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 latent representations of scRNA-seq datasets and spatial spot expression datasets to deconvolute cell-type proportions and related transcriptional states in single spots.
“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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MethodInfluentialSelf-citationEmbryo-scale, single-cell spatial transcriptomics
Srivatsan, S. R., Regier, M. C., Barkan, E. R., Franks, J. M., Packer, J. S., Grosjean, P., Duran, M., Saxton, S. H., Ladd, J. J., Spielmann, M., et al. (2021) · Science · doi:10.1126/science.abb9536 · Cited by 265 · cited 2× · see citing works
“Using a dataset of mouse embryo cortex [35], we apply our full battery of tests from the simulation benchmarking, and report our results in Supplementary Figure 5.”
MethodDSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence
Song, Q., & Su, J. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa414 · Cited by 224 · cited 1× · see citing works
“The convention for analyzing such pairs of datasets (as implemented by all existing pipelines, including NMFReg [14], RCTD [16], SPOTLight [17], Stereoscope [18], DSTG [19], and cell2location [20]) is to apply a two-step process.”
MethodMultimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma
Ji, A. L., Rubin, A. J., Thrane, K., Jiang, S., Reynolds, D. L., Meyers, R. M., Guo, M. G., George, B. M., Mollbrink, A., Bergenstråhle, J., et al. (2020) · Cell · doi:10.1016/j.cell.2020.08.043 · Cited by 215 · see citing works
Modeling Cell-Cell Interactions from Spatial Molecular Data with Spatial Variance Component Analysis
Arnol, D., Schapiro, D., Bodenmiller, B., Sáez-Rodríguez, J., & Stegle, O. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.08.077 · Cited by 210 · see citing works
SpaGE: Spatial Gene Enhancement using scRNA-seq
Abdelaal, T., Mourragui, S., Mahfouz, A., & Reinders, M. J. T. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa740 · Cited by 207 · see citing works
Data denoising with transfer learning in single-cell transcriptomics
Wang, J., Agarwal, D., Huang, M. C., Hu, G., Zhou, Z., Ye, C., & Zhang, N. R. (2019) · Nature Methods · doi:10.1038/s41592-019-0537-1 · Cited by 190 · cited 1× · see citing works
“This idea was introduced in the context of scRNA-seq imputation by the SAVER-X method, in which an autoencoder alongside the high-dimensional expression data is used to perform accurate data imputation [64].”
MethodBuild, Compute, Critique, Repeat: Data Analysis with Latent Variable Models
Blei, D. M. (2014) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-022513-115657 · Cited by 159 · cited 1× · see citing works
“DestVI uses two different latent variable models (LVMs) [27] for delineating cell-type proportions as well as cell-type-specific continuous sub-states.”
MethodRobust decomposition of cell type mixtures in spatial transcriptomicsPreprint
Cable, D. M., Murray, E. M., Zou, L. S., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.07.082750 · Cited by 154 · see citing works
Robust linear dimensionality reduction
Koren, Y., & Carmel, L. (2004) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2004.17 · Cited by 152 · see citing works
Space-time logic of liver gene expression at sub-lobular scale
Droin, C., Kholtei, J. E., Bahar Halpern, K., Hurni, C., Rozenberg, M., Muvkadi, S., Itzkovitz, S., & Naef, F. (2021) · Nature Metabolism · doi:10.1038/s42255-020-00323-1 · Cited by 148 · cited 1× · see citing works
“Turning to hepatocytes, we used DestVI to explore how hepatocyte-specific gene expression pertains to liver zonation [77].”
MethodMacrophage arginase-1 controls bacterial growth and pathology in hypoxic tuberculosis granulomas
Duque-Correa, M. A., Kühl, A. A., Rodriguez, P. C., Zedler, U., Schommer-Leitner, S., Rao, M., Weiner, J., Hurwitz, R., Qualls, J. E., Kosmiadi, G. A., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1408839111 · Cited by 146 · cited 1× · see citing works
“This observation is also consistent with previous work, as cells with similar phenotypes have been observed to congregate in hypoxic tissue niches (cancerous [60] and non-cancerous [61]).”
ResultKumar, S., Sharife, H., Kreisel, T., Mogilevsky, M., Bar-Lev, L., Grunewald, M., Aizenshtein, E., Karni, R., Paldor, I., Shlomi, T., et al. (2019) · Cell Metabolism · doi:10.1016/j.cmet.2019.04.003 · Cited by 122 · cited 1× · see citing works
“Furthermore, our spatial analysis identified a discrete sub-population of immunosuppressive macrophages, expressing an hypoxia activation signature, within the tumor core [25].”
BackgroundSimulating multiple faceted variability in single cell RNA sequencing
Zhang, X., Xu, C. A., & Yosef, N. (2019) · Nature Communications · doi:10.1038/s41467-019-10500-w · Cited by 120 · cited 1× · see citing works
“A similar approach was taken in the design of the Simsym simulator for scRNA-seq data [84].”
MethodSelf-citationLeal, J. M., Huang, J. Y., Kohli, K., Stoltzfus, C. R., Lyons‐Cohen, M. R., Olin, B. E., Gale, M., & Gerner, M. Y. (2021) · Science Immunology · doi:10.1126/sciimmunol.abb9435 · Cited by 109 · see citing works
scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies
Schmid, K. T., Höllbacher, B., Cruceanu, C., Böttcher, A., Lickert, H., Binder, E. B., Theis, F. J., & Heinig, M. (2021) · Nature Communications · doi:10.1038/s41467-021-26779-7 · Cited by 108 · cited 1× · see citing works
“In general, we suggest that users of DestVI prepare their single-cell RNA sequencing data according to power analysis tools [69].”
MethodGarraud, O., Borhis, G., Badr, G., Degrelle, S. A., Pozzetto, B., Cognasse, F., & Richard, Y. (2012) · BMC Immunology · doi:10.1186/1471-2172-13-63 · Cited by 100 · cited 1× · see citing works
“As expected, the first striking pattern is the organization of the lymph node sections into the B cell follicles (Cd79a; external area) and a T cell compartment (CD8; internal area) (Figure 3E; Supplementary Figure 11) [42,43].”
BackgroundJoint cell segmentation and cell type annotation for spatial transcriptomics
Littman, R., Hemminger, Z., Foreman, R., Arneson, D. V., Zhang, G., Gómez‐Pinilla, F., Yang, X., & Wollman, R. (2021) · Molecular Systems Biology · doi:10.15252/msb.202010108 · Cited by 96 · cited 1× · see citing works
“However, these methods rely on cell-segmentation algorithms [8,9].”
MethodLymph node conduits transport virions for rapid T cell activation
Reynoso, G. V., Weisberg, A. S., Shannon, J. P., McManus, D. T., Shores, L. S., Americo, J. L., Stan, R. V., Yewdell, J. W., & Hickman, H. D. (2019) · Nature Immunology · doi:10.1038/s41590-019-0342-0 · Cited by 90 · see citing works
Kleshchevnikov, V., Shmatko, A., Dann, E., Aivazidis, A., King, H. W., Li, T., Lomakin, A., Kedlian, V. R., Jain, M. S., Park, J. S., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.15.378125 · Cited by 88 · cited 3× · see citing works
“15 ) and is expected, as these embedding methods (for example, scVI) do not explicitly consider that spatial spots may include a mixture of cell types.”
BackgroundSubcapsular Sinus Macrophages: The Seat of Innate and Adaptive Memory in Murine Lymph Nodes
Moran, I., Grootveld, A. K., Nguyen, A., & Phan, T. G. (2018) · Trends in Immunology · doi:10.1016/j.it.2018.11.004 · Cited by 87 · see citing works
Enhancing scientific discoveries in molecular biology with deep generative models
Lopez, R., Gayoso, A., & Yosef, N. (2020) · Molecular Systems Biology · doi:10.15252/msb.20199198 · Cited by 82 · cited 2× · see citing works
“Unlike other methods, DestVI learns both discrete cell-type-specific profiles and continuous sub-cell-type latent variations using a conditional deep generative model 18 .”
BackgroundMethodSelf-citationHampton, H. R., & Chtanova, T. (2016) · Seminars in Immunology · doi:10.1016/j.smim.2016.03.008 · Cited by 78 · cited 1× · see citing works
“As expected, the first striking pattern is the organization of the lymph node sections into the B cell follicles (Cd79a; external area) and a T cell compartment (CD8; internal area) (Figure 3E; Supplementary Figure 11) [42,43].”
BackgroundCharacterization of Kupffer cells in livers of developing mice
Lopez, B. G., Tsai, M. S., Baratta, J. L., Longmuir, K. J., & Robertson, R. T. (2011) · Comparative Hepatology · doi:10.1186/1476-5926-10-2 · Cited by 73 · see citing works
Spatial detection of fetal marker genes expressed at low level in adult human heart tissue
Asp, M., Salmén, F., Ståhl, P. L., Vicković, S., Felldin, U., Löfling, M., Navarro, J. F., Maaskola, J., Eriksson, M. J., Persson, B., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-13462-5 · Cited by 72 · cited 1× · see citing works
“In particular, this emerging technology helped study the organization of complex tissues such as the mouse brain [2] and the human heart [3].”
BackgroundNeuropathology for the neuroradiologist: palisades and pseudopalisades.
Wippold, F. J., Lämmle, M., Anatelli, F., Lennerz, J. K., & Perry, A. (2006) · PubMed Central · Cited by 70 · see citing works
Wei, S., Shreiner, A. B., Takeshita, N., Chen, L., Zou, W., & Chang, A. E. (2008) · Cancer Research · doi:10.1158/0008-5472.can-07-6598 · Cited by 67 · cited 1× · see citing works
“MCA205 is a weakly immunogenic fibrosarcoma-derived cell line which is a common model used to study the immune responses to tumors and for developing immunotherapies [51].”
Backgroundscvi-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 · cited 1× · see citing works
“DestVI is implemented in the scvi-tools package 21 and is readily available as open source, along with the accompanying tutorials.”
MethodSelf-citationCoombes, J. L., Han, S., van Rooijen, N., Raulet, D. H., & Robey, E. A. (2012) · Cell Reports · doi:10.1016/j.celrep.2012.06.001 · Cited by 63 · see citing works
Boyeau, P., Regier, J., Gayoso, A., Jordan, M. I., Lopez, R., & Yosef, N. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2209124120 · Cited by 56 · see citing works
Self-citationMassi, D., Marconi, C., Franchi, A., Bianchini, F., Paglierani, M., Ketabchi, S., Miracco, C., Santucci, M., & Calorini, L. (2007) · Human Pathology · doi:10.1016/j.humpath.2007.02.018 · Cited by 53 · cited 1× · see citing works
“We found that the tumor-infiltrating myeloid suppressive cells (Mreg and TAM) were mostly abundant in the inner layers of the MCA205 tumor, consistent with previous observations in human tumors [55] and murine models [54].”
ResultBlecher‐Gonen, R., Bost, P., Hilligan, K. L., David, E., Salame, T. M., Roussel, E., Connor, L. M., Mayer, J. U., Halpern, K. B., Tóth, B., et al. (2019) · Cell Systems · doi:10.1016/j.cels.2019.01.001 · Cited by 51 · cited 19× · see citing works
“This finding is consistent with our previous work, which showed that, after immunization with MS, NK cells are recruited to the lymph nodes and produce IFN γ .”
BackgroundMethodResultInfluentialSelf-citationWrit large: Genomic dissection of the effect of cellular environment on immune response
Yosef, N., & Regev, A. (2016) · Science · doi:10.1126/science.aaf5453 · Cited by 50 · cited 1× · see citing works
“Spatial transcriptomics is a promising approach for unravelling cell-cell interactions [78] and other forms of cellular communication and function in a tissue [79].”
BackgroundSelf-citationBiancalani, T., Scalia, G., Buffoni, L., et al. (2020) · bioRxiv · doi:10.1101/2020.08.29.272831 · Cited by 38 · see citing works
Decision-Making with Auto-Encoding Variational BayesPreprint
Lopez, R., Boyeau, P., Yosef, N., Jordan, M. I., & Regier, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.07217 · Cited by 29 · see citing works
Self-citationSpatially resolved transcriptomics reveals the architecture of the tumor/microenvironment interfacePreprint
Hunter, M. V., Moncada, R. M., Weiss, J. M., Yanai, I., & White, R. M. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.05.368753 · Cited by 24 · cited 8× · see citing works
“For scRNA-seq data generation, we refer to the recent review on deconvolution 47 and provide more specific recommendations in Supplementary Note 5.”
MethodInfluentialBayesian segmentation of spatially resolved transcriptomics dataPreprint
Petukhov, V., Soldatov, R., Khodosevich, K. V., & Kharchenko, P. V. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.10.05.326777 · Cited by 19 · cited 1× · see citing works
“However, these methods rely on cell-segmentation algorithms [8,9].”
MethodSpatialDE - Identification of spatially variable genesPreprint
Svensson, V., Teichmann, S. A., & Stegle, O. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/143321 · Cited by 17 · see citing works
Steinberg, J. D., Raju, A., Chandrasekharan, P., et al. (2014) · EJNMMI Research · doi:10.1186/2191-219x-4-15 · Cited by 14 · cited 2× · see citing works
“To build the spatial transcriptomics data, we first constructed a regular grid of dimension 40 × 40.”
BackgroundMethodGeneric Inference in Latent Gaussian Process ModelsPreprint
Bonilla, E. V., Krauth, K., & Dezfouli, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.00577 · Cited by 14 · cited 1× · see citing works
“A plethora of computational techniques, based on black-box inference techniques [65,66] for Gaussian processes [67] is likely to make this possible.”
BackgroundDeep Generative Models for Detecting Differential Expression in Single CellsPreprint
Boyeau, P., Lopez, R., Regier, J., Gayoso, A., Jordan, M. I., & Yosef, N. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/794289 · Cited by 13 · cited 2× · see citing works
“In our previous work, we used a purely Bayesian approach to differential expression [62,63].”
MethodSelf-citationIdentifying Informative Gene Modules Across Modalities of Single Cell GenomicsPreprint
DeTomaso, D., & Yosef, N. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.02.06.937805 · Cited by 13 · see citing works
Self-citationComparison and evaluation of statistical error models for scRNA-seqPreprint
Choudhary, S., & Satija, R. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.07.07.451498 · Cited by 8 · see citing works
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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 · see citing works
An introduction to spatial transcriptomics for biomedical research
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A Python library for probabilistic analysis of single-cell omics data
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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 515 · see citing works
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Self-citationIntegrative spatial analysis reveals a multi-layered organization of glioblastoma
Greenwald, A., Darnell, N. G., Hoefflin, R., Simkin, D., Mount, C., Castro, L. N. G., Harnik, Y., Dumont, S. D., Hirsch, D., Nomura, M., et al. (2024) · Cell · doi:10.1016/j.cell.2024.03.029 · Cited by 397 · see citing works
Self-citationIL-1β+ macrophages fuel pathogenic inflammation in pancreatic cancer
Caronni, N., Terza, F. L., Vittoria, F. M., Barbiera, G., Mezzanzanica, L., Cuzzola, V., Barresi, S., Pellegatta, M., Canevazzi, P., Dunsmore, G., et al. (2023) · Nature · doi:10.1038/s41586-023-06685-2 · Cited by 408 · see citing works
Unsupervised spatially embedded deep representation of spatial transcriptomics
Xu, H., Fu, H., Long, Y., Ang, K. S., Sethi, R., Chong, K., Li, M., Uddamvathanak, R., Lee, H. K., Ling, J., et al. (2024) · Genome Medicine · doi:10.1186/s13073-024-01283-x · Cited by 402 · see citing works
Spatial components of molecular tissue biology
Palla, G., Fischer, D. S., Regev, A., & Theis, F. J. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01182-1 · Cited by 315 · see citing works
Li, H., Zhou, J., Li, Z., Chen, S., Liao, X., Zhang, B., Zhang, R., Wang, Y., Sun, S., & Gao, X. (2023) · Nature Communications · doi:10.1038/s41467-023-37168-7 · Cited by 240 · see citing works
Advances in spatial transcriptomic data analysisPreprint
Dries, R., Chen, J. G., Rossi, N. D., Khan, M. M., Sistig, A., & Yuan, G. (2021) · Genome Research · doi:10.1101/gr.275224.121 · Cited by 236 · see citing works
A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell–Cell Communication
Cheng, C., Chen, W., Jin, H., & Chen, X. '. (2023) · Cells · doi:10.3390/cells12151970 · Cited by 237 · see citing works
Clinical and translational values of spatial transcriptomics
Zhang, L., Chen, D., Song, D., Liu, X., Zhang, Y., Xu, X., & Wang, X. (2022) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-022-00960-w · Cited by 212 · see citing works
Spatial biology of cancer evolution
Seferbekova, Z., Lomakin, A., Yates, L. R., & Gerstung, M. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-022-00553-x · Cited by 210 · see citing works
An integrated single cell and spatial transcriptomic map of human white adipose tissue
Massier, L., Jalkanen, J., Elmastas, M., Zhong, J., Wang, T., Nono Nankam, P. A., Frendo-Cumbo, S., Bäckdahl, J., Subramanian, N., Sekine, T., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36983-2 · Cited by 195 · see citing works
Modeling intercellular communication in tissues using spatial graphs of cells
Fischer, D. S., Schaar, A. C., & Theis, F. J. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01467-z · Cited by 217 · see citing works
“We considered data from lymph nodes 18,19 (Extended Data Fig.”
BackgroundMethodLiu, Z., Kong, X., Long, Y., Liu, S., Zhang, H., Jia, J., Cui, W., Zhang, Z., Song, X., Qiu, L., et al. (2023) · Nature Plants · doi:10.1038/s41477-023-01387-z · Cited by 172 · see citing works
Wan, X., Xiao, J., Tam, S. S. T., Cai, M., Sugimura, R., Wang, Y., Wan, X., Lin, Z., Wu, A. R., & Yang, C. (2023) · Nature Communications · doi:10.1038/s41467-023-43629-w · Cited by 152 · see citing works
Mapping cells through time and space with moscot
Klein, D., Palla, G., Lange, M., Klein, M., Piran, Z., Gander, M., Meng-Papaxanthos, L., Sterr, M., Saber, L., Jing, C., et al. (2025) · Nature · doi:10.1038/s41586-024-08453-2 · Cited by 202 · see citing works
Deep learning applications in single-cell genomics and transcriptomics data analysis
Erfanian, N., Heydari, A. A., Feriz, A. M., Picazo, P. I., Derakhshani, A., GhasemiGol, M., Farahpour, M., Razavi, S. M., Nasseri, S., Safarpour, H., et al. (2023) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2023.115077 · Cited by 162 · see citing works
Spatial transcriptomics in health and disease
Jain, S. R., & Eadon, M. T. (2024) · Nature Reviews Nephrology · doi:10.1038/s41581-024-00841-1 · Cited by 149 · see citing works
SPACEL: deep learning-based characterization of spatial transcriptome architectures
Xu, H., Wang, S., Fang, M., Luo, S., Chen, C., Wan, S., Wang, R., Tang, M., Xue, T., Li, B., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43220-3 · Cited by 130 · see citing works
Self-citationThe cycling and aging mouse female reproductive tract at single-cell resolution
Winkler, I., Tolkachov, A., Lammers, F., Lacour, P., Daugėlaitė, K., Schneider, N. G., Koch, M.-L., Panten, J., Grünschläger, F., Poth, T., et al. (2024) · Cell · doi:10.1016/j.cell.2024.01.021 · Cited by 133 · see citing works
Lymphatics act as a signaling hub to regulate intestinal stem cell activity
Niec, R. E., Chu, T., Schernthanner, M., Gur‐Cohen, S., Hidalgo, L., Pasolli, H. A., Luckett, K. A., Wang, Z., Bhalla, S. R., Cambuli, F., et al. (2022) · Cell stem cell · doi:10.1016/j.stem.2022.05.007 · Cited by 122 · see citing works
Unsupervised Spatially Embedded Deep Representation of Spatial TranscriptomicsPreprint
Fu, H., Xu, H., Chong, K., Li, M., Ang, K. S., Lee, H. K., Ling, J., Chen, A., Shao, L., Liu, L., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.06.15.448542 · Cited by 98 · see citing works
Application of Deep Learning on Single-Cell RNA Sequencing Data Analysis: A Review
Brendel, M., Su, C., Bai, Z., Zhang, H., Elemento, O., & Wang, F. (2022) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.11.011 · Cited by 102 · see citing works
“For example, DestVI uses a reference scRNA-seq dataset to deconvolve or attempt to identify unique cell types within a given SRT spot [183].”
MethodPark, H., Jo, S. H., Lee, R. H., Macks, C. P., Ku, T., Park, J., Lee, C. W., Hur, J. K., & Sohn, C. H. (2023) · Advanced Science · doi:10.1002/advs.202206939 · Cited by 98 · see citing works
Benchmarking and integration of methods for deconvoluting spatial transcriptomic data
Yan, L., & Sun, X. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btac805 · Cited by 92 · see citing works
Principles and challenges of modeling temporal and spatial omics data
Velten, B., & Stegle, O. (2023) · Nature Methods · doi:10.1038/s41592-023-01992-y · Cited by 90 · see citing works
Evaluating spatially variable gene detection methods for spatial transcriptomics data
Chen, C., Kim, H. J., & Yang, P. (2024) · Genome biology · doi:10.1186/s13059-023-03145-y · Cited by 75 · see citing works
“• Lopez et al., Visium [46].”
Single-cell omics: experimental workflow, data analyses and applications
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A comprehensive comparison on cell-type composition inference for spatial transcriptomics data
Chen, J., Liu, W., Luo, T., Yu, Z., Jiang, M.-Z., Wen, J., Gupta, G. P., Giusti‐Rodríguez, P., Hongtu Zhu, B., Yang, Y., et al. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac245 · Cited by 76 · see citing works
A guidebook of spatial transcriptomic technologies, data resources and analysis approaches
Yue, L., Liu, F., Hu, J., Yang, P., Wang, Y., Dong, J., Shu, W., Huang, X., & Wang, S. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.01.016 · Cited by 80 · see citing works
Spatial transcriptomics deconvolution at single-cell resolution using Redeconve
Zhou, Z., Zhong, Y., Zhang, Z., & Ren, X. (2023) · Nature Communications · doi:10.1038/s41467-023-43600-9 · Cited by 79 · see citing works
Wang, G., Zhao, J., Yan, Y., Wang, Y., Wu, A. R., & Yang, C. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00734-1 · Cited by 81 · see citing works
“We also compared STitch3D with cell-type deconvolution methods such as RCTD 15 , Cell2location 16 , Tangram 17 , DestVI 18 and CARD 19 .”
MethodInfluentialMultiscale topology classifies cells in subcellular spatial transcriptomics
Benjamin, K., Bhandari, A., Kepple, J. D., Qi, R., Shang, Z., Xing, Y., An, Y., Zhang, N., Hou, Y., Crockford, T. L., et al. (2024) · Nature · doi:10.1038/s41586-024-07563-1 · Cited by 81 · see citing works
Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis
Liu, J., Cen, X., Yi, C., Wang, F.-A., Ding, J., Cheng, J., Wu, Q., Gai, B., Zhou, Y., He, R., et al. (2025) · Genomics, Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzaf011 · Cited by 75 · see citing works
“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].”
He, S., Jin, Y., Nazaret, A., Shi, L., Chen, X., Rampersaud, S., Dhillon, B. S., Valdez, I. K., Friend, L. E., Fan, J. L., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02173-8 · Cited by 76 · see citing works
“Most existing computational methods for analyzing ST data (Cell2location 8 , DestVI 9 , Tangram 10 , Stereoscope 11 , RCTD 12 , BayesPrism 13 and so on) require paired and annotated single-cell data as references to overcome this challenge and are not capable of integrating tissue samples.”
InfluentialIntegrative multi-omics profiling in human decedents receiving pig heart xenografts
Schmauch, E., Piening, B., Mohebnasab, M., Xia, B., Zhu, C., Stern, J. M., Zhang, W., Dowdell, A. K., Kim, J. I., Andrijević, D., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02972-1 · Cited by 71 · see citing works
Spatial transcriptomics in development and disease
Zhou, R., Yang, G., Zhang, Y., & Wang, Y. (2023) · Molecular Biomedicine · doi:10.1186/s43556-023-00144-0 · Cited by 70 · see citing works
“Alternative tools based on probabilistic models such as stereoscope [70], SpatialDecon [71], RCTD [72], DestVI [73], STRIDE [74], SpaCET [75], Spatial-LDA [76], Cell2location [77] and Cottrazm [78] deconvolute the cellular proportion of the spots in cooperating with the gene signatures from…”
MethodToward modeling metabolic state from single-cell transcriptomics
Hrovatin, K., Fischer, D. S., & Theis, F. J. (2021) · Molecular Metabolism · doi:10.1016/j.molmet.2021.101396 · Cited by 69 · see citing works
Deep learning in spatially resolved transcriptomics: a comprehensive technical view
Zahedi, R., Ghamsari, M. R. E., Argha, A., MacPhillamy, C., Beheshti, A., Alizadehsani, R., Lovell, N. H., Lotfollahi, M., & Alinejad‐Rokny, H. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae082 · Cited by 74 · see citing works
“The Leiden algorithm was used to cluster the spatial domain with the obtained embeddings. conST achieved an ARI of 0.65, approximately a 10% increase compared to other state-of-the-art methods in the sequencing-based datasets, and demonstrated high performance on the image-based datasets.”
InfluentialRecent advances in spatially resolved transcriptomics: challenges and opportunities
Lee, J., Yoo, M., & Choi, J. (2022) · BMB Reports · doi:10.5483/bmbrep.2022.55.3.014 · Cited by 61 · see citing works
Cross-species single-cell spatial transcriptomic atlases of the cerebellar cortex
Hao, S., Zhu, X., Huang, Z. R., Yang, Q., Liu, H.-C., Wu, Y. H., Zhan, Y., Dong, Y., Li, C., Wang, H., et al. (2024) · Science · doi:10.1126/science.ado3927 · Cited by 54 · see citing works
“We applied a recently developed method, “ DestVI ” ( 31 ), to annotate the segmented single cells on the Stereo-seq map ( 28 ).”
MethodSpatialDE2: Fast and localized variance component analysis of spatial transcriptomicsPreprint
Kats, I., Vento‐Tormo, R., & Stegle, O. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.27.466045 · Cited by 52 · see citing works
Mason, K., Sathe, A., Hess, P. R., Rong, J., Wu, C.-Y., Furth, E. E., Suszták, K., Levinsohn, J. L., Ji, H. P., & Zhang, N. R. (2024) · Genome biology · doi:10.1186/s13059-023-03159-6 · Cited by 55 · see citing works
Emerging artificial intelligence applications in Spatial Transcriptomics analysis
Li, Y., Stanojevic, S., & Garmire, L. X. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.05.056 · Cited by 54 · see citing works
“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…”
MethodInfluentialSpatial transcriptomics using multiplexed deterministic barcoding in tissue
Wirth, J., Huber, N., Yin, K., Brood, S., Chang, S., Martinez‐Jimenez, C. P., & Meier, M. (2023) · Nature Communications · doi:10.1038/s41467-023-37111-w · Cited by 48 · see citing works
“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
Gaspard-Boulinc, L., Gortana, L., Walter, T., Barillot, E., & Cavalli, F. M. G. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-025-00845-y · Cited by 54 · see citing works
“In terms of gene selection, some methods (for example, GraphST 91 and DestVI 47 ) focus on highly variable genes to capture the broad pattern of the scRNA-seq, whereas others (for example, CARD 38 and RCTD 54 ) perform differential gene expression analyses to identify key marker genes for each cell…”
Mapping cells through time and space with moscotPreprint
Klein, D., Palla, G., Lange, M., Klein, M., Piran, Z., Gander, M., Meng-Papaxanthos, L., Sterr, M., Bastidas-Ponce, A., Tarquis-Medina, M., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.11.540374 · Cited by 49 · see citing works
STdGCN: spatial transcriptomic cell-type deconvolution using graph convolutional networks
Li, Y., & Luo, Y. (2024) · Genome biology · doi:10.1186/s13059-024-03353-0 · Cited by 51 · see citing works
“…17 state-of-the-art deconvolution models, including stereoscope [15], RCTD [18], SPOTlight [16], SpatialDWLS [19], Cell2location [26], DSTG [20], CellDART [21], DestVI [22], STRIDE [23], Tangram [24], BayesPrism [25], AdRoit [17], SpatialDecon [28], SD2 [29], NMFreg [3], CARD [30], and SONAR [34].”
BackgroundMethodDeep learning in spatial transcriptomics: Learning from the next next-generation sequencing
Heydari, A. A., & Sindi, S. S. (2023) · Biophysics Reviews · doi:10.1063/5.0091135 · Cited by 44 · see citing works
Deconvolution algorithms for inference of the cell-type composition of the spatial transcriptome
Zhang, Y., Lin, X., Yao, Z., Sun, D., Lin, X., Wang, X., Yang, C. J., & Song, J. T. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.12.001 · Cited by 39 · see citing works
“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.”
MethodInfluentialSang-aram, C., Browaeys, R., Seurinck, R., & Saeys, Y. (2023) · eLife · doi:10.7554/elife.88431 · Cited by 50 · see citing works
Advancing biological understanding of cellular senescence with computational multiomics
Li, S., Agudelo Garcia, P. A., Aliferis, C., Becich, M. J., Calyeca, J., Cosgrove, B. D., Elisseeff, J., Farzad, N., Fertig, E. J., Glass, C., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02314-y · Cited by 40 · see citing works
New tools for immunologists: models of lymph node function from cells to tissues
Ozulumba, T., Montalbine, A. N., Ortiz-Cárdenas, J. E., & Pompano, R. R. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1183286 · Cited by 34 · see citing works
Maurer, K., Park, C. Y., Mani, S., Borji, M., Raths, F., Gouin, K. H., Penter, L., Jin, Y., Zhang, J. Y., Shin, C. S., et al. (2025) · Science Immunology · doi:10.1126/sciimmunol.adr0782 · Cited by 40 · see citing works
Machine learning integrative approaches to advance computational immunology
Curion, F., & Theis, F. J. (2024) · Genome Medicine · doi:10.1186/s13073-024-01350-3 · Cited by 35 · see citing works
“DestVI [159] relies on variational inference to predict discrete cell-type-specific profiles and continuous latent variables of cell-states to describe the tissue architecture, and it is able to recover the interferon-induced changes in gene expression and cell-type composition of the spatial…”
Unsupervised Spatially Embedded Deep Representation of Spatial TranscriptomicsPreprint
Chen, J., Fu, H., Xu, H., Chong, K., Li, M., Ang, K. S., Lee, H. K., Ling, J., Chen, A., Shao, L., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-665505/v1 · Cited by 32 · see citing works
A review on deep learning applications in highly multiplexed tissue imaging data analysis
Zidane, M., Makky, A., Bruhns, M., Rochwarger, A., Babaei, S., Claassen, M., & Schürch, C. M. (2023) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2023.1159381 · Cited by 30 · see citing works
“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 33 · see citing works
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 29 · see citing works
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 32 · see citing works
“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 · see citing works
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 · see citing works
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 · see citing works
Hong, R., Tong, Y., Tang, H., Zeng, T., & Liu, R. (2024) · Research · doi:10.34133/research.0522 · Cited by 24 · see citing works
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 27 · see citing works
“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 24 · see citing works
“…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 · see citing works
“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 24 · see citing works
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 22 · see citing works
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 · see citing works
“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 22 · see citing works
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 21 · see citing works
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 21 · see citing works
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 22 · see citing works
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 23 · see citing works
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 · see citing works
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 22 · see citing works
“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 · see citing works
“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 · see citing works
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 19 · see citing works
Zhang, D.-J., Qi, R., Lan, X., & Liu, B. (2025) · Genome Research · doi:10.1101/gr.280281.124 · Cited by 19 · see citing works
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 · see citing works
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 43 · see citing works
“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 · see citing works
“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 15 · see citing works
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 · see citing works
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 · see citing works
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 14 · see citing works
scDOT: optimal transport for mapping senescent cells in spatial transcriptomics
Nguyen, N. D., Rosas, L., Оскарович, Х. Т., 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 13 · see citing works
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 13 · see citing works
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 · see citing works
“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 · see citing works
“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 12 · see citing works
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 · see citing works
Hahn, N., Bens, M., Kempfer, M., et al. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1198154 · Cited by 11 · see citing works
“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 12 · see citing works
“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…”
- 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 · 2021: 9
- 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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