Identifying subspace gene clusters from microarray data using low-rank representation.

Identifying subspace gene clusters from the gene expression data is useful for discovering novel functional gene interactions. In this paper, we propose to use low-rank representation (LRR) to identify the subspace gene clusters from microarray data. LRR seeks the lowest-rank representation among al...

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Autores principales:	Yan Cui, Chun-Hou Zheng, Jian Yang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d8dc02d66f4a411f8de1c2282c962c8b
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id	oai:doaj.org-article:d8dc02d66f4a411f8de1c2282c962c8b
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spelling	oai:doaj.org-article:d8dc02d66f4a411f8de1c2282c962c8b2021-11-18T07:52:51ZIdentifying subspace gene clusters from microarray data using low-rank representation.1932-620310.1371/journal.pone.0059377https://doaj.org/article/d8dc02d66f4a411f8de1c2282c962c8b2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23527177/?tool=EBIhttps://doaj.org/toc/1932-6203Identifying subspace gene clusters from the gene expression data is useful for discovering novel functional gene interactions. In this paper, we propose to use low-rank representation (LRR) to identify the subspace gene clusters from microarray data. LRR seeks the lowest-rank representation among all the candidates that can represent the genes as linear combinations of the bases in the dataset. The clusters can be extracted based on the block diagonal representation matrix obtained using LRR, and they can well capture the intrinsic patterns of genes with similar functions. Meanwhile, the parameter of LRR can balance the effect of noise so that the method is capable of extracting useful information from the data with high level of background noise. Compared with traditional methods, our approach can identify genes with similar functions yet without similar expression profiles. Also, it could assign one gene into different clusters. Moreover, our method is robust to the noise and can identify more biologically relevant gene clusters. When applied to three public datasets, the results show that the LRR based method is superior to existing methods for identifying subspace gene clusters.Yan CuiChun-Hou ZhengJian YangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 3, p e59377 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yan Cui Chun-Hou Zheng Jian Yang Identifying subspace gene clusters from microarray data using low-rank representation.
description	Identifying subspace gene clusters from the gene expression data is useful for discovering novel functional gene interactions. In this paper, we propose to use low-rank representation (LRR) to identify the subspace gene clusters from microarray data. LRR seeks the lowest-rank representation among all the candidates that can represent the genes as linear combinations of the bases in the dataset. The clusters can be extracted based on the block diagonal representation matrix obtained using LRR, and they can well capture the intrinsic patterns of genes with similar functions. Meanwhile, the parameter of LRR can balance the effect of noise so that the method is capable of extracting useful information from the data with high level of background noise. Compared with traditional methods, our approach can identify genes with similar functions yet without similar expression profiles. Also, it could assign one gene into different clusters. Moreover, our method is robust to the noise and can identify more biologically relevant gene clusters. When applied to three public datasets, the results show that the LRR based method is superior to existing methods for identifying subspace gene clusters.
format	article
author	Yan Cui Chun-Hou Zheng Jian Yang
author_facet	Yan Cui Chun-Hou Zheng Jian Yang
author_sort	Yan Cui
title	Identifying subspace gene clusters from microarray data using low-rank representation.
title_short	Identifying subspace gene clusters from microarray data using low-rank representation.
title_full	Identifying subspace gene clusters from microarray data using low-rank representation.
title_fullStr	Identifying subspace gene clusters from microarray data using low-rank representation.
title_full_unstemmed	Identifying subspace gene clusters from microarray data using low-rank representation.
title_sort	identifying subspace gene clusters from microarray data using low-rank representation.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/d8dc02d66f4a411f8de1c2282c962c8b
work_keys_str_mv	AT yancui identifyingsubspacegeneclustersfrommicroarraydatausinglowrankrepresentation AT chunhouzheng identifyingsubspacegeneclustersfrommicroarraydatausinglowrankrepresentation AT jianyang identifyingsubspacegeneclustersfrommicroarraydatausinglowrankrepresentation
_version_	1718422825359900672

Identifying subspace gene clusters from microarray data using low-rank representation.

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