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Differentially Expressed Genes Extracted by the Tensor Robust Principal Component Analysis (TRPCA) Method
Joint Authors
Li, Shengjun
Liu, Jin-Xing
Hu, Yue
Gao, Ying-Lian
Wang, Juan
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-06-02
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Hu, Yue& Liu, Jin-Xing& Gao, Ying-Lian& Li, Shengjun& Wang, Juan. 2019. Differentially Expressed Genes Extracted by the Tensor Robust Principal Component Analysis (TRPCA) Method. Complexity،Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1132347
Modern Language Association (MLA)
Hu, Yue…[et al.]. Differentially Expressed Genes Extracted by the Tensor Robust Principal Component Analysis (TRPCA) Method. Complexity No. 2019 (2019), pp.1-13.
https://search.emarefa.net/detail/BIM-1132347
American Medical Association (AMA)
Hu, Yue& Liu, Jin-Xing& Gao, Ying-Lian& Li, Shengjun& Wang, Juan. Differentially Expressed Genes Extracted by the Tensor Robust Principal Component Analysis (TRPCA) Method. Complexity. 2019. Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1132347
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1132347