ArticleFrontiers in genetics2019
Multi-view Subspace Clustering Analysis for Aggregating Multiple Heterogeneous Omics Data.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A self-training subspace clustering algorithm based on adaptive confidence for gene expression data.Frontiers in genetics · 2023Article
- Genetic heterogeneity: Challenges, impacts, and methods through an associative lens.Genetic epidemiology · 2022Review
- Multi-modal intermediate integrative methods in neuropsychiatric disorders: A review.Computational and structural biotechnology journal · 2022Review
- Evaluation and comparison of multi-omics data integration methods for cancer subtyping.PLoS computational biology · 2021Article
- Article
- Multi-view clustering for multi-omics data using unified embedding.Scientific reports · 2020Article
- An Adaptive Sparse Subspace Clustering for Cell Type Identification.Frontiers in genetics · 2020Article
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Authors and funding
4 authors.
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Abstract
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.
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