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

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DestVI identifies continuums of cell types in spatial transcriptomics data

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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

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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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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

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Additional file 9 of Niche-DE: niche-differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions
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Additional file 4 of Niche-DE: niche-differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions
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Additional file 7 of Niche-DE: niche-differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions
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Additional file 6 of Niche-DE: niche-differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions
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Additional file 5 of Niche-DE: niche-differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions
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Additional file 8 of Niche-DE: niche-differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions
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Additional file 3 of Niche-DE: niche-differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions
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Additional file 10 of Niche-DE: niche-differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions
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Additional file 2 of Niche-DE: niche-differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions
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Additional file 11 of Niche-DE: niche-differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions
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Li, Z., Li, Y., Zhao, C., & Yu, L. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.135120
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Gao, P., Zhang, Y., Zou, Q., Zhang, Z., & 姚宇华 (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.115096
Artificial intelligence in spatial transcriptomics: a review of methods, datasets, and directions
Sharma, N., Şen, R., & Erdem, C. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag569

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

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DestVI identifies continuums of cell types in spatial transcriptomics data

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