spatialGE：利用空间转录组学对肿瘤微环境异质性进行定量和可视化分析。
spatialGE: quantification and visualization of the tumor microenvironment heterogeneity using spatial transcriptomics.
作者信息
Ospina Oscar E, Wilson Christopher M, Soupir Alex C, Berglund Anders, Smalley Inna, Tsai Kenneth Y, Fridley Brooke L
机构信息
Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL 33612, USA.
Department of Tumor Biology, Moffitt Cancer Center, Tampa, FL 33612, USA.
出版信息
Bioinformatics. 2022 Apr 28;38(9):2645-2647. doi: 10.1093/bioinformatics/btac145.
SUMMARY
Spatially resolved transcriptomics promises to increase our understanding of the tumor microenvironment and improve cancer prognosis and therapies. Nonetheless, analytical methods to explore associations between the spatial heterogeneity of the tumor and clinical data are not available. Hence, we have developed spatialGE, a software that provides visualizations and quantification of the tumor microenvironment heterogeneity through gene expression surfaces, spatial heterogeneity statistics that can be compared against clinical information, spot-level cell deconvolution and spatially informed clustering, all using a new data object to store data and resulting analyses simultaneously.
AVAILABILITY AND IMPLEMENTATION
The R package and tutorial/vignette are available at https://github.com/FridleyLab/spatialGE. A script to reproduce the analyses in this manuscript is available in Supplementary information. The Thrane study data included in spatialGE was made available from the public available from the website https://www.spatialresearch.org/resources-published-datasets/doi-10-1158-0008-5472-can-18-0747/.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
摘要
空间分辨转录组学有望增进我们对肿瘤微环境的理解，并改善癌症预后和治疗方法。然而，目前尚无探索肿瘤空间异质性与临床数据之间关联的分析方法。因此，我们开发了spatialGE软件，它可通过基因表达表面对肿瘤微环境异质性进行可视化和量化，提供可与临床信息相比较的空间异质性统计数据、斑点水平的细胞反卷积和空间信息聚类，所有这些都使用一个新的数据对象来同时存储数据和分析结果。
可用性和实现方式
补充信息
补充数据可在《生物信息学》在线获取。
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