Multiview data, which represent distinct but related groupings of variables, can be useful for identifying relevant and robust clustering structures among observations. A large number of multiview classification algorithms have been proposed in the fields of computer science and genomics; here, we instead focus on the task of merging or splitting an existing hard or soft cluster partition based on multiview data. This article is specifically motivated by an application involving multiomic breast cancer data from The Cancer Genome Atlas, where multiple molecular profiles (gene expression, microRNA expression, methylation and copy number alterations) are used to further subdivide the five currently accepted intrinsic tumor subtypes into distinct subgroups of patients. In addition, we investigate the performance of the proposed multiview splitting and aggregation algorithms, as compared to single- and concatenated-view alternatives, in a set of simulations. The multiview splitting and aggregation algorithms developed here are implemented in the maskmeans R package.

中文翻译：

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多视图聚类聚合和拆分，应用于多组学乳腺癌数据

多视图数据代表了变量的不同但相关的分组，可用于识别观测值之间相关且稳健的聚类结构。在计算机科学和基因组学领域已经提出了大量的多视图分类算法。在这里，我们改为专注于基于多视图数据合并或拆分现有的硬集群分区或软集群分区的任务。本文特别受涉及来自The Cancer Genome Atlas的多组性乳腺癌数据的应用启发，在该应用程序中，使用了多种分子特征（基因表达，microRNA表达，甲基化和拷贝数变化）将当前接受的五种内在肿瘤亚型进一步细分为不同的类型患者亚组。此外，我们在一组仿真中研究了与单视图和串联视图替代方案相比，所提出的多视图拆分和聚合算法的性能。此处开发的多视图拆分和聚合算法是在maskmeans R软件包。