Spatial transcriptomics at subspot resolution with BayesSpace

By Edward Zhao, M. Stone, Xing Ren, Jamie Guenthoer, K. Smythe, T. Pulliam, Stephen R. Williams, C. Uytingco, S. Taylor, P. Nghiem and 2 more. Nature Biotechnology, 2021

857 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/912c676847379784ab311a1f01df9497cdd8de11

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Citations in the corpus

Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST (2023, positive): “With function (11), we can derive the annotation-to-spot projection matrix Pspot 2 Rspot ×Nannot cell .”
An introduction to spatial transcriptomics for biomedical research (2022, background): “Visium, to finer sub-spots with the use of spatial prior information from nearby spots [84].”
Spatially Informed Cell Type Deconvolution for Spatial Transcriptomics (2022, methodology): “78 and Supplementary Table 5), and it is 5,875–7,028 times faster and uses only 14–17% of the physical memory compared to BayesSpace in creating refined spatial maps (Supplementary Figs.”
Unsupervised spatially embedded deep representation of spatial transcriptomics (2021, methodology): “Next, current methods that integrate gene expression with associated spatial information were used to benchmark SEDR, namely SpatialLDA [33], Giotto [26], stLearn [28], SpaGene[34], SpaGCN [29], BayesSpace [27], UTAG [32], DeepST [30], and STAGATE [31] (Additional file 1: Supplementary methods).”
Spatial components of molecular tissue biology (2022, positive): “They can be broadly categorized into Gaussian process-based frameworks 164,165,168–170 , methods that use hidden Markov random fields 171–173 and graph-based methods 114,174 .”
SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network (2021, methodology): “This example demonstrates that SpaGCN utilizes spatial information more efficiently than BayesSpace and HMRF.”
Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder (2021, methodology): “…it as the ground truth, we compared the clustering accuracy of STAGATE with the non-spatial clustering method implemented by SCANPY 17 and three recently developed spatial clustering approaches (BayesSpace 10 , stLearn 11 , and SEDR 12 ) in terms of adjusted rand index (ARI) (Supplementary Notes) .”
DeepST: identifying spatial domains in spatial transcriptomics by deep learning (2022, methodology): “Up-to-date methods (BayesSpace and SpaGCN) are unable to integrate ST datasets from different batches simultaneously.”
Best practices for single-cell analysis across modalities (2023, methodology): “Our work represents an entry point”