Differentially Expressed Genes Extracted by the Tensor Robust Principal Component Analysis (TRPCA) Method
In the big data era, sequencing technology has produced a large number of biological sequencing data. Different views of the cancer genome data provide sufficient complementary information to explore genetic activity. The identification of differentially expressed genes from multiview cancer gene da...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/6136245 |
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| _version_ | 1849399663939026944 |
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| author | Yue Hu Jin-Xing Liu Ying-Lian Gao Sheng-Jun Li Juan Wang |
| author_facet | Yue Hu Jin-Xing Liu Ying-Lian Gao Sheng-Jun Li Juan Wang |
| author_sort | Yue Hu |
| collection | DOAJ |
| description | In the big data era, sequencing technology has produced a large number of biological sequencing data. Different views of the cancer genome data provide sufficient complementary information to explore genetic activity. The identification of differentially expressed genes from multiview cancer gene data is of great importance in cancer diagnosis and treatment. In this paper, we propose a novel method for identifying differentially expressed genes based on tensor robust principal component analysis (TRPCA), which extends the matrix method to the processing of multiway data. To identify differentially expressed genes, the plan is carried out as follows. First, multiview data containing cancer gene expression data from different sources are prepared. Second, the original tensor is decomposed into a sum of a low-rank tensor and a sparse tensor using TRPCA. Third, the differentially expressed genes are considered to be sparse perturbed signals and then identified based on the sparse tensor. Fourth, the differentially expressed genes are evaluated using Gene Ontology and Gene Cards tools. The validity of the TRPCA method was tested using two sets of multiview data. The experimental results showed that our method is superior to the representative methods in efficiency and accuracy aspects. |
| format | Article |
| id | doaj-art-9d041989489e4e16a69d7bfcb18c7e32 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-9d041989489e4e16a69d7bfcb18c7e322025-08-20T03:38:16ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/61362456136245Differentially Expressed Genes Extracted by the Tensor Robust Principal Component Analysis (TRPCA) MethodYue Hu0Jin-Xing Liu1Ying-Lian Gao2Sheng-Jun Li3Juan Wang4School of Information Science and Engineering, Qufu Normal University, Rizhao 276826, ChinaSchool of Information Science and Engineering, Qufu Normal University, Rizhao 276826, ChinaLibrary of Qufu Normal University, Qufu Normal University, Rizhao 276826, ChinaSchool of Information Science and Engineering, Qufu Normal University, Rizhao 276826, ChinaSchool of Information Science and Engineering, Qufu Normal University, Rizhao 276826, ChinaIn the big data era, sequencing technology has produced a large number of biological sequencing data. Different views of the cancer genome data provide sufficient complementary information to explore genetic activity. The identification of differentially expressed genes from multiview cancer gene data is of great importance in cancer diagnosis and treatment. In this paper, we propose a novel method for identifying differentially expressed genes based on tensor robust principal component analysis (TRPCA), which extends the matrix method to the processing of multiway data. To identify differentially expressed genes, the plan is carried out as follows. First, multiview data containing cancer gene expression data from different sources are prepared. Second, the original tensor is decomposed into a sum of a low-rank tensor and a sparse tensor using TRPCA. Third, the differentially expressed genes are considered to be sparse perturbed signals and then identified based on the sparse tensor. Fourth, the differentially expressed genes are evaluated using Gene Ontology and Gene Cards tools. The validity of the TRPCA method was tested using two sets of multiview data. The experimental results showed that our method is superior to the representative methods in efficiency and accuracy aspects.http://dx.doi.org/10.1155/2019/6136245 |
| spellingShingle | Yue Hu Jin-Xing Liu Ying-Lian Gao Sheng-Jun Li Juan Wang Differentially Expressed Genes Extracted by the Tensor Robust Principal Component Analysis (TRPCA) Method Complexity |
| title | Differentially Expressed Genes Extracted by the Tensor Robust Principal Component Analysis (TRPCA) Method |
| title_full | Differentially Expressed Genes Extracted by the Tensor Robust Principal Component Analysis (TRPCA) Method |
| title_fullStr | Differentially Expressed Genes Extracted by the Tensor Robust Principal Component Analysis (TRPCA) Method |
| title_full_unstemmed | Differentially Expressed Genes Extracted by the Tensor Robust Principal Component Analysis (TRPCA) Method |
| title_short | Differentially Expressed Genes Extracted by the Tensor Robust Principal Component Analysis (TRPCA) Method |
| title_sort | differentially expressed genes extracted by the tensor robust principal component analysis trpca method |
| url | http://dx.doi.org/10.1155/2019/6136245 |
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