多个异构组学数据的集成

Chuanchao Zhang, Juan Liu, Qianqian Shi, Xiangtian Yu, T. Zeng, Luonan Chen
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引用次数: 5

摘要

整合不同的基因组图谱对以多视角理解复杂疾病具有挑战性。计算方法既要保留数据类型的有用信息,又要纠正偏差。因此,我们提出了一种新的框架模式融合分析(PFA),通过自适应地对齐每种生物数据中的信息,将局部样本模式融合到相对于基础数据的患者全局模式中。特别是,PFA可以调整不同的数据类型,并在不同的配置文件中实现更健壮的样本模式。为了验证PFA的有效性,我们在各种合成数据集上测试了PFA,发现PFA比最先进的综合方法(如moCluster, iClusterPlus和SNF)能够有效地捕获内在聚类结构。此外,在肾癌的案例研究中,PFA不仅识别了已知疾病相关基因、甲基化和mirna之间的多向特征模块,而且在癌症亚型识别方面也表现出色,能够得到有效的临床预后预测。总的来说,PFA不仅提供了更全面和系统级的样本模式的新见解,而且为选择更多信息类型的生物数据提供了一种新的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integration of multiple heterogeneous omics data
Integration of different genomic profiles is challenging to understand complex diseases in a multi-view manner. Computational method is needed to preserve useful information of data types as well as correct bias. Thus, we proposed a novel framework pattern fusion analysis (PFA), to fuse the local sample patterns into a global pattern of patients with respect to the underlying data, by adaptively aligning the information in each type of biological data. In particular, PFA can adjust the distinct data types and achieve more robust sample pattern within different profiles. To validate the effectiveness of PFA, we tested PFA on various synthetic datasets and found that PFA is able to effectively capture the intrinsic clustering structure than the state-of-the-art integrative methods, such as moCluster, iClusterPlus and SNF. Moreover, in a case study on kidney cancer, PFA not only identified the multi-way feature modules among the prior-known disease associated genes, methylations and miRNAs, but also outperformed in cancer subtypes identification and could get effective clinical prognosis prediction. Totally, PFA not only provides new insights on the more holistic & systems-level sample pattern, but also supplies a new way for selecting more informative types of biological data.
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