Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Department of Biostatistics, University of Washington, Seattle, WA, USA.
Nat Biotechnol. 2021 Nov;39(11):1375-1384. doi: 10.1038/s41587-021-00935-2. Epub 2021 Jun 3.
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.
最近的空间基因表达技术能够在保留空间背景的同时全面测量转录组谱。然而，现有的分析方法没有解决技术的有限分辨率问题，也没有有效地利用空间信息。在这里，我们介绍了 BayesSpace，这是一种完全贝叶斯统计方法，它利用空间邻域的信息来增强空间转录组数据的分辨率，并进行聚类分析。我们将 BayesSpace 与当前的空间和非空间聚类方法进行基准测试，结果表明它可以提高从大脑、黑色素瘤、浸润性导管癌和卵巢腺癌样本中识别不同组织内转录谱的能力。我们使用免疫组织化学和从 scRNA-seq 数据构建的一个计算机数据集，表明 BayesSpace 可以解决在原始分辨率下无法检测到的组织结构问题，并确定组织学分析无法获得的转录异质性。我们的结果说明了 BayesSpace 在促进从空间转录组数据集中发现生物学见解方面的实用性。
