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Convex multi-view clustering via robust low rank approximation with application to multi-omic data

Convex multi-view clustering via robust low rank approximation with application to multi-omic data
Convex multi-view clustering via robust low rank approximation with application to multi-omic data
Recent advances in high throughput technologies have made large amounts of biomedical omics data accessible to the scientific community. Single omic data clustering has proved its impact in the biomedical and biological research fields. Multi-omic data clustering and multi-omic data integration techniques have shown improved clustering performance and biological insight. Cancer subtype clustering is an important task in the medical field to be able to identify a suitable treatment procedure and prognosis for cancer patients. State of the art multi-view clustering methods are based on non-convex objectives which only guarantee non-global solutions that are high in computational complexity. Only a few convex multi-view methods are present. However, their models do not take into account the intrinsic manifold structure of the data. In this paper, we introduce a convex graph regularized multi-view clustering method that is robust to outliers. We compare our algorithm to state of the art convex and non-convex multi-view and single view clustering methods and show its superiority in clustering cancer subtypes on publicly available cancer genomic datasets from the TCGA repository. We also show our method's better ability to potentially discover cancer subtypes compared to other state of the art multi-view methods.
Bioinformatics, Cancer, Clustering methods, Data models, Genomics, Matrix decomposition, Multi-view clustering, Sparse matrices, cancer subtype identification, convex optimization, multi-omic data, outlier robustness
1545-5963
Shetta, Omar Essam
168fd473-4857-42ce-8c4a-b4e83740462b
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Dasmahapatra, Srinandan
eb5fd76f-4335-4ae9-a88a-20b9e2b3f698
Shetta, Omar Essam
168fd473-4857-42ce-8c4a-b4e83740462b
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Dasmahapatra, Srinandan
eb5fd76f-4335-4ae9-a88a-20b9e2b3f698

Shetta, Omar Essam, Niranjan, Mahesan and Dasmahapatra, Srinandan (2021) Convex multi-view clustering via robust low rank approximation with application to multi-omic data. IEEE/ACM Transactions on Computational Biology and Bioinformatics. (doi:10.1109/TCBB.2021.3122961).

Record type: Article

Abstract

Recent advances in high throughput technologies have made large amounts of biomedical omics data accessible to the scientific community. Single omic data clustering has proved its impact in the biomedical and biological research fields. Multi-omic data clustering and multi-omic data integration techniques have shown improved clustering performance and biological insight. Cancer subtype clustering is an important task in the medical field to be able to identify a suitable treatment procedure and prognosis for cancer patients. State of the art multi-view clustering methods are based on non-convex objectives which only guarantee non-global solutions that are high in computational complexity. Only a few convex multi-view methods are present. However, their models do not take into account the intrinsic manifold structure of the data. In this paper, we introduce a convex graph regularized multi-view clustering method that is robust to outliers. We compare our algorithm to state of the art convex and non-convex multi-view and single view clustering methods and show its superiority in clustering cancer subtypes on publicly available cancer genomic datasets from the TCGA repository. We also show our method's better ability to potentially discover cancer subtypes compared to other state of the art multi-view methods.

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Published date: 27 October 2021
Keywords: Bioinformatics, Cancer, Clustering methods, Data models, Genomics, Matrix decomposition, Multi-view clustering, Sparse matrices, cancer subtype identification, convex optimization, multi-omic data, outlier robustness

Identifiers

Local EPrints ID: 453062
URI: http://eprints.soton.ac.uk/id/eprint/453062
ISSN: 1545-5963
PURE UUID: 67ac7a73-4af7-4ba4-8769-11ce4926d70a
ORCID for Mahesan Niranjan: ORCID iD orcid.org/0000-0001-7021-140X

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Date deposited: 07 Jan 2022 17:50
Last modified: 28 Apr 2022 01:58

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Contributors

Author: Omar Essam Shetta
Author: Mahesan Niranjan ORCID iD
Author: Srinandan Dasmahapatra

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