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DestVI identifies continuums of cell types in spatial transcriptomics data
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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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“For example, DestVI uses a reference scRNA-seq dataset to deconvolve or attempt to identify unique cell types within a given SRT spot [183].”
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“We applied a recently developed method, “ DestVI ” ( 31 ), to annotate the segmented single cells on the Stereo-seq map ( 28 ).”
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“External benchmark study [64] showed Tangram had decent deconvolution performance across diverse real and synthetic datasets and top performance in predicting spatial distribution of gene expression compared to Seurat [65], Cell2location [66], SpatialDWLS [67], RCTD [68], Stereoscope [69], DestVI [60], STRIDE [70], SP…”
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“Larger spot sizes can be compensated for by integrating scT with ST and inferring the cell type composition of each spot (Andersson et al., 2020; Kleshchevnikov et al., 2022; Lopez et al., 2022; Ma and Zhou, 2022).”
BackgroundMethodCell-type deconvolution methods for spatial transcriptomics
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“In addition to general MAP inference, DestVI uses a penalized likelihood method to infer some parameters in LVMs. Finally, the cell type composition is estimated for each spot.”
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“Lopez et al. proposed DestVI, a method for deconvolving spatial transcriptomics pro fi les (Lopez et al., 2022).”
MethodTian, R., Xue, Z., Chen, Y., Qi, Y., Zhang, J., Yuan, J., Ruan, D., Lin, J., Liu, J., Wang, D., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-63915-z · Cited by 38
Deep learning in integrating spatial transcriptomics with other modalities
Luo, J., Fu, J., Lu, Z., & Tu, J. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae719 · Cited by 31
Spatial transcriptomics: a new frontier in cancer research
Huang, S., Ouyang, L., Tang, J., Qian, K., Chen, X., Xu, Z., Ming, J., & Xi, R. (2024) · Clinical Cancer Bulletin · doi:10.1007/s44272-024-00018-8 · Cited by 34
“Several tools in this field have been developed to perform deconvolution effectively, such as SPOT-light [117], SpatialDWLS [118], DSTG [130], cell2loca-tion [119], CARD [120], RCTD [123], destVI [121] and STRIDE [122].”
Spatiotemporal Omics-Refining the landscape of precision medicine
Zhang, J., Yin, J., Heng, Y., Xie, K., Chen, A., Amit, I., Bian, X.-W., & Xu, X. (2022) · Life Medicine · doi:10.1093/lifemedi/lnac053 · Cited by 27
Self-citationLiang, Y., Bu, Q., You, W., Zhang, R., Xu, Z., Gan, X., Zhou, J., Qiao, L., Huang, T., & Lü, L. (2024) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2024.167276 · Cited by 26
A deep learning-based multiscale integration of spatial omics with tumor morphology
Schmauch, B., Herpin, L., Olivier, A., Duboudin, T., Dubois, R., Gillet, L., Filiot, A., Schiratti, J.-B., Proietto, V. D., Corre, D. L., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-66691-y · Cited by 26
Hong, R., Tong, Y., Tang, H., Zeng, T., & Liu, R. (2024) · Research · doi:10.34133/research.0522 · Cited by 25
The promise and challenge of spatial omics in dissecting tumour microenvironment and the role of AI
Lee, R. Y., Ng, C. W., Rajapakse, M. P., Ang, N., Yeong, J. P.-S., & Lau, M. C. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1172314 · Cited by 28
“These include a Bayesian modelling method called DestVI (118); methods that infer spatial cell composition from scRNAseq data such as CellDART (119) and Tangram (120); a graph-based CNN method called DSTG (121) which was used to uncover cell states of pancreatic tumor tissues.”
MethodWang, F., Yang, F., Huang, L.-K., Li, W., Song, J., Gasser, R. B., Aebersold, R. H., Wang, G., & Yao, J. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00737-y · Cited by 26
“…for transcriptomic data, including Scaden (a deep-learning-based method) 6 , MuSiC (a linear-regression-based method) 1 , BayesPrism (a Bayes-based method) 28 , DestVI (a distribution-hypothesis-based method) 29 and CIBERSORTx (a widely-used linear support vector regression-based method) 5 .”
MethodIntegrating single-cell RNA-seq datasets with substantial batch effectsPreprint
Hrovatin, K., Moinfar, A. A., Zappia, L., Tejada-Lapuerta, A., Lengerich, B. J., Kellis, M., & Theis, F. J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.03.565463 · Cited by 24
“It was previously shown that this also applies to single-cell data 30 , similar to other flexible priors, such as standard Gaussian mixtures (GM) 32–34 , and may be used to add prior information to latent space regularization 35 .”
EnDecon: cell type deconvolution of spatially resolved transcriptomics data via ensemble learning
Tu, J.-J., Li, H.-S., Yan, H., & Zhang, X.-F. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac825 · Cited by 22
Avraham‐Davidi, I., Mages, S., Klughammer, J., Moriel, N., Imada, S., Hofree, M., Murray, E. M., Chen, J. H., Pelka, K., Mehta, A., et al. (2025) · eLife · doi:10.1101/2022.10.02.508492 · Cited by 24
Chen, J., Luo, T., Jiang, M.-Z., Liu, J., Gupta, G. P., & Li, Y. (2023) · Science Advances · doi:10.1126/sciadv.add9818 · Cited by 22
“We compared POLARIS with five state-of-the-art methods: CARD ( 26 ), DestVI ( 29 ), RCTD ( 28 ), stereoscope ( 27 ), and SPOTlight ( 30 ).”
Spatial omics techniques and data analysis for cancer immunotherapy applications
Zhang, Y., Lee, R. Y., Tan, C. W., Guo, X., Yim, W. W., Lim, J., Wee, F. Y. T., Wu, Y., Kharbanda, M., Lee, J.-Y. J., et al. (2024) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2024.103111 · Cited by 23
Simulating multiple variability in spatially resolved transcriptomics with scCube
Qian, J., Bao, H., Shao, X., Yin, F., Liao, J., Chen, Z., Li, C., Guo, W., Hu, Y., Li, A., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-49445-0 · Cited by 23
Zhang, H., Hunter, M. V., Chou, J., Quinn, J. F., Zhou, M., White, R. M., & Tansey, W. (2023) · Cell Systems · doi:10.1016/j.cels.2023.06.003 · Cited by 22
Unravelling tumour spatiotemporal heterogeneity using spatial multimodal data
Zuo, C., Zhu, J., Zou, J., & Chen, L. (2025) · Clinical and Translational Medicine · doi:10.1002/ctm2.70331 · Cited by 24
SIMVI disentangles intrinsic and spatial-induced cellular states in spatial omics data
Dong, M., Su, D. G., Kluger, H. M., Fan, R., & Kluger, Y. (2025) · Nature Communications · doi:10.1038/s41467-025-58089-7 · Cited by 25
Cancer therapy resistance from a spatial‐omics perspective
Zhang, Y., Yang, C., Chen, X., Wu, L. L., Yuan, Z., Zhang, F., & Qian, B. (2025) · Clinical and Translational Medicine · doi:10.1002/ctm2.70396 · Cited by 19
Scvi-hub: an actionable repository for model-driven single-cell analysis
Ergen, C., Amiri, V. V. P., Kim, M., Kronfeld, O., Streets, A., Gayoso, A., & Yosef, N. (2025) · Nature Methods · doi:10.1038/s41592-025-02799-9 · Cited by 26
“Beyond deconvolution, the use of DestVI estimates cell-type-specific gene expression inside each relevant spot, thus opening the way for downstream analysis such as comparing how cells of the same type differ between tissue areas 7 .”
Self-citationYin, W., Wan, Y., & Zhou, Y. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae130 · Cited by 18
“In contrast to results using the internal reference, CARD and DestVI [13] showed the most obvious performance decrease,while the results ofCell2location weresignificantlyimprovedwhenusingtheexter-nal reference (Figure 9A and B).”
MethodResultInfluentialSpatial Transcriptomics of the Respiratory System
Megas, S., Wilbrey-Clark, A., Maartens, A. P., Teichmann, S. A., & Meyer, K. B. (2024) · Annual Review of Physiology · doi:10.1146/annurev-physiol-022724-105144 · Cited by 18
Zhang, D., Yu, N., Yuan, Z., Li, W., Sun, X. G., Zou, Q., Li, X., Liu, Z., Zhang, W., & Gao, R. (2024) · GigaScience · doi:10.1093/gigascience/giae089 · Cited by 22
Zhang, D.-J., Qi, R., Lan, X., & Liu, B. (2025) · Genome Research · doi:10.1101/gr.280281.124 · Cited by 19
Profiling immune cell tissue niches in the spatial -omics era
Lee, C. Y. C., McCaffrey, J., McGovern, D., & Clatworthy, M. R. (2024) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2024.11.001 · Cited by 21
Integrating single-cell RNA-seq datasets with substantial batch effects
Hrovatin, K., Moinfar, A. A., Zappia, L., Parikh, S., Tejada-Lapuerta, A., Lengerich, B. J., Kellis, M., & Theis, F. J. (2025) · BMC Genomics · doi:10.1186/s12864-025-12126-3 · Cited by 47
“Additionally, other approaches, such as constraining the cVAE’s prior distribution with cell type information, have been explored [71].”
Navigating the landscapes of spatial transcriptomics: How computational methods guide the way
Li, R., Chen, X., & Yang, X. (2024) · Wiley Interdisciplinary Reviews - RNA · doi:10.1002/wrna.1839 · Cited by 15
“DestVI uses conditional deep generative models to learn cell types and identify transcriptomic variations within the same cell type, thereby facilitating the discovery of cell subtypes (Lopez et al., 2022).”
MethodZhang, H., Lu, X., Lu, B., Gullo, G., & Chen, L. (2024) · Future Oncology · doi:10.2217/fon-2023-0658 · Cited by 16
High-resolution mapping of single cells in spatial context
Ke, J., Xu, J., Jia Liu, B., Yang, Y., Guo, C., Xie, B., Cui, G., Peng, G., & Suo, S. (2025) · Nature Communications · doi:10.1038/s41467-025-61667-4 · Cited by 13
Niyakan, S., Sheng, J., Cao, Y., Zhang, X. H.-F., Xu, Z., Wu, L., Wong, S. T. C., & Qian, X. (2024) · Patterns · doi:10.1016/j.patter.2024.100986 · Cited by 13
Single-Cell Sequencing: High-Resolution Analysis of Cellular Heterogeneity in Autoimmune Diseases
Tang, X., Zhang, Y., Zhang, H., Zhang, N., Dai, Z., Cheng, Q., & Li, Y. (2024) · Clinical Reviews in Allergy & Immunology · doi:10.1007/s12016-024-09001-6 · Cited by 15
scDOT: optimal transport for mapping senescent cells in spatial transcriptomics
Nguyen, N. D., Rosas, L., Khaliullin, T. O., Jiang, P., Hasanaj, E., Ovando-Ricárdez, J. A., Bueno, M., Rahman, I., Pryhuber, G., Li, D., et al. (2024) · Genome biology · doi:10.1186/s13059-024-03426-0 · Cited by 12
Multi-omics and 3D-imaging reveal bone heterogeneity and unique calvaria cells in neuroinflammationPreprint
Kolabas, Z. I., Kuemmerle, L. B., Perneczky, R., Förstera, B., Büttner, M., Çalışkan, Ö. Ş., Ali, M., Rong, Z., Mai, H., Hummel, S., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.24.473988 · Cited by 15
Deep learning models will shape the future of stem cell research
Ouyang, J. F., Chothani, S. P., & Rackham, O. J. L. (2023) · Stem Cell Reports · doi:10.1016/j.stemcr.2022.11.007 · Cited by 13
“The DestVI model (Lopez et al., 2022) deconvolutes the cell types observed in spatial transcriptomics by training two autoencoders to learn cell-type-specific gene expression patterns and the spatial context of these patterns.”
MethodTao, Q., Xu, Y., He, Y., Luo, T., Li, X.-M., & Han, L. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae250 · Cited by 14
“DestVI [18] incorporates a variance penalty based on all genes and utilizes a neural network to parameterize potential variables as a function of input reference data.”
BackgroundNext-generation deconvolution of transcriptomic data to investigate the tumor microenvironment
Merotto, L., Zopoglou, M., Zackl, C., & Finotello, F. (2023) · International review of cytology · doi:10.1016/bs.ircmb.2023.05.002 · Cited by 14
Jiang, S., Freedman, J., Mantri, M., Maymí, V. I., Leddon, S. A., Schweitzer, P. A., Bhandari, S., Holdener, C., Ntekas, I., Vollmers, C., et al. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2504742123 · Cited by 12
Hahn, N., Bens, M., Kempfer, M., Reißig, C., Schmidl, L., & Geis, C. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1198154 · Cited by 11
“High-resolution single nuclei data can then be mapped back to the spatial transcriptome data using deconvolution algorithms (Elosua-Bayes et al., 2021; Cable et al., 2022; Kleshchevnikov et al., 2022; Lopez et al., 2022) to infer the cell composition of each spot.”
MethodMa, M., Luo, Q., Chen, L., Liu, F., Yin, L., & Guan, B. (2025) · BMC Nephrology · doi:10.1186/s12882-025-04103-5 · Cited by 13
“D e s t V I [ 88 ] S p a t i a l t r a n s c r i p t o m i c d a t a P r o b l e m a t i c m e t h o d f o r m u l t i - r e s o l u t i o n a n a l y s i s I d e n t i fi e s i m p o r t a n t c e ll t y p e - s p e c i fi c c h a n g e s i n i n t r a c e ll u l a r t r a n s c r i p t o m e v a…”
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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