聚类分析中的跨研究可重复性
Cross-Study Replicability in Cluster Analysis
AI总结：
本文回顾并提出了评估聚类分析跨研究可重复性的框架，适用于任何聚类算法，通过全局和局部相似性度量，在合成和真实基因表达数据上验证了其有效性。
摘要：
在癌症研究中，聚类技术被广泛用于探索性分析和降维，在识别新型癌症亚型方面发挥着关键作用，通常对患者管理有直接影响。随着多个研究小组收集的数据不断增长，研究聚类程序的可重复性（即跨多个数据集一致恢复具有生物学意义的聚类的能力）变得越来越可行。在本文中，我们回顾了评估聚类分析可重复性的现有方法，并讨论了一个用于评估跨研究聚类可重复性的框架，该框架在有两项或更多研究可用时非常有用。这些方法可应用于任何聚类算法，并可采用不同的分区相似性度量来量化可重复性，既可在全局（即针对整个样本）也可在局部（即针对单个聚类）进行量化。通过使用合成和真实基因表达数据的实验，我们展示了可重复性度量在评估跨数据集集合是否一致识别相同聚类方面的实用性。
英文摘要：
In cancer research, clustering techniques are widely used for exploratory analyses and dimensionality reduction, playing a critical role in the identification of novel cancer subtypes, often with direct implications for patient management. As data collected by multiple research groups grows, it is increasingly feasible to investigate the replicability of clustering procedures, that is, their ability to consistently recover biologically meaningful clusters across several datasets. In this paper, we review existing methods to assess replicability of clustering analyses, and discuss a framework for evaluating cross-study clustering replicability, useful when two or more studies are available. These approaches can be applied to any clustering algorithm and can employ different measures of similarity between partitions to quantify replicability, globally (i.e. for the whole sample) as well as locally (i.e. for individual clusters). Using experiments on synthetic and real gene expression data, we illustrate the utility of replicability metrics to evaluate if the same clusters are identified consistently across a collection of datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
