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在空间转录学数据分析中的基准测试多切片集成和下游应用

Kejing Dong^1,2,3, Yicheng Gao^1,2,3, Qi Zou⁴

¹State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China.

|September 30, 2025

概括

一个新的基准评估了12个空间转录学多切片集成方法. 性能因数据和任务而异,强调需要在空间生物学中进行强有力的上游分析.

科学领域:

空间转录组学空间转录组学
计算生物学是一种计算生物学.
生物信息学是一种生物信息学.

背景情况:

空间转录学技术产生具有空间背景的基因表达数据.
越来越需要多切片整合方法来结合来自多个组织切片的数据.
现有方法的可靠性各不相同,并且面临着各种技术的挑战,需要一个基准.

研究的目的:

开发一个全面的基准来评估空间转录学中的多切片集成方法.
在关键任务的管道中评估方法性能:集成,聚类,对齐和表示.
为方法选择和应用提供可行的建议.

主要方法:

开发了一个基准测试框架,涵盖了四个上游到下游的任务.
评估了12种多切片集成方法.
19个不同的空间转录学数据集被用于系统评估.

主要成果:

在所有评估任务中,方法性能显示了大量数据依赖的变化.
发现下游任务的性能高度依赖上游分析的质量.
确定了上游和下游任务之间的相互依赖性,强调了早期阶段分析的重要性.

结论:

该基准提供了对19个数据集中的12种多切片集成方法的系统评估.
方法的性能取决于应用上下文,数据集大小和基础技术.
强大的上游分析对于多切片空间转录学集成的可靠下游结果至关重要.

关键词:

空间多切片集成空间集成空间转录组学空间转录组学系统的基准指标系统的基准指标

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