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
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Lenguaje:EN
Publicado: Public Library of Science (PLoS) 2014
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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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