Multiple types of molecular data for the same set of clinical samples are increasingly available and may be analyzed jointly in an integrative analysis to maximize comprehensive biological insight. This analysis is important as separate analyses of individual omics data types usually do not fully explain disease phenotypes. An increasing number of studies have now been focusing on multi‐omics data integration, yet not many studies have included phosphoproteomics data, an important layer for understanding signaling pathways. Multi‐omics integration methods with phosphoproteomics data are reviewed in the context of cancer research as well as multi‐omics methods papers that would be promising to apply to phosphoproteomics data. Analysis of individual data types is still the major approach even in large cohort proteogenomics studies. Hence, a section is dedicated on possible integrative methods for multi‐omics and phosphoproteomics data. In summary, this review provides the readers with both currently used integrative methods previously applied to phosphoproteomics and multi‐omics data integration and other algorithms for multi‐omics data integration promising for future application to phosphoproteomics data.

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多组学癌症研究中磷酸蛋白质组学数据的计算分析

同一组临床样本的多种类型的分子数据越来越多，可以在综合分析中联合分析，以最大限度地提高综合生物学洞察力。这种分析很重要，因为对单个组学数据类型的单独分析通常不能完全解释疾病表型。越来越多的研究现在关注多组学数据整合，但包括磷酸蛋白质组学数据的研究并不多，磷酸蛋白质组学数据是理解信号通路的重要层。在癌症研究以及有望应用于磷酸蛋白质组学数据的多组学方法论文中，对具有磷酸蛋白质组学数据的多组学整合方法进行了审查。即使在大型队列蛋白质基因组学研究中，分析单个数据类型仍然是主要方法。因此，一节专门介绍多组学和磷酸蛋白质组学数据的可能综合方法。总之，这篇综述为读者提供了以前应用于磷酸蛋白质组学和多组学数据集成的当前使用的集成方法以及其他多组学数据集成算法，这些算法有望在未来应用于磷酸蛋白质组学数据。