Multi-view Subspace Clustering Analysis for Aggregating Multiple Heterogeneous Omics Data
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-08-01
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| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fgene.2019.00744/full |
| _version_ | 1811225267886096384 |
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| author | Qianqian Shi Bing Hu Tao Zeng Tao Zeng Chuanchao Zhang |
| author_facet | Qianqian Shi Bing Hu Tao Zeng Tao Zeng Chuanchao Zhang |
| author_sort | Qianqian Shi |
| collection | DOAJ |
| description | 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 patterns (structures) 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., Cancer Cell Line Encyclopedia (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. |
| first_indexed | 2024-04-12T09:04:12Z |
| format | Article |
| id | doaj.art-d1f9109330e04eee9eb0dc7c7ecb73af |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-04-12T09:04:12Z |
| publishDate | 2019-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-d1f9109330e04eee9eb0dc7c7ecb73af2022-12-22T03:39:09ZengFrontiers Media S.A.Frontiers in Genetics1664-80212019-08-011010.3389/fgene.2019.00744444803Multi-view Subspace Clustering Analysis for Aggregating Multiple Heterogeneous Omics DataQianqian Shi0Bing Hu1Tao Zeng2Tao Zeng3Chuanchao Zhang4Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, ChinaDepartment of Applied Mathematics, College of Science, Zhejiang University of Technology, Hangzhou, ChinaKey Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Shanghai Institute of Biological Sciences, Chinese Academy of Sciences, Shanghai, ChinaShanghai Research Center for Brain Science and Brain-Inspired Intelligence, Shanghai, ChinaWuhan Institute of Huawei Technologies, Wuhan, ChinaIntegration 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 patterns (structures) 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., Cancer Cell Line Encyclopedia (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.https://www.frontiersin.org/article/10.3389/fgene.2019.00744/fullmulti-view subspace clustering analysisdata integrationheterogeneitylow-rank representationgraph diffusion |
| spellingShingle | Qianqian Shi Bing Hu Tao Zeng Tao Zeng Chuanchao Zhang Multi-view Subspace Clustering Analysis for Aggregating Multiple Heterogeneous Omics Data Frontiers in Genetics multi-view subspace clustering analysis data integration heterogeneity low-rank representation graph diffusion |
| title | Multi-view Subspace Clustering Analysis for Aggregating Multiple Heterogeneous Omics Data |
| title_full | Multi-view Subspace Clustering Analysis for Aggregating Multiple Heterogeneous Omics Data |
| title_fullStr | Multi-view Subspace Clustering Analysis for Aggregating Multiple Heterogeneous Omics Data |
| title_full_unstemmed | Multi-view Subspace Clustering Analysis for Aggregating Multiple Heterogeneous Omics Data |
| title_short | Multi-view Subspace Clustering Analysis for Aggregating Multiple Heterogeneous Omics Data |
| title_sort | multi view subspace clustering analysis for aggregating multiple heterogeneous omics data |
| topic | multi-view subspace clustering analysis data integration heterogeneity low-rank representation graph diffusion |
| url | https://www.frontiersin.org/article/10.3389/fgene.2019.00744/full |
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