited by 5,635 · see citing works
Variational Inference: A Review for Statisticians
Blei, D. M., Kucukelbir, A., & McAuliffe, J. D. (2017) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1285773 · Cited by 3,974 · see citing works
mRNA-Seq whole-transcriptome analysis of a single cell
Tang, F., Barbacioru, C., Wang, Y., Nordman, E., Lee, C., Xu, N., Wang, X., Bodeau, J., Tuch, B. B., Siddiqui, A., et al. (2009) · Nature Methods · doi:10.1038/nmeth.1315 · Cited by 3,969 · see citing works
ATAC-seq: a method for assaying chromatin accessibility genome-wide
Buenrostro, J. D., Wu, B., Chang, H. Y., & Greenleaf, W. J. (2015) · Current Protocols in Molecular Biology · doi:10.1002/0471142727.mb2129s109 · Cited by 3,289 · see citing works
Deep generative modeling for single-cell transcriptomics
Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., & Yosef, N. (2018) · Nature Methods · doi:10.1038/s41592-018-0229-2 · Cited by 2,530 · see citing works
Self-citationAdvances in Neural Information Processing Systems 27 (NIPS 2014)
Zhong, M., Goddard, N., & Sutton, C. A. (2014) · Cited by 2,440 · see citing works
Single-cell chromatin accessibility reveals principles of regulatory variation
Buenrostro, J. D., Wu, B., Litzenburger, U. M., Ruff, D., Gonzales, M. L., Snyder, M. P., Chang, H. Y., & Greenleaf, W. J. (2015) · Nature · doi:10.1038/nature14590 · Cited by 2,308 · see citing works
A Kernel Method for the Two-Sample-Problem
Gretton, A., Borgwardt, K. M., Rasch, M., Schölkopf, B., & Smola, A. J. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0069 · Cited by 2,167 · see citing works
Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types
Jaitin, D. A., Kenigsberg, E., Keren-Shaul, H., Elefant, N., Paul, F., Zaretsky, I., Mildner, A., Cohen, N., Jung, S., Tanay, A., et al. (2014) · Science · doi:10.1126/science.1247651 · Cited by 1,795 · see citing works
Adult mouse cortical cell taxonomy revealed by single cell transcriptomics
Tasic, B., Menon, V., Nguyen, T. N., Kim, T. K., Jarsky, T., Yao, Z., Levi, B., Gray, L. T., Sorensen, S. A., Dolbeare, T., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4216 · Cited by 1,714 · see citing works
Benchmarking atlas-level data integration in single-cell genomics
Luecken, M. D., Büttner, M., Chaichoompu, K., Danese, A., Interlandi, M., Mueller, M. F., Strobl, D. C., Zappia, L., Dugas, M., Colomé-Tatché, M., et al. (2022) · Nature Methods · doi:10.1038/s41592-021-01336-8 · Cited by 1,438 · see citing works
ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis
Granja, J. M., Corces, M. R., Pierce, S. E., Bagdatli, S. T., Choudhry, H., Chang, H. Y., & Greenleaf, W. J. (2021) · Nature Genetics · doi:10.1038/s41588-021-00790-6 · Cited by 1,392 · see citing works
MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data
Argelaguet, R., Arnol, D., Bredikhin, D., Deloro, Y., Velten, B., Marioni, J. C., & Stegle, O. (2020) · Genome Biology · doi:10.1186/s13059-020-02015-1 · Cited by 1,091 · see citing works
Satpathy, A. T., Granja, J. M., Yost, K. E., Qi, Y., Meschi, F., McDermott, G. P., Olsen, B. N., Mumbach, M. R., Pierce, S. E., Corces, M. R., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0206-z · Cited by 980 · see citing works
Joint profiling of chromatin accessibility and gene expression in thousands of single cells
Cao, J., Cusanovich, D. A., Ramani, V., Aghamirzaie, D., Pliner, H. A., Hill, A. J., Daza, R. M., McFaline-Figueroa, J. L., Packer, J. S., Christiansen, L., et al. (2018) · Science · doi:10.1126/science.aau0730 · Cited by 938 · see citing works
Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
Ding, J., Adiconis, X., Simmons, S. K., Kowalczyk, M. S., Hession, C. C., Marjanovic, N. D., Hughes, T. K., Wadsworth, M. H., Burks, T., Nguyen, L. T., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0465-8 · Cited by 871 · see citing works
A Python library for probabilistic analysis of single-cell omics data
Gayoso, A., Lopez, R., Xing, G., Boyeau, P., Valiollah Pour Amiri, V., Hong, J., Wu, K., Jayasuriya, M., Mehlman, E., Langevin, M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01206-w · Cited by 789 · see citing works
Self-citationJoint probabilistic modeling of single-cell multi-omic data with totalVI
Gayoso, A., Steier, Z., Lopez, R., Regier, J., Nazor, K. L., Streets, A., & Yosef, N. (2021) · Nature Methods · doi:10.1038/s41592-020-01050-x · Cited by 592 · see citing works
Self-citationMimitou, E. P., Lareau, C. A., Chen, K. Y., Zorzetto-Fernandes, A. L., Hao, Y., Takeshima, Y., Luo, W., Huang, T.-S., Yeung, B. Z., Papalexi, E., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00927-2 · Cited by 529 · see citing works
Computational principles and challenges in single-cell data integration
Argelaguet, R., Cuomo, A. S. E., Stegle, O., & Marioni, J. C. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00895-7 · Cited by 472 · see citing works
Swanson, E., Lord, C., Reading, J., Heubeck, A. T., Genge, P. C., Thomson, Z., Weiss, M. D., Li, X.-J., Savage, A. K., Green, R. R., et al. (2021) · eLife · doi:10.7554/elife.63632 · Cited by 267 · see citing works
Functional interpretation of single cell similarity maps
DeTomaso, D., Jones, M. G., Subramaniam, M., Ashuach, T., Ye, C., & Yosef, N. (2019) · Nature Communications · doi:10.1038/s41467-019-12235-0 · Cited by 251 · see citing works
Self-citationFast, sensitive, and accurate integration of single cell data with HarmonyPreprint
Korsunsky, I., Fan, J., Slowikowski, K., et al. (2018) · bioRxiv · doi:10.1101/461954 · Cited by 177 · see citing works
Cobolt: integrative analysis of multimodal single-cell sequencing data
Gong, B., Zhou, Y., & Purdom, E. (2021) · Genome Biology · doi:10.1186/s13059-021-02556-z · Cited by 168 · see citing works
A mixture-of-experts deep generative model for integrated analysis of single-cell multiomics data
Minoura, K., Abe, K., Nam, H., Nishikawa, H., & Shimamura, T. (2021) · Cell Reports Methods · doi:10.1016/j.crmeth.2021.100071 · Cited by 147 · see citing works
Benchmarking atlas-level data integration in single-cell genomicsPreprint
Luecken, M. D., Büttner, M., Chaichoompu, K., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.22.111161 · Cited by 122 · see citing works
Satpathy, A. T., Granja, J. M., Yost, K. E., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/610550 · Cited by 120 · see citing works
Lakkis, J., Schroeder, A., Su, K., Lee, M. Y. Y., Bashore, A. C., Reilly, M. P., & Li, M. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00545-w · Cited by 105 · see citing works
Lopez, R., Nazaret, A., Langevin, M., Samaran, J., Regier, J., Jordan, M. I., & Yosef, N. (2019) · arXiv · doi:10.48550/arxiv.1905.02269 · Cited by 105 · see citing works
Self-citationPeakVI: A deep generative model for single-cell chromatin accessibility analysis
Ashuach, T., Reidenbach, D., Gayoso, A., & Yosef, N. (2022) · Cell Reports Methods · doi:10.1016/j.crmeth.2022.100182 · Cited by 102 · see citing works
Self-citationStabilized mosaic single-cell data integration using unshared features
Ghazanfar, S., Guibentif, C., & Marioni, J. C. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01766-z · Cited by 101 · see citing works
Statistical Methods in Integrative Genomics
Richardson, S. T., Tseng, G. C., & Sun, W. (2016) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-041715-033506 · Cited by 97 · see citing works
Kriebel, A. R., & Welch, J. D. (2022) · Nature Communications · doi:10.1038/s41467-022-28431-4 · Cited by 85 · see citing works
scvi-tools: a library for deep probabilistic analysis of single-cell omics dataPreprint
Gayoso, A., Lopez, R., Xing, G. K., et al. (2021) · bioRxiv · doi:10.1101/2021.04.28.441833 · Cited by 66 · see citing works
Schema: metric learning enables interpretable synthesis of heterogeneous single-cell modalities
Singh, R., Hie, B., Narayan, A., & Berger, B. (2021) · Genome biology · doi:10.1186/s13059-021-02313-2 · Cited by 40 · see citing works
Sinzger-D’Angelo, M., Vanhoefer, J., Loos, C., & Hasenauer, J. (2019) · Scientific Reports · doi:10.1038/s41598-019-38907-x · Cited by 31 · see citing works
Functional Interpretation of Single-Cell Similarity MapsPreprint
DeTomaso, D., Jones, M. G., Subramaniam, M., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/403055 · Cited by 28 · see citing works
StabMap: Mosaic single cell data integration using non-overlapping features
Ghazanfar, S., Guibentif, C., & Marioni, J. C. (2022) · doi:10.1101/2022.02.24.481823 · Cited by 16 · see citing works
Cobolt: Joint analysis of multimodal single-cell sequencing dataPreprint
Gong, B., Zhou, Y., & Purdom, E. (2021) · bioRxiv · doi:10.1101/2021.04.03.438329 · Cited by 8 · see citing works
MultiVI - Intermediate datasets, notebooks, and scripts
Ashuach, T. (2021) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.5225857 · Cited by 2 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 3
- [22]Lopez, R. et al. Decision-making with auto-encoding variational Bayes. Advances in Neural Information Processing Systems33, 5081–5092 (2020).
- [31]Loshchilov, I. & Hutter, F. Decoupled weight decay regularization. OpenReview.net https://openreview.net/forum?id=Bkg6RiCqY7 (2019).
- [32]Yaroslav, G. et al. Domain-Adversarial Training of Neural Networks Vol. 7 (2016).
Cited by
Sorted by citations
Best practices for single-cell analysis across modalities
Heumos, L., Schaar, A. C., Lance, C., Litinetskaya, A., Drost, F., Zappia, L., Lücken, M. D., Strobl, D. C., Henao, J., Curion, F., et al. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00586-w · Cited by 1,051 · see citing works
“Once TFs of interest have been identified, scATAC-seq data allow for additional validation of the TF impact through footprinting, which indicates whether the TF is binding in the given cell cluster.”
MethodThe technological landscape and applications of single-cell multi-omics
Baysoy, A., Bai, Z., Satija, R., & Fan, R. (2023) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-023-00615-w · Cited by 1,037 · see citing works
Integrated multimodal cell atlas of Alzheimer’s disease
Gabitto, M. I., Travaglini, K. J., Rachleff, V. M., Kaplan, E. S., Long, B., Ariza, J., Ding, Y., Mahoney, J. T., Dee, N., Goldy, J., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01774-5 · Cited by 397 · see citing works
“We then extended our transcriptionally defined supertypes across snRNA-seq, snATAC–seq and snMultiome datasets to construct a joint multiomic representation 49 from both neurotypical reference donors and donors with the disease (Extended Data Fig.”
Self-citationSingle-cell transcriptomics for the assessment of cardiac disease
Miranda, A. M. A., Janbandhu, V., Maatz, H., Kanemaru, K., Cranley, J., Teichmann, S. A., Hübner, N., Schneider, M. D., Harvey, R. P., & Noseda, M. (2023) · Nature Reviews Cardiology · doi:10.1038/s41569-022-00805-7 · Cited by 117 · see citing works
Mosaic integration and knowledge transfer of single-cell multimodal data with MIDAS
He, Z., Hu, S., Chen, Y., An, S., Zhou, J., Liu, R., Shi, J., Wang, J., Dong, G., Shi, J., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-02040-y · Cited by 84 · see citing works
“22 and 23).”
Multimodal single cell data integration challenge: results and lessons learned
Lance, C., Luecken, M. D., Burkhardt, D. B., Cannoodt, R., Rautenstrauch, P., Laddach, A., Ubingazhibov, A., Cao, Z.-J., Deng, K., Khan, S., et al. (2022) · doi:10.1101/2022.04.11.487796 · Cited by 81 · see citing works
Chandrashekar, P. B., Alatkar, S., Wang, J., Hoffman, G. E., He, C., Jin, T., Khullar, S., Bendl, J., Fullard, J. F., Roussos, P., et al. (2023) · Genome Medicine · doi:10.1186/s13073-023-01248-6 · Cited by 37 · see citing works
Wang, G., Zhao, J., Lin, Y., Liu, T., Zhao, Y., & Zhao, H. (2025) · Nature Communications · doi:10.1038/s41467-025-60333-z · Cited by 29 · see citing works
Cell signaling pathways discovery from multi-modal data
He, C., Simpson, C., Cossentino, I., Zhang, B., Tkachev, S., Eddins, D. J., Kosters, A., Yang, J., Sheth, S., Levy, T., et al. (2025) · doi:10.1101/2025.02.06.636961 · Cited by 3 · see citing works
- Authors
- Ashuach, T., Gabitto, M. I., Jordan, M. I., & Yosef, N.
- Affiliations
- University of California, Berkeley (US)
- Allen Institute for Brain Science (US)
- Weizmann Institute of Science (IL)
- Corresponding author
- Gabitto, Mariano I.
- Yosef, Nir
- Venue
- Nature Methods
- Publisher
- openRxiv
- Published
- 2021-08-20
- Volume
- 20
- Issue
- 8
- Pages
- 1222-1231
- ISSN
- 1548-7091 (ISSN-L)
- PMC
- PMC10406609
- OpenAlex
- W4382632237
- Cited by
- 385
- References
- 43
- Citations by year
- 2026: 153 · 2025: 107 · 2024: 65 · 2023: 42 · 2022: 16 · 2021: 1
- FWCI
- 32.97
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Nature Methods (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (AI090023)
- National Institute of Allergy and Infectious Diseases (U19AI090023, AI090023, U19AI167903)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- deep generative models, multiomic data integration, single-cell transcriptomics, chromatin accessibility, probabilistic models, missing data
- MeSH terms
- Models, Statistical, Transcriptome
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