Open AccessSpatial transcriptomics at subspot resolution with BayesSpace
Author(s) -
Edward Zhao,
Matthew R. Stone,
Xing Ren,
Jamie Guenthoer,
Kimberly S. Smythe,
Thomas H. Pulliam,
Stephen R. Williams,
Cedric Uytingco,
Sarah E. Taylor,
Paul Nghiem,
Jason H. Bielas,
Raphaël Gottardo
Publication year - 2021
Publication title -
nature biotechnology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 15.358
H-Index - 445
eISSN - 1546-1696
pISSN - 1087-0156
DOI - 10.1038/s41587-021-00935-2
Subject(s) - spatial analysis , cluster analysis , transcriptome , spatial contextual awareness , context (archaeology) , benchmark (surveying) , computer science , identification (biology) , geospatial analysis , data mining , computational biology , pattern recognition (psychology) , artificial intelligence , biology , cartography , gene , geography , gene expression , genetics , remote sensing , paleontology , botany
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.