A class-information-based penalized matrix decomposition for identifying plants core genes responding to abiotic stresses.

In terms of making genes expression data more interpretable and comprehensible, there exists a significant superiority on sparse methods. Many sparse methods, such as penalized matrix decomposition (PMD) and sparse principal component analysis (SPCA), have been applied to extract plants core genes....

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Autores principales:	Jin-Xing Liu, Jian Liu, Ying-Lian Gao, Jian-Xun Mi, Chun-Xia Ma, Dong Wang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b3228c151a3e4e1a85e0d3322838c575
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spelling	oai:doaj.org-article:b3228c151a3e4e1a85e0d3322838c5752021-11-25T06:02:27ZA class-information-based penalized matrix decomposition for identifying plants core genes responding to abiotic stresses.1932-620310.1371/journal.pone.0106097https://doaj.org/article/b3228c151a3e4e1a85e0d3322838c5752014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/25180509/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203In terms of making genes expression data more interpretable and comprehensible, there exists a significant superiority on sparse methods. Many sparse methods, such as penalized matrix decomposition (PMD) and sparse principal component analysis (SPCA), have been applied to extract plants core genes. Supervised algorithms, especially the support vector machine-recursive feature elimination (SVM-RFE) method, always have good performance in gene selection. In this paper, we draw into class information via the total scatter matrix and put forward a class-information-based penalized matrix decomposition (CIPMD) method to improve the gene identification performance of PMD-based method. Firstly, the total scatter matrix is obtained based on different samples of the gene expression data. Secondly, a new data matrix is constructed by decomposing the total scatter matrix. Thirdly, the new data matrix is decomposed by PMD to obtain the sparse eigensamples. Finally, the core genes are identified according to the nonzero entries in eigensamples. The results on simulation data show that CIPMD method can reach higher identification accuracies than the conventional gene identification methods. Moreover, the results on real gene expression data demonstrate that CIPMD method can identify more core genes closely related to the abiotic stresses than the other methods.Jin-Xing LiuJian LiuYing-Lian GaoJian-Xun MiChun-Xia MaDong WangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 9, p e106097 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jin-Xing Liu Jian Liu Ying-Lian Gao Jian-Xun Mi Chun-Xia Ma Dong Wang A class-information-based penalized matrix decomposition for identifying plants core genes responding to abiotic stresses.
description	In terms of making genes expression data more interpretable and comprehensible, there exists a significant superiority on sparse methods. Many sparse methods, such as penalized matrix decomposition (PMD) and sparse principal component analysis (SPCA), have been applied to extract plants core genes. Supervised algorithms, especially the support vector machine-recursive feature elimination (SVM-RFE) method, always have good performance in gene selection. In this paper, we draw into class information via the total scatter matrix and put forward a class-information-based penalized matrix decomposition (CIPMD) method to improve the gene identification performance of PMD-based method. Firstly, the total scatter matrix is obtained based on different samples of the gene expression data. Secondly, a new data matrix is constructed by decomposing the total scatter matrix. Thirdly, the new data matrix is decomposed by PMD to obtain the sparse eigensamples. Finally, the core genes are identified according to the nonzero entries in eigensamples. The results on simulation data show that CIPMD method can reach higher identification accuracies than the conventional gene identification methods. Moreover, the results on real gene expression data demonstrate that CIPMD method can identify more core genes closely related to the abiotic stresses than the other methods.
format	article
author	Jin-Xing Liu Jian Liu Ying-Lian Gao Jian-Xun Mi Chun-Xia Ma Dong Wang
author_facet	Jin-Xing Liu Jian Liu Ying-Lian Gao Jian-Xun Mi Chun-Xia Ma Dong Wang
author_sort	Jin-Xing Liu
title	A class-information-based penalized matrix decomposition for identifying plants core genes responding to abiotic stresses.
title_short	A class-information-based penalized matrix decomposition for identifying plants core genes responding to abiotic stresses.
title_full	A class-information-based penalized matrix decomposition for identifying plants core genes responding to abiotic stresses.
title_fullStr	A class-information-based penalized matrix decomposition for identifying plants core genes responding to abiotic stresses.
title_full_unstemmed	A class-information-based penalized matrix decomposition for identifying plants core genes responding to abiotic stresses.
title_sort	class-information-based penalized matrix decomposition for identifying plants core genes responding to abiotic stresses.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/b3228c151a3e4e1a85e0d3322838c575
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A class-information-based penalized matrix decomposition for identifying plants core genes responding to abiotic stresses.

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