Cited by 279Single-cell and spatial transcriptomicsGene expression and cancer classificationGene Regulatory Network Analysis

The DOI matches its Crossref record; 99 of the 107 references it cites were found in a registry or an index, and 8 are not checked yet.

DestVI identifies continuums of cell types in spatial transcriptomics data

Open access

Lopez, Romain, Li, Baoguo, Keren-Shaul, Hadas, Boyeau, Pierre, Kedmi, Merav, Pilzer, David, Jelinski, Adam, Yofe, Ido, David, Eyal, Wagner, Allon, Ergen, Can, Addadi, Yoseph, Golani, Ofra, Ronchese, Franca, Jordan, Michael I., Amit, Ido, Yosef, Nir·2022·Nature Biotechnology

DOI 10.1038/s41587-022-01272-8PMC PMC9756396

Published version of a preprint

Open this paper in CoChat — free account Open PDF

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1038/s41587-022-01272-8

Cites

99 of 107 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · cited 1×
“independent samples of the random variables wsc and apply a two-sample Kolmogorov– Smirnov test, and we correct for multiple testing using the Benjamini-Hochberg procedure [90].”
Method
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · cited 1×
“The counts were normalized using scanpy 58 with a target count of 10,000 UMIs, and the sparse PCA model was fit using sklearn 59 , using four components and a Lagrange multiplier of 5 for the ∥ .”
Method
Fiji: an open-source platform for biological-image analysis
Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., et al. (2012) · Nature Methods · doi:10.1038/nmeth.2019 · Cited by 66,890 · cited 1×
“Fiji [96] will be used for image registration.”
Method
SciPy 1.0: fundamental algorithms for scientific computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0686-2 · Cited by 38,430 · cited 1×
“The result is a curve that may be interpolated using splines using the scipy.interpolate function from SciPy 62 .”
Method
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453
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 · cited 3×
“For algorithms based on embeddings and nearest neighbors imputation (scVI, Harmony and Scanorama), we impute by calculating the average gene expression of the k-nearest neighbors of the embedding of the spot s restricted to the single-cell data of cell type c only.”
Method
SciPy 1.0: fundamental algorithms for scientific computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2019) · Apollo - University of Cambridge Repository · doi:10.17863/cam.64134 · Cited by 10,460
UMAP: Uniform Manifold Approximation and Projection
McInnes, L., Healy, J., Saul, N., & Großberger, L. (2018) · Journal of Open Source Software · doi:10.21105/joss.00861 · Cited by 9,826 · cited 2×
“We present this information in a two-dimensional plot of the annotated latent space (Figure 3C, Supplementary Figure 8), as embedded by scVI [29], and laid out by UMAP [41].”
Method
QuPath: Open source software for digital pathology image analysis
Bankhead, P., Loughrey, M. B., Fernández, J. A., Dombrowski, Y., McArt, D. G., Dunne, P. D., McQuaid, S., Gray, R. T., Murray, L. J., Coleman, H. G., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-17204-5 · Cited by 8,908 · cited 1×
“Then create a new projection in QuPath [95].”
Background

Show all 98 references

Research works printed but not linked: 4

Tomczak, J. M. & Welling, M. VAE with a VampPrior. Int. Conf. on Artificial Int. and Stat. (2018).
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014).
Ioffe, S. & Szegedy, C. Batch normalization: accelerating deep network training by reducing internal covariate shift. International Conference on Machine Learning (2015).
Ba, J. L., Kiros, J. R. & Hinton, G. E. Layer normalization. Preprint at https://arxiv.org/abs/1607.06450 (2016). · arXiv

Sorted by citations

Items 101–200 of 279

Mapping Cell Atlases at the Single‐Cell Level
Ye, F., Wang, J., Li, J., Mei, Y., & Guo, G. (2023) · Advanced Science · doi:10.1002/advs.202305449 · Cited by 13
Spatial transcriptomics in glioblastoma: is knowing the right zip code the key to the next therapeutic breakthrough?
Shireman, J. M., Cheng, L., Goel, A., Garcia, D. P. M., Partha, S., Quiñones‐Hinojosa, A., Kendziorski, C. M., & Dey, M. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1266397 · Cited by 13
Tissue and cellular spatiotemporal dynamics in colon aging
Daly, A. C., Cambuli, F., Äijö, T., Lötstedt, B., Marjanovic, N. D., Fernandez, S., Kuksenko, O., Smith-Erb, M., Domovic, D., Wittenberghe, N. V., et al. (2025) · Nature Biotechnology · doi:10.1038/s41587-025-02830-6 · Cited by 11
Genome analysis through image processing with deep learning models
Zhang, Y., & Imoto, S. (2024) · Journal of Human Genetics · doi:10.1038/s10038-024-01275-0 · Cited by 10
SDePER: a hybrid machine learning and regression method for cell-type deconvolution of spatial barcoding-based transcriptomic data
Liu, Y., Li, N., Qi, J., Xu, G., Zhao, J., Wang, N., Huang, X., Jiang, W., Wei, H. Y., Justet, A., et al. (2024) · Genome biology · doi:10.1186/s13059-024-03416-2 · Cited by 13
“Giotto, version 1.1.2) [ 26], SONAR (version 1.0.0) [ 28], SPOTlight (version 1.8.0) [25], cell2location (version 0.1.3) [ 15], DestVI (implemented in the python package scVI, version 1.1.3) [ 14], and CARD (version 1.0) [24].”
MethodInfluential
Emerging Roles of Spatial Transcriptomics in Liver Research
Fujiwara, N., Kimura, G., & Nakagawa, H. (2024) · Seminars in Liver Disease · doi:10.1055/a-2299-7880 · Cited by 12
Spatial Transcriptomic Cell-type Deconvolution Using Graph Neural NetworksPreprint
Li, Y., & Luo, Y. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.10.532112 · Cited by 9
SpatialDDLS: an R package to deconvolute spatial transcriptomics data using neural networks
Mañanes, D., Rivero-García, I., Relaño‐Rupérez, C., Torres, M., Sancho, D., Jiménez‐Carretero, D., Torroja, C., & Sánchez‐Cabo, F. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae072 · Cited by 10
“S3a) (Lopez et al. 2022).”
NLSDeconv: an efficient cell-type deconvolution method for spatial transcriptomics data
Chen, Y., Ruan, F., & Wang, J.-P. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae747 · Cited by 9
From pixels to cell types: a comprehensive review of computational methods for spatial transcriptomics deconvolution
Saqib, J., & Kim, J. (2025) · Genomics & Informatics · doi:10.1186/s44342-025-00055-2 · Cited by 12
“The first group probabilistic methods includes tools like Adroit [15], STRIDE [16], SpatialDecon [17], cell-2location [18], DestVI [19], RCTD [20], CARD [21], Berglund [22], STdeconvolve [23], and stereoscope [24].”
The industrial genomic revolution: A new era in neuroimmunology
Shalita, R., & Amit, I. (2022) · Neuron · doi:10.1016/j.neuron.2022.09.030 · Cited by 9
Self-citation
Dual decoding of cell types and gene expression in spatial transcriptomics with PANDA
Wang, M.-G., Chen, L., & Zhang, X.-F. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae876 · Cited by 8
Spatial resolved transcriptomics: Computational insights into gene transcription across tissue and organ architecture in diverse applications
Miao, Z., Tian, T., Chen, W., Wang, Q., Ma, L., Zhang, D., Xie, M., Yu, Z., Guo, X., Bai, G., et al. (2024) · The Innovation Life · doi:10.59717/j.xinn-life.2024.100097 · Cited by 9
Smoother: a unified and modular framework for incorporating structural dependency in spatial omics data
Su, J., Reynier, J.-B., Fu, X., Zhong, G., Jiang, J., Escalante, R. S., Wang, Y., Aparicio, L., Izar, B., Knowles, D. A., et al. (2023) · Genome biology · doi:10.1186/s13059-023-03138-x · Cited by 5
“Despite the many 107 deconvolution methods(26-31) developed for spatial transcriptomics (ST) data, few recognize 108 spatial dependency explicitly.”
Background
Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS
Marghi, Y. M., Gala, R., Baftizadeh, F., & Sümbül, U. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00683-8 · Cited by 7
“MMIDAS is an unsupervised method and does not require any priors on the relative abundances of cell types, labelled data, or batch information [20, 38, 19, 55].”
Deep topic modeling of spatial transcriptomics in the rheumatoid arthritis synovium identifies distinct classes of ectopic lymphoid structuresPreprint
Periyakoil, P. K., Smith, M. H., Kshirsagar, M., Ramirez, D. C., DiCarlo, E. F., Goodman, S. M., Rudensky, A. Y., Donlin, L. T., & Leslie, C. S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.08.631928 · Cited by 8
Computational Strategies and Algorithms for Inferring Cellular Composition of Spatial Transcriptomics Data
Liu, X., & Ren, X. (2024) · Genomics Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzae057 · Cited by 7
“DestVI applies a Bayesian model to simultaneously recover cell type proportions and cell type-specific snapshots of the transcriptional state based on scRNA-seq and ST data [44].”
KanCell: dissecting cellular heterogeneity in biological tissues through integrated single-cell and spatial transcriptomics
Wang, Z., Dai, R., Wang, M., Lei, L., Zhang, Z., Han, K., Wang, Z., & Guo, Q. (2024) · Journal of genetics and genomics/Journal of Genetics and Genomics · doi:10.1016/j.jgg.2024.11.009 · Cited by 8
Spatial genomics: Mapping the landscape of fibrosis
Velu, P. P., Abhari, R. E., & Henderson, N. C. (2025) · Science Translational Medicine · doi:10.1126/scitranslmed.adm6783 · Cited by 8
Improving cell-type composition inference in spatial transcriptomics with SpaDAMA
Huang, L., Liu, X., Zhu, F., & Min, W. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013354 · Cited by 7
“Furthermore, we incorporated four real ST datasets spanning different biological systems: the human developing heart (HDH) [28], mouse lymph node (MLN) [19], zebrafish embryo (ZE) [34], and human pancreatic ductal adenocarcinoma (PDAC) [37] (Table 1).”
Influential
Decoding congenital heart disease: a multi-omic framework for cardiac lineage and regulatory dysfunction
Lv, H., Sun, F., & Chen, Y. (2025) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2025.1659884 · Cited by 8
MOH: A Novel Multilayer Multi-Omics Heterogeneous Graph for Single-Cell Clustering
Dong, Y., Chen, C., Shi, J., Hu, Y., Cao, X., Dong, Y., & Zhang, X. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3589464 · Cited by 7
"Fighting for our homes": An archaeology of women's domestic labor and social change in a working-class, coal mining community, 1900--1930
Wood, M. C. (2002) · Syracuse University Libraries (Syracuse University) · Cited by 7
Nonnegative spatial factorizationPreprint
Townes, F. W., & Engelhardt, B. E. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.06122 · Cited by 11
Starfysh reveals heterogeneous spatial dynamics in the breast tumor microenvironmentPreprint
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. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.21.517420 · Cited by 7
“Each spot is independently assigned with the number of cells (5-15) and the number of cell types (1-5) uniformly, and the final cell-type assignment is determined by a uniform Dirichlet distribution.”
Background
CellsFromSpace: a fast, accurate, and reference-free tool to deconvolve and annotate spatially distributed omics data
Thuilliez, C., Moquin‐Beaudry, G., Khneisser, P., Costa, M. E. M. D., Karkar, S., Boudhouche, H., Drubay, D., Audinot, B., Geoerger, B., Scoazec, J., et al. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae081 · Cited by 9
“…most often rely on single-cell RNA sequencing references (Andersson et al. 2020, Biancalani et al. 2021, Dong and Yuan 2021, Elosua-Bayes et al. 2021, Song and Su 2021, Cable et al. 2022, Danaher et al. 2022, Gayoso et al. 2022, Kleshchevnikov et al. 2022, Lopez et al. 2022, Li et al. 2023).”
Background
Benchmarking LLM-based agents for single-cell omics analysis
Liu, Y., Zhou, L., Du, X., He, R., Zhang, X., Shen, R., & Li, Y. (2026) · Genome biology · doi:10.1186/s13059-026-03998-z · Cited by 12
An opposing molecular gradient axis underlies primate cortical organization
Huang, Z. R., Yang, Q., Li, S., Zhu, X., Wang, H., Lin, J., Zhan, Y., Wu, Y. H., Wang, Z.-H., Majka, P., et al. (2026) · Science · doi:10.1126/science.aea2673 · Cited by 8
stVAE deconvolves cell-type composition in large-scale cellular resolution spatial transcriptomics
Li, C., Chan, T., Yang, C., & Lin, Z. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad642 · Cited by 7
Recovering single-cell expression profiles from spatial transcriptomics with scResolve
Chen, H., Lee, Y. J., Ovando-Ricárdez, J. A., Rosas, L., Rojas, M., Mora, A. L., Bar‐Joseph, Z., & Lugo-Martinez, J. (2024) · Cell Reports Methods · doi:10.1016/j.crmeth.2024.100864 · Cited by 9
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. (2025) · Communications Biology · doi:10.1038/s42003-025-07872-9 · Cited by 7
Spider: a flexible and unified framework for simulating spatial transcriptomics data
Yang, J., Wei, N., Qu, Y., Hu, C., Zhang, W., Liu, L., Wu, H., & Zheng, X. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf562 · Cited by 6
“…(for 10x Visium and Slide-seq) (Andersson et al. 2020, Elosua-Bayes et al. 2021, Song and Su 2021, Cable et al. 2022, Kleshchevnikov et al. 2022, Lopez et al. 2022, Miller et al. 2022, Sun et al. 2022, Hao et al. 2024), predicting the spatial distribution of RNA transcripts (Lopez et al. 2019,…”
A Unified Probabilistic Framework for Modeling and Inferring Spatial Transcriptomic Data
Huang, Z., Luo, S., Zhang, Z., Wang, Z., Zhou, T., & Zhang, J. (2023) · Current Bioinformatics · doi:10.2174/1574893618666230529145130 · Cited by 6
A lymphatic-stem cell interactome regulates intestinal stem cell activityPreprint
Niec, R. E., Chu, T., Gur‐Cohen, S., Schernthanner, M., Hidalgo, L., Pasolli, H. A., Kataru, R. P., Mehrara, B. J., Pe’er, D., & Fuchs, E. V. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.01.29.478341 · Cited by 6
Coordinated Immune Cell Networks in the Bone Marrow Microenvironment Define the Graft versus Leukemia Response with Adoptive Cellular TherapyPreprint
Maurer, K., Park, C. Y., Mani, S., Borji, M., Penter, L., Jin, Y., Zhang, J. Y., Shin, C. S., Brenner, J. R., Southard, J., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.09.579677 · Cited by 6
Deep learning based deconvolution methods: A systematic review
Redondo, A. L., Velázquez, J. M. S., García-Tejedor, Á. J., & Lobo, V. J. S.-A. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.05.038 · Cited by 5
Spatial transcriptomics deconvolution methods generalize well to spatial chromatin accessibility data
Ouologuem, S., Martens, L. D., Schaar, A. C., Shulman, M., Gagneur, J., & Theis, F. J. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf268 · Cited by 5
“In this work, we benchmark five well-performing spatial transcriptomics deconvolution methods (Cell2location (Kleshchevnikov et al. 2022), RCTD (Cable et al. 2022), Tangram (Biancalani et al. 2021), SpatialDWLS (Dong and Yuan 2021), and DestVI (Lopez et al. 2022)) and assess whether they can be readily applied to emer…”
Influential
DeCoST: unveiling cell type heterogeneity in spatial transcriptomics based on inter-domain alignment and Gaussian kernel conditional autoregressive
Guo, X., Li, Z., Huang, Z., Li, J., Zhao, C., & Yu, L. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf490 · Cited by 5
Expansion omics: from expansion microscopy to spatial omics
Dong, Z., Xiang, W., Jiang, W., & Guo, T. (2025) · Molecular Systems Biology · doi:10.1038/s44320-025-00171-9 · Cited by 6
“…2021), using seeded non-negative matrix factorization, and Deconvolution of Spatial Tran-scriptomics pro ﬁ les using Variational Inference (DestVI) (Lopez et al, 2022), applying variational inference with autoencoder architectures, may help mitigate contamination and provide cleaner estimates of…”
Robust integration and annotation of single-cell and spatial omics data using interpretable gene programs
Zhang, Y., Ming, W., Yu, B., Wang, L., Lu, K., Xu, L., Ni, Y., Deng, R., & Chen, D. (2025) · Cell Genomics · doi:10.1016/j.xgen.2025.101105 · Cited by 6
Flexible analysis of spatial transcriptomics data (FAST): a deconvolution approach
Zhang, M., Liu, Y., Parker, J., An, L., & Sun, X. (2025) · BMC Bioinformatics · doi:10.1186/s12859-025-06054-y · Cited by 7
Enhancing and accelerating cell type deconvolution of large-scale spatial transcriptomics slices with dual network model
Zha, Y., Feng, S., Gao, P., Zou, Q., & Ma, X. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf419 · Cited by 6
“DestVI (Lopez et al. 2022) utilizes the continuous variation of transcriptome within cells of the same type, which facilitates optimization of mixture of cell types.”
How to make spatial maps of gene activity — down to the cellular level
Eisenstein, M. S. (2022) · Nature · doi:10.1038/d41586-022-01743-7 · Cited by 5
Multi-task benchmarking of spatially resolved gene expression simulation models
Liang, X., Torkel, M., Cao, Y., & Yang, J. Y. H. (2025) · Genome biology · doi:10.1186/s13059-025-03505-w · Cited by 4
stGNN: Spatially Informed Cell-Type Deconvolution Based on Deep Graph Learning and Statistical Modeling
Zhu, J., Wang, D., Chen, S., Meng, L., Long, Y., & Liang, C. (2025) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-025-00728-0 · Cited by 5
Spotless: a reproducible pipeline for benchmarking cell type deconvolution in spatial transcriptomicsPreprint
Sang-aram, C., Browaeys, R., Seurinck, R., & Saeys, Y. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.22.533802 · Cited by 5
“The tools include eight spatial deconvolution methods (cell2location [13], DestVI [14], DSTG [15], RCTD [16], SpatialDWLS [17], SPOTlight [18], stereoscope [19], and STRIDE [20]), one bulk deconvolution method (MuSiC [21]), and two mapping methods (Seurat [22] and Tangram [23]).”
Method
GraphCellNet: A deep learning method for integrated single-cell and spatial transcriptomic analysis with applications in development and disease
Dai, R., Wang, Z., Zhang, Z., Lei, L., Wang, M., Han, K., Wang, Z., Li, Z., Zhang, J., & Guo, Q. (2025) · Journal of Molecular Medicine · doi:10.1007/s00109-025-02575-4 · Cited by 4
“…the performance of Graph-CellNet, we conducted a series of systematic experiments on multiple datasets and compared it with state-of-the-art methods, including Tangram [36], Stereoscope [37], Spa-tialDWLS [38], STRIDE [23], RCTD [39], Seurat [40], SPOTlight [41], DestVI [42], and DSTG [43].”
STAN, a computational framework for inferring spatially informed transcription factor activity
Zhang, L., Sagan, A., Qin, B., Wang, H., Kim, E. S., Hu, B., & Osmanbeyoglu, H. U. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf1473 · Cited by 4
“…as a proxy for TF activity, STAN estimates spatially informed TF activity, which is further refined by incorporating cell-type deconvolution approaches (e.g. Cell2location [ 16 ], DestVI [ 17 ], Tangram [ 18 ], Stereoscope [ 19 ], RCTD [ 20 ], and others) to identify cell-type-specific TFs.”
Spotless: a reproducible pipeline for benchmarking cell type deconvolution in spatial transcriptomicsPreprint
Sang-aram, C., Browaeys, R., Seurinck, R., & Saeys, Y. (2023) · eLife · doi:10.7554/elife.88431.1 · Cited by 4
SIMVI reveals intrinsic and spatial-induced states in spatial omics dataPreprint
Dong, M., Su, D. G., Kluger, H. M., Fan, R., & Kluger, Y. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.28.554970 · Cited by 4
“To address these limitations, advanced deconvolution methods such as Tangram [51], CARD [52], and DestVI [53] could help reveal the single-cell profile within each pixel by using scRNA-seq references.”
stMMR: accurate and robust spatial domain identification from spatially resolved transcriptomics with multi-modal feature representationPreprint
Zhang, D., Yu, N., Li, W., Sun, X. G., Zou, Q., Li, X., Liu, Z., Yuan, Z., Zhang, W., & Gao, R. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.22.581503 · Cited by 4
Generative modeling of spatially resolved perturbation responses with CONCERTPreprint
Lin, X., Kong, Z., Fu, B., Ghosh, S., Kellis, M., & Žitnik, M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.08.686890 · Cited by 6
“In parallel, spatial transcriptomics methods address deconvolution and spatial patterning [25–33] yet do not analyze responses to perturbations.”
Robust Spatial Cell‐Type Deconvolution with Qualitative Reference for Spatial Transcriptomics
Dong, Q., Yang, Y., Luo, Z., Shen, H., Shi, X., & Liu, J. (2025) · Small Methods · doi:10.1002/smtd.202401145 · Cited by 4
Mouse-Specific Single cell cytokine activity prediction and Estimation (MouSSE)
Javaid, A., & Frost, H. R. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013475 · Cited by 3
“…datasets included: 1) Liao et al. [32] scRNA-seq dataset characterizing bronchoalveolar lavage fluid (BALF) immune cells from healthy individuals and patients with moderate and severe COVID19 and 2) Lopez et al. [34] ST dataset characterizing mouse lymph nodes treated with mycobacterium or PBS.”
From bulk to single-cell and spatial data: An AI framework to characterise breast cancer metabolic dysregulations across modalities
Doan, L. M. T., Verma, S., Eftekhari, N., Angione, C., & Occhipinti, A. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.111195 · Cited by 4
Reconstructing single-cell resolution from spatial transcriptomics with CellRefiner
Bourgain-Chang, E., Kuang, X., Cang, Z., & Nie, Q. H. (2026) · Nature Communications · doi:10.1038/s41467-026-70090-2 · Cited by 3
Multimodal spatial omics: From data acquisition to computational integration
Işık, E. B., Usta, Y. H., Riazi, M., Liu, H., Roach, W., Zhou, H., Nicolaou, A., Rattray, M., & Georgaka, S. (2026) · Patterns · doi:10.1016/j.patter.2026.101592 · Cited by 5
“98 Hybrid approaches combine probabilistic inference with deep learning for spatial prediction tasks.”
Interpretable spatial multi-omics data integration and dimensionality reduction with SpaMV
Liu, Y., Ma, K., Xu, H., Xu, K., Hu, Y., Lin, Z., Lin, J.-L., Han, B., Li, S.-C., Lin, Z.-X., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-74718-1 · Cited by 4
Spatially Aware Domain Adaptation Enables Cell Type Deconvolution from Multi‐Modal Spatially Resolved Transcriptomics
Wang, L., Bai, X., Zhang, C., Shi, Q., & Chen, L. (2024) · Small Methods · doi:10.1002/smtd.202401163 · Cited by 3
Masked adversarial neural network for cell type deconvolution in spatial transcriptomics
Huang, L., Liu, X., Wang, S., et al. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10821782 · Cited by 3
“• DestVI [17]: DestVI utilizes variational inference and latent variable models to estimate the proportions of different cell types.”
BackgroundMethodInfluential
DSCT: a novel deep-learning framework for rapid and accurate spatial transcriptomic cell typing
Xu, Y., Yu, B., Chen, X., Peng, A., Tao, Q., He, Y., Wang, Y., & Li, X. (2025) · National Science Review · doi:10.1093/nsr/nwaf030 · Cited by 3
Semibulk RNA-seq analysis as a convenient method for measuring gene expression statuses in a local cellular environment
Muto, K., Tsuchiya, I., Kim, S. H., Nagasawa, S., Takishita, M. N., Tsugawa, K., Saito, H., Komazaki, Y., Torii, T., Fujii, T., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-19391-2 · Cited by 3
Decoding spatial transcriptomics: Current trends and future prospects in data analysis methodologies
Li, R., Chen, X., & Yang, X. (2024) · Chinese Science Bulletin (Chinese Version) · doi:10.1360/tb-2024-0332 · Cited by 3
Unsupervised pattern identification in spatial gene expression atlas reveals mouse brain regions beyond established ontology
Cahill, R., Wang, Y., Xian, R. P., Lee, A. J., Zeng, H., Yu, B., Tasic, B., & Abbasi-Asl, R. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2319804121 · Cited by 4
An Overview of Spatial Transcriptomics Methodologies in Traversing the Biological System
John, A., Nallusamy, S., Senthil, N., Nathan, B., Venugopal, A., & Chinnaswamy, H. B. (2025) · Current Bioinformatics · doi:10.2174/0115748936352261241224053340 · Cited by 3
Spatial transcriptomics deconvolution at single-cell resolution by RedeconvePreprint
Zhou, Z., Zhong, Y., Zhang, Z., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.22.521551 · Cited by 3
A hybrid machine learning and regression method for cell type deconvolution of spatial barcoding-based transcriptomic dataPreprint
Liu, Y., Li, N., Qi, J., Xu, G., Zhao, J., Wang, N., Huang, X., Jiang, W., Justet, A., Adams, T. S., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.24.554722 · Cited by 3
Spotless: a reproducible pipeline for benchmarking cell type deconvolution in spatial transcriptomicsPreprint
Sang-aram, C., Browaeys, R., Seurinck, R., & Saeys, Y. (2024) · eLife · doi:10.7554/elife.88431.2 · Cited by 3
Spatially defined multicellular functional units in colorectal cancer revealed from single cell and spatial transcriptomicsPreprint
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:10.7554/elife.104815 · Cited by 5
MNiST: A deep learning framework for multi-scale spatial feature modeling and cellular landscape decoding in spatial
Wang, Z., Dai, R., Han, K., Wang, M., Lei, L., Zhang, Z., Li, Z., Liu, X., Zhang, J., Yan, H., et al. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114233 · Cited by 2
stSCI: A multi-task learning framework for integrative analysis of single-cell and spatial transcriptomics data
Shu, H., Jing, C., Hu, J., Zhang, R., Wang, Y., Peng, J., Xu, D., Shang, X., Yuan, Z., & Wang, T. (2025) · The Innovation · doi:10.1016/j.xinn.2025.101220 · Cited by 2
Unlocking single-cell level and continuous whole-slide insights in spatial transcriptomics with PanoSpace
He, H., Peng, P., Shi-Tong, Y., Wang, M.-G., Zhang, X.-F., & Chen, L. (2026) · Nature Computational Science · doi:10.1038/s43588-025-00938-y · Cited by 3
SpatioFreq: A Deep Learning Framework for Decoding Cellular and Tissue Landscapes Across Organisms Using Spatial Transcriptomics
Wang, Z., Dai, R., Wang, M., Zhang, Z., Lei, L., Li, Z., Han, K., Wang, Z., & Guo, Q. (2026) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-025-00811-6 · Cited by 2
Data enhancement in the age of spatial biology
Liao, L., Martin, P. C. N., Kim, H., Panahandeh, S., & Won, K. (2024) · Advances in cancer research · doi:10.1016/bs.acr.2024.06.008 · Cited by 2
Implementing distinct spatial proteogenomic technologies: opportunities, challenges, and key considerations
Verstappe, B., & Scott, C. L. (2024) · Clinical & Experimental Immunology · doi:10.1093/cei/uxae077 · Cited by 3
“…the relative cell-composition in each spot, including Seurat label transfer [90], NMFreg [91], Stereoscope [92], SPOTlight [93], DSTG [94], SpatialDWLS [95], CARD [96], RCTD [97], Tangram [98], Cell2location [99], SpatialDecon [100], DestVI [101], CellDART [102], STRIDE [103], and Redeconve [104].”
Background
Deconvolution of spatial transcriptomics data via graph contrastive learning and partial least square regression
Mo, Y., Liu, J., & Zhang, L. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf052 · Cited by 2
Deciphering Cell Type Abundance in Proteomics Data Through Graph Neural Networks
Dai, Z., Song, Y., Qi, T., Zhang, H., Zhao, H., Wang, Z., Yang, Y., & Zeng, Y. (2025) · Advanced Science · doi:10.1002/advs.202502987 · Cited by 2
stTransfer enables transfer of single-cell annotations to spatial transcriptomics with single-cell resolution
Zhou, T., Lin, X., Liao, K., He, Y., Zhuang, Z., & Liu, S. (2025) · Cell Reports Methods · doi:10.1016/j.crmeth.2025.101205 · Cited by 3
Bioinformatics approach to spatially resolved transcriptomics
Lukić, I. K. (2021) · Emerging Topics in Life Sciences · doi:10.1042/etls20210131 · Cited by 3
Unsupervised pattern discovery in spatial gene expression atlas reveals mouse brain regions beyond established ontologyPreprint
Cahill, R., Wang, Y., Xian, R. P., Lee, A. J., Zeng, H., Yu, B., Tasic, B., & Abbasi-Asl, R. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.10.531984 · Cited by 2
Single-cell Spatial Transcriptomics Reveals Disease-specific Microenvironmental Niches in Neurodegeneration and COVID-19Preprint
Graf, S., Ludwig, N., Flotho, M., Engel, A., Diks, I. F., Zimmer, P., Wemheuer, W. M., Wrede, A., Mîrzac, D., González‐Escamilla, G., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.15.643484 · Cited by 2
Benchmarking cell type deconvolution in spatial transcriptomics and application to cancer immunotherapyPreprint
Sun, A. Y., Azad, T., Brown, C. C., & Pritykin, Y. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.13.699379 · Cited by 3
ChatSpatial: Schema-Enforced Agentic Orchestration for Reproducible and Cross-Platform Spatial TranscriptomicsPreprint
Yang, C., Zhang, X., & Chen, J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.26.708361 · Cited by 5
A Tissue Virus Microenvironment with Activated Stress Responses Underlies Durable SIV PersistencePreprint
Hope, T. J., Crentsil, E. U., Arif, M. S., Thomas, Y., Zhang, E., Thuruthiyil, C. T., Moriarty, R. V., Engelmann, F., Pascoe, S., Hasson, J. M., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.02.716177 · Cited by 2
SURF: A Self‐Supervised Deep Learning Method for Reference‐Free Deconvolution in Spatial Transcriptomics
Liang, S., Zhou, Z., Huang, P., Fu, J., Jiao, J., Huang, Y., Zhou, S., Wang, G., Wang, Y., & Guo, Y. (2025) · Advanced Science · doi:10.1002/advs.202505456 · Cited by 1
Whole-brain reconstruction of fiber tracts based on cytoarchitectonic organization
Zhang, Y., Song, T., Yang, C., Shen, Y. X., Yang, Y., Hu, X., Zhao, R., Wang, W., Wang, X., Liu, H., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02865-2 · Cited by 2
“Neuron subclusters mapped by DestVI exhibited distinct layer distributions.”
Influential
AI-driven deciphering of cell niches with single-cell and spatial omics: a new perspective for traditional Chinese medicine research
Qian, J., Bao, H., Yang, H., Zhang, J., Shao, X., & Fan, X. (2026) · Targetome · doi:10.48130/targetome-0026-0009 · Cited by 1
scKAN: Integration of multi-modal single-cell data via adaptive Kolmogorov–Arnold networks
Zheng, L., Zhang, Y., Tian, S., Ding, B., Wang, S., Wang, X., Gu, A., Yang, L., Wang, M., & Zhang, H. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113807 · Cited by 1
DADA-EV: domain-adaptive diffusion autoencoder for estimating tissue- and cell-type-specific origin in extracellular vesicle transcriptomes
Liao, S., Yang, H., Xiao, S., Li, Z., Zhu, Q., Li, L., 卢尚辉, Liang, Y., & Dong, M. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag281 · Cited by 1
PlantDeconv: A structure-guided probabilistic deconvolution framework for plant spatial transcriptomics
Guan, X., Zhi, D., Wang, L., & Chen, W. (2026) · Plant Science · doi:10.1016/j.plantsci.2026.113358 · Cited by 1
q-Diffusion leverages the full dimensionality of gene coexpression in single-cell transcriptomics
Marmarelis, M. G., Littman, R., Battaglin, F., Niedzwiecki, D., Venook, A. P., Ambite, J. L., Galstyan, A., Lenz, H., & Steeg, G. V. (2024) · Communications Biology · doi:10.1038/s42003-024-06104-w · Cited by 1
Deciphering the abundance of immune cells in glomerular endothelium of Alport syndrome kidneys using the deconvolution algorithm CONVdeconv
Sun, Y., Liu, J., Fang, F., & Huang, Y. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.07.013 · Cited by 1
STGAT: Graph attention networks for deconvolving spatial transcriptomics data
Li, W., Zhang, H., Wang, L., Wang, P., & Yu, K. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108431 · Cited by 1
A Meta-Review of Spatial Transcriptomics Analysis Software
Gillespie, J. L., Pietrzak, M., Song, M., & Chung, D. (2025) · Cells · doi:10.3390/cells14141060 · Cited by 1
A Comprehensive Review of Cell-Type Deconvolution in Spatial Transcriptomic Data
Wang, L., Hu, Y., & Gao, L. (2025) · Big Data Mining and Analytics · doi:10.26599/bdma.2025.9020056 · Cited by 1
Multi-view deep learning of highly multiplexed imaging data improves association of cell states with clinical outcomes
Ayub, S., Gorman, J. L., Chen, E., Jackson, H. W., Selega, A., & Campbell, K. R. (2026) · Bioinformatics Advances · doi:10.1093/bioadv/vbag010 · Cited by 1
“The resulting latent spaces from hmiVAE can be used for downstream analysis (Gayoso et al. 2021, Lopez et al. 2022, Xu et al. 2022).”
BayesTME: A unified statistical framework for spatial transcriptomicsPreprint
Zhang, H., Hunter, M. V., Chou, J., Quinn, J. F., Zhou, M., White, R. M., & Tansey, W. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.08.499377 · Cited by 1
STEP: Spatial Transcriptomics Embedding Procedure for Multi-scale Biological Heterogeneities Revelation in Multiple SamplesPreprint
Li, L., Li, Z., Yin, X., & Xu, X. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.15.589470 · Cited by 1
Spatially defined multicellular functional units in colorectal cancer revealed from single cell and spatial transcriptomicsPreprint
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:10.7554/elife.104815.1 · Cited by 2
Spatially defined multicellular functional units in colorectal cancer revealed from single cell and spatial transcriptomicsPreprint
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:10.7554/elife.104815.2 · Cited by 1

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: Lopez, Romain
Li, Baoguo
Boyeau, Pierre
Kedmi, Merav
Yofe, Ido
David, Eyal
Wagner, Allon
Ergen, Can
Addadi, Yoseph
Golani, Ofra
Ronchese, Franca
Jordan, Michael I.
Amit, Ido
Yosef, Nir
Venue: Nature Biotechnology
Publisher: Springer Science and Business Media LLC
Published: 2022-04-21
Volume: 40
Issue: 9
Pages: 1360-1369
ISSN: 0733-222X (ISSN-L)
1087-0156
1546-1696
DOI: 10.1038/s41587-022-01272-8
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: cc-by-nd
www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

DestVI identifies continuums of cell types in spatial transcriptomics data

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks