scholarly journals Differentially Expressed Genes Extracted by the Tensor Robust Principal Component Analysis (TRPCA) Method

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Yue Hu ◽  
Jin-Xing Liu ◽  
Ying-Lian Gao ◽  
Sheng-Jun Li ◽  
Juan Wang

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.

2020 ◽  
Vol 367 ◽  
pp. 124783 ◽  
Author(s):  
Jing-Hua Yang ◽  
Xi-Le Zhao ◽  
Teng-Yu Ji ◽  
Tian-Hui Ma ◽  
Ting-Zhu Huang

2019 ◽  
Vol 9 (7) ◽  
pp. 1411 ◽  
Author(s):  
Shuting Cai ◽  
Qilun Luo ◽  
Ming Yang ◽  
Wen Li ◽  
Mingqing Xiao

Tensor Robust Principal Component Analysis (TRPCA) plays a critical role in handling high multi-dimensional data sets, aiming to recover the low-rank and sparse components both accurately and efficiently. In this paper, different from current approach, we developed a new t-Gamma tensor quasi-norm as a non-convex regularization to approximate the low-rank component. Compared to various convex regularization, this new configuration not only can better capture the tensor rank but also provides a simplified approach. An optimization process is conducted via tensor singular decomposition and an efficient augmented Lagrange multiplier algorithm is established. Extensive experimental results demonstrate that our new approach outperforms current state-of-the-art algorithms in terms of accuracy and efficiency.


2014 ◽  
Vol 998-999 ◽  
pp. 889-893
Author(s):  
Zhu Lin Xiong ◽  
Ce Lun Liu ◽  
Wei Du ◽  
Ze Bin Xie

Non-Line-of-Sight (NLOS) propagation problems badly degrade the accuracy of wireless mobile positioning algorithms, which incurs a large positive bias in the Time-of-Arrival (TOA) measurements. Under several assumptions, the Hankel matrix of TOA data can be decomposed into a low-rank distance matrix and a sparse error matrix. This paper utilizes the robust principal component analysis (RPCA) method to solve the decomposition problem. After estimating the distance, the positioning problem can use existing Line-of-Sight (LOS) based algorithms to calculate the coordinate of the mobile station (MS). Simulation results show our method outperforms other existing NLOS positioning methods and the RPCA based matrix decomposition process can eliminate NLOS effect efficiently.


2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
E. Zhu ◽  
M. Xu ◽  
D. Pi

Noise exhibits low rank or no sparsity in the low-rank matrix recovery, and the nuclear norm is not an accurate rank approximation of low-rank matrix. In the present study, to solve the mentioned problem, a novel nonconvex approximation function of the low-rank matrix was proposed. Subsequently, based on the nonconvex rank approximation function, a novel model of robust principal component analysis was proposed. Such model was solved with the alternating direction method, and its convergence was verified theoretically. Subsequently, the background separation experiments were performed on the Wallflower and SBMnet datasets. Furthermore, the effectiveness of the novel model was verified by numerical experiments.


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