Gene

Identification (biology)

Data mining

Gene expression

Paleontology

Pattern recognition (psychology)

Artificial intelligence

Spatial analysis

Transcriptome

Benchmark (surveying)

Geography

Context (archaeology)

Computer science

Genetics

Cartography

Botany

Remote sensing

Spatial contextual awareness

Computational biology

Cluster analysis

Biology

Geospatial analysis

Nature Biotechnology

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.

Spatial transcriptomics at subspot resolution with BayesSpace