Journal
NATURE BIOTECHNOLOGY
Volume 39, Issue 11, Pages 1375-+Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41587-021-00935-2
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Funding
- National Institutes of Health [P01-CA225517, P30-CA015704, T32-CA080416, F30-CA254168]
- Immunotherapy and Data Science Integrated Research Centers at Fred Hutchinson
- Scientific Computing Infrastructure at Fred Hutchinson - ORIP [S10OD028685]
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BayesSpace increases the resolution of spatial transcriptomics by utilizing neighborhood information. It outperforms current methods for spatial and non-spatial clustering, improving the identification of distinct intra-tissue transcriptional profiles. BayesSpace can resolve tissue structures undetectable at the original resolution and identify transcriptional heterogeneity inaccessible to histological analysis.
BayesSpace increases the resolution of spatial transcriptomics by using neighborhood information. 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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