Spectral clustering using Nyström approximation for the accurate identification of cancer molecular subtypes

Abstract A major challenge in clinical cancer research is the identification of accurate molecular subtype. While unsupervised clustering methods have been applied for class discovery, this clustering method remains a bottleneck in developing accurate method for molecular subtype discovery. In this...

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Autores principales:	Mingguang Shi, Guofu Xu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/04523840f289459abc1d58f99e166de2
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spelling	oai:doaj.org-article:04523840f289459abc1d58f99e166de22021-12-02T16:06:05ZSpectral clustering using Nyström approximation for the accurate identification of cancer molecular subtypes10.1038/s41598-017-05275-32045-2322https://doaj.org/article/04523840f289459abc1d58f99e166de22017-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-05275-3https://doaj.org/toc/2045-2322Abstract A major challenge in clinical cancer research is the identification of accurate molecular subtype. While unsupervised clustering methods have been applied for class discovery, this clustering method remains a bottleneck in developing accurate method for molecular subtype discovery. In this analysis, we hypothesize that spectral clustering method could identify molecular subtypes in correlation with survival outcomes. We propose an accurate subtype identification method, Cancer Subtype Identification with Spectral Clustering using Nyström approximation (CSISCN), for the discovery of molecular subtypes, based on spectral clustering method. CSISCN could be used to improve gene expression-based identification of breast cancer molecular subtypes. We demonstrated that CSISCN identified the molecular subtypes with distinct clinical outcomes and was valid for the number of molecular subtypes. Furthermore, CSISCN identified molecular subtypes for improving clinical and molecular relevance which significantly outperformed consensus clustering and spectral clustering methods. To test the general applicability of the CSISCN, we further applied it on human CRC datasets and AML datasets and demonstrated superior performance as compared to consensus clustering method. In summary, CSISCN demonstrated the great potential in gene expression-based subtype identification.Mingguang ShiGuofu XuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-13 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mingguang Shi Guofu Xu Spectral clustering using Nyström approximation for the accurate identification of cancer molecular subtypes
description	Abstract A major challenge in clinical cancer research is the identification of accurate molecular subtype. While unsupervised clustering methods have been applied for class discovery, this clustering method remains a bottleneck in developing accurate method for molecular subtype discovery. In this analysis, we hypothesize that spectral clustering method could identify molecular subtypes in correlation with survival outcomes. We propose an accurate subtype identification method, Cancer Subtype Identification with Spectral Clustering using Nyström approximation (CSISCN), for the discovery of molecular subtypes, based on spectral clustering method. CSISCN could be used to improve gene expression-based identification of breast cancer molecular subtypes. We demonstrated that CSISCN identified the molecular subtypes with distinct clinical outcomes and was valid for the number of molecular subtypes. Furthermore, CSISCN identified molecular subtypes for improving clinical and molecular relevance which significantly outperformed consensus clustering and spectral clustering methods. To test the general applicability of the CSISCN, we further applied it on human CRC datasets and AML datasets and demonstrated superior performance as compared to consensus clustering method. In summary, CSISCN demonstrated the great potential in gene expression-based subtype identification.
format	article
author	Mingguang Shi Guofu Xu
author_facet	Mingguang Shi Guofu Xu
author_sort	Mingguang Shi
title	Spectral clustering using Nyström approximation for the accurate identification of cancer molecular subtypes
title_short	Spectral clustering using Nyström approximation for the accurate identification of cancer molecular subtypes
title_full	Spectral clustering using Nyström approximation for the accurate identification of cancer molecular subtypes
title_fullStr	Spectral clustering using Nyström approximation for the accurate identification of cancer molecular subtypes
title_full_unstemmed	Spectral clustering using Nyström approximation for the accurate identification of cancer molecular subtypes
title_sort	spectral clustering using nyström approximation for the accurate identification of cancer molecular subtypes
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/04523840f289459abc1d58f99e166de2
work_keys_str_mv	AT mingguangshi spectralclusteringusingnystromapproximationfortheaccurateidentificationofcancermolecularsubtypes AT guofuxu spectralclusteringusingnystromapproximationfortheaccurateidentificationofcancermolecularsubtypes
_version_	1718385066829152256

Spectral clustering using Nyström approximation for the accurate identification of cancer molecular subtypes

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