IBM Research Europe, Zurich, 8803 Rüschlikon, Switzerland.
Bioinformatics. 2022 May 26;38(11):3151-3153. doi: 10.1093/bioinformatics/btac303.
Tumor heterogeneity has emerged as a fundamental property of most human cancers, with broad implications for diagnosis and treatment. Recently, spatial omics have enabled spatial tumor profiling, however computational resources that exploit the measurements to quantify tumor heterogeneity in a spatially aware manner are largely missing. We present ATHENA (Analysis of Tumor HEterogeNeity from spAtial omics measurements), a computational framework that facilitates the visualization, processing and analysis of tumor heterogeneity from spatial omics measurements. ATHENA uses graph representations of tumors and bundles together a large collection of established and novel heterogeneity scores that quantify different aspects of the complexity of tumor ecosystems.
ATHENA is available as a Python package under an open-source license at: https://github.com/AI4SCR/ATHENA. Detailed documentation and step-by-step tutorials with example datasets are also available at: https://ai4scr.github.io/ATHENA/. The data presented in this article are publicly available on Figshare at https://figshare.com/articles/dataset/zurich_pkl/19617642/2.
Supplementary data are available at Bioinformatics online.
肿瘤异质性已成为大多数人类癌症的基本特征,这对诊断和治疗具有广泛的影响。最近,空间组学已经实现了空间肿瘤分析,然而,利用这些测量数据以空间感知的方式量化肿瘤异质性的计算资源在很大程度上仍然缺失。我们提出了 ATHENA(Analysis of Tumor HEterogeNeity from spAtial omics measurements),这是一个计算框架,可促进从空间组学测量中可视化、处理和分析肿瘤异质性。ATHENA 使用肿瘤的图表示形式,并将大量已建立和新颖的异质性评分捆绑在一起,这些评分可量化肿瘤生态系统复杂性的不同方面。
ATHENA 作为一个开源许可证下的 Python 包在以下网址提供:https://github.com/AI4SCR/ATHENA。详细的文档和带有示例数据集的逐步教程也可在以下网址获得:https://ai4scr.github.io/ATHENA/。本文中呈现的数据可在 Figshare 上公开获取,网址为 https://figshare.com/articles/dataset/zurich_pkl/19617642/2。
补充数据可在生物信息学在线获取。