对于多种类数据的关系持久同质学,适用于瘤微环境
Bernadette J Stolz1,2, Jagdeep Dhesi2, Joshua A Bull2
1Laboratory for Topology and Neuroscience, EPFL, Station 8, Lausanne, 1015, Switzerland.
Bulletin of mathematical biology
|September 17, 2024
概括
拓数据分析 (TDA) 方法现在量化了多种类的数据关系. 新的Dowker和Witness复杂方法从复杂的空间数据中揭示了生物学见解,克服了传统的持久性同质性限制.
科学领域:
- 数学 数学 是一个数学.
- 计算生物学 计算生物学
- 数据科学数据科学数据科学
背景情况:
- 拓数据分析 (TDA) 和持久同质性 (PH) 传统上分析单个实体的数据.
- 现代数据收集产生复杂的多种数据集,需要关系分析.
- 应用范围包括生物医学成像,地理空间分析和物种生态学.
研究的目的:
- 开发新的TDA方法来量化多种数据中的空间关系.
- 适应多克复合体和Witness复合体用于异质数据分析.
- 将方法应用于瘤微环境数据,以提取生物见解.
主要方法:
- 提出了两种用于编码不同数据类型之间的空间关系的新方法.
- 用道克复合体和见证复合体作为基础框架.
- 应用于合成多种瘤微环境数据的方法.
主要成果:
- 确定了捕捉跨物种细胞类型关系的拓特征 (例如瘤细胞,免疫细胞).
- 提取了重要的生物学见解,包括主导性免疫细胞表型.
- 已证明能够推断数据生成模型的参数模式.
结论:
- 开发了用于对多个物种空间数据的关系分析的定量方法.
- 克服了复杂数据集传统持久同质学的局限性.
- 方法易于计算,在TDA中提供了新的视角.
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