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DestVI identifies continuums of cell types in spatial transcriptomics data
Open accessPublished version of a preprint
Abstract
Most spatial transcriptomics technologies are limited by their resolution, with spot sizes larger than that of a single cell. Although joint analysis with single-cell RNA sequencing can alleviate this problem, current methods are limited to assessing discrete cell types, revealing the proportion of cell types inside each spot. To identify continuous variation of the transcriptome within cells of the same type, we developed Deconvolution of Spatial Transcriptomics profiles using Variational Inference (DestVI). Using simulations, we demonstrate that DestVI outperforms existing methods for estimating gene expression for every cell type inside every spot. Applied to a study of infected lymph nodes and of a mouse tumor model, DestVI provides high-resolution, accurate spatial characterization of the cellular organization of these tissues and identifies cell-type-specific changes in gene expression between different tissue regions or between conditions. DestVI is available as part of the open-source software package scvi-tools (https://scvi-tools.org). DestVI models continuous cell states in spatial transcriptomics data.
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“DestVI proposed a framework that jointly learned the latent representations of scRNA-seq datasets and spatial spot expression datasets to deconvolute cell-type proportions and related transcriptional states in single spots [126].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [126]
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Cited by
Sorted by citations
Exploring tissue architecture using spatial transcriptomics
Rao, A., Barkley, D., França, G. S., & Yanai, I. (2021) · Nature · doi:10.1038/s41586-021-03634-9 · Cited by 1,700
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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
Self-citationSpatially informed cell-type deconvolution for spatial transcriptomics
Ma, Y., & Zhou, X. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01273-7 · Cited by 525
Li, B., Zhang, W., Guo, C., Xu, H., Li, L., Fang, M., Hu, Y., Zhang, X., Yao, X., Tang, M., et al. (2022) · Nature Methods · doi:10.1038/s41592-022-01480-9 · Cited by 459
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Metadata
- Authors
- Lopez, R., Li, B., Keren-Shaul, H., Boyeau, P., Kedmi, M., Pilzer, D., Jelinski, A., Yofe, I., David, E., Wagner, A., Ergen, C., Addadi, Y., Golani, O., Ronchese, F., Jordan, M. I., Amit, I., & Yosef, N.
- Affiliations
- University of California, Berkeley (US)
- Weizmann Institute of Science (IL)
- Malaghan Institute of Medical Research (NZ)
- Ragon Institute of MGH, MIT and Harvard (US)
- Corresponding author
- Amit, Ido
- Yosef, Nir
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2022-04-21
- Volume
- 40
- Issue
- 9
- Pages
- 1360-1369
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC9756396
- OpenAlex
- W4224218779
- Semantic Scholar
- d88d598ef4cdd976986d65e6f9849e5b7b223ecb
- Cited by
- 276
- References
- 98
- Citations by year
- 2026: 63 · 2025: 67 · 2024: 71 · 2023: 40 · 2022: 25
- FWCI
- 17.06
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Biotechnology (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (U19 AI090023)
- Chan-Zuckerberg BioHub
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- spatial transcriptomics, cell type deconvolution, variational inference, single-cell RNA sequencing, gene expression estimation, lymph node infection, mouse tumor model, cell organization
- MeSH terms
- Exome Sequencing, Animals, Neoplasms, Software, Gene Expression Profiling, Mice, Single-Cell Analysis, Transcriptome
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