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整合对持续的同源性聚类和scRNA-seq数据中的生物信号检测的影响

Jonah Daneshmand¹, Julia H Chariker^2,3, Akshitkumar Mistry⁴

¹Department of Graduate and Interdisciplinary Studies, University of Louisville, Louisville, Kentucky 40202, United States of America.

bioRxiv : the preprint server for biology

|August 6, 2025

概括

持久同源性 (PH) 有助于评估单细胞RNA测序 (scRNA-seq) 数据集成. 拓总结揭示了整合如何影响生物模式和噪声,帮助解释超出标准集群.

科学领域:

计算生物学计算生物学
拓数据分析拓数据分析
基因组学就是基因组学.

背景情况:

单细胞RNA测序 (scRNA-seq) 数据集成对于比较各种生物样本和实验条件至关重要.
需要强大的方法来处理越来越多的scRNA-seq数据集的数量和复杂性.
持久同质 (PH) 是拓数据分析 (TDA) 的一种技术,为分析高维数据中的结构模式提供了一个框架.

研究的目的:

研究数据整合对scRNA-seq数据的拓特征和生物解释性的影响.
评估数据集成是否增强或掩盖scRNA-seq数据集中的生物意义模式.
建立一个使用拓方法来评估scRNA-seq数据集成质量的框架.

主要方法:

将PH应用于8种组织类型的多种scRNA-seq数据集.
基于PH衍生的对距离进行聚类.
使用贝蒂曲线,欧勒特征和持久性景观评估全球拓结构.
在原始,规范化和集成数据集中比较拓总结.

主要成果:

PH作为评估数据整合效应的补充策略.
拓总结有助于在单细胞数据中区分生物信号与批量相关噪声.
该研究为使用拓方法评估集成质量提供了一个框架.

结论:

持久性同源性为评估单细胞RNA测序数据集成提供了一种新的方法.
拓总结为复杂的转录组景观提供了宝贵的见解,补充了传统的集群方法.
这一框架有助于更深入地了解数据集成如何影响生物信号检测.

关键词:

集群集成是指集群集成.整合整合整合整合整合规范化的正常化.持久的同质性持续的同质性这就是scRNA-seqq.拓学数据分析数据分析.

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