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- W3165120573 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. BayesSpace increases the resolution of spatial transcriptomics by using neighborhood information." @default.
- W3165120573 created "2021-06-07" @default.
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- W3165120573 date "2021-06-03" @default.
- W3165120573 modified "2023-10-14" @default.
- W3165120573 title "Spatial transcriptomics at subspot resolution with BayesSpace" @default.
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- W3165120573 doi "https://doi.org/10.1038/s41587-021-00935-2" @default.
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