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Spatial transcriptomics at subspot resolution with BayesSpace


Recent spatial gene expression technologies enable comprehensive measurement of transcriptomic profiles while retaining spatial context. However, existing analysis methods do not address the limited resolution of the technology or use the spatial information efficiently. Here, we introduce BayesSpace, a fully Bayesian statistical method that uses the information from spatial neighborhoods for resolution enhancement of spatial transcriptomic data and for clustering analysis. We benchmark BayesSpace against current methods for spatial and non-spatial clustering and show that it improves identification of distinct intra-tissue transcriptional profiles from samples of the brain, melanoma, invasive ductal carcinoma and ovarian adenocarcinoma. Using immunohistochemistry and an in silico dataset constructed from scRNA-seq data, we show that BayesSpace resolves tissue structure that is not detectable at the original resolution and identifies transcriptional heterogeneity inaccessible to histological analysis. Our results illustrate BayesSpace’s utility in facilitating the discovery of biological insights from spatial transcriptomic datasets.

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Fig. 1: The BayesSpace workflow.
Fig. 2: BayesSpace improves computational resolution of layers in the DLPFC.
Fig. 3: Enhanced-resolution clustering identifies tumor-proximal lymphoid tissue in a melanoma sample.
Fig. 4: Immunohistochemistry validates BayesSpace enhancement in an IDC sample and an OC sample.
Fig. 5: BayesSpace identifies transcriptional heterogeneity within an IDC.
Fig. 6: BayesSpace outperforms spatial and non-spatial clustering methods with simulated data.

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Data availability

Datasets analyzed in this paper are available in raw form from their original authors (see details in the Supplementary Note), and the SingleCellExperiment objects that we prepared for analysis with BayesSpace are available through the BayesSpace package. Raw count matrices, images and spatial data from the IDC sample are accessible on the 10x Genomics website at

Code availability

BayesSpace is available as a Bioconductor package at, and the source code is publicly available at


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This research was supported by funding from the National Institutes of Health (P01-CA225517, P30-CA015704 to R.G. and P.N.; T32-CA080416, F30-CA254168 to T.P.), the Immunotherapy and Data Science Integrated Research Centers at Fred Hutchinson to E.Z., M.R.S., X.R. and J.H.B. and the Scientific Computing Infrastructure at Fred Hutchinson funded by ORIP grant S10OD028685. We thank M. Lin and P.L. Porter for their pathological review of J.G.’s histological annotations, K.J. Cheung from the Fred Hutchinson Public Health Sciences and Human Biology Divisions for his suggestions in our analysis of the IDC sample, A. Moshiri from the UW Division of Dermatology for his review of T.P.’s histopathological annotations and Q. Nguyen and X. Tan at the University of Queensland for their assistance in applying stLearn.

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Authors and Affiliations



E.Z. and R.G. formulated the method and wrote the paper. M.R.S. and E.Z. developed software. E.Z., M.R.S. and X.R. analyzed data. J.G., K.S.S. and T.P. contributed to annotation and interpretation of cancer samples. C.R.U., S.R.W. and S.E.B.T. prepared and contributed to analysis of the IDC sample. P.N., J.H.B. and R.G. supervised the project.

Corresponding author

Correspondence to Raphael Gottardo.

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Competing interests

R.G. has received consulting income from Juno Therapeutics, Takeda, Infotech Soft, Celgene and Merck, has received research support from Janssen Pharmaceuticals and Juno Therapeutics and declares ownership in Ozette Technologies and stock ownership in 10x Genomics. S.R.W., C.R.U. and S.E.B.T. are employees of and hold shares in 10x Genomics. All other authors declare no conflicts of interest.

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Supplementary Note, Figs. 1–23 and Table 1

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Zhao, E., Stone, M.R., Ren, X. et al. Spatial transcriptomics at subspot resolution with BayesSpace. Nat Biotechnol 39, 1375–1384 (2021).

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