Multi-view Subspace Clustering Analysis for Aggregating Multiple Heterogeneous Omics Data - Shi, Qianqian; Hu, Bing; Zeng, Tao; Zhang, Chuanchao (Frontiers in Genetics, 2019)
Integration of distinct biological data types could provide a comprehensive view of biological processes or complex diseases. The combinations of molecules responsible for different phenotypes form multiple embedded (expression) subspaces, thus identifying the intrinsic data structure is challenging by regular integration methods. In this paper, we propose a novel framework of “Multi-view Subspace Clustering Analysis (MSCA)”, which could measure the local similarities of samples in the same subspace and obtain the global consensus sample pattern (structure) for multiple data types, thereby comprehensively capturing the underlying heterogeneity of samples. Applied to various synthetic datasets, MSCA performs effectively to recognize the predefined sample patterns, and is robust to data noises. Given a real biological dataset, i.e., CCLE data, MSCA successfully identifies cell clusters of common aberrations across cancer types. A remarkable superiority over the state-of-the-art methods, such as iClusterPlus, SNF and ANF, has also been demonstrated in our simulation and case studies. Availability: MSCA was implemented as a Matlab package and freely available at https://github.com/ZCCQQWork/MSCA.