Spatial transcriptomics at subspot resolution with BayesSpace
Top Cited Papers
- 3 June 2021
- journal article
- research article
- Published by Springer Science and Business Media LLC in Nature Biotechnology
- Vol. 39 (11), 1375-1384
- https://doi.org/10.1038/s41587-021-00935-2
Abstract
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.Keywords
Funding Information
- U.S. Department of Health & Human Services | NIH | National Cancer Institute (T32-CA080416, P01-CA225517, P30-CA015704, P30-CA015704, P01-CA225517)
- U.S. Department of Health & Human Services | National Institutes of Health (F30-CA254168)
- U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (U19AI128914, U19AI128914)
- U.S. Department of Health & Human Services | NIH | NIH Office of the Director (S10OD028685)
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