Network analysis based on low-rank method for mining information on integrated data of multi-cancers |
| |
| Institution: | 1. School of Information Science and Engineering, Qufu Normal University, Rizhao, China;2. Library of Qufu Normal University, Qufu Normal University, Rizhao, China;3. Co-Innovation Center for Information Supply & Assurance Technology, Anhui University, Hefei, China;1. Departamento de Bioquímica Clínica y Cuantitativa, Facultad de Bioquímica y Ciencias Biológicas, Universidad Nacional del Litoral, Santa Fe, Argentina;2. Instituto de Salud y Ambiente del Litoral (ISAL), Facultad de Bioquímica y Ciencias Biológicas, Universidad Nacional del Litoral-CONICET, Santa Fe, Argentina;1. DBT Centre for Bioinformatics, Presidency University, 86/1 College Street, Kolkata - 700073, India;2. Jhargram Raj College, Jhargram, Paschim Medinipur, India;1. Department of Veterinary Clinical Sciences, Clinic for Swine, JLU Giessen, Germany;2. Institute for Hygiene and Infectious Diseases of Animals, JLU Giessen, Germany;3. Institute for Microbiology, Department of Infectious Diseases, University of Veterinary Medicine, Hannover, Germany;1. Institute of Computational Science and Technology, Guangzhou University, Guangzhou, 510006, China;2. Department of Physics and Electronic information engineering, Wenzhou University, Wenzhou, 325000, Zhejiang, China;3. College of Information engineering, Wenzhou Vocational & Technology College, Wenzhou, 325000, Zhejiang, China;4. Department of Computer and Information Science, Fordham University, New York, NY, 10023, USA |
| |
| Abstract: | The noise problem of cancer sequencing data has been a problem that can’t be ignored. Utilizing considerable way to reduce noise of these cancer data is an important issue in the analysis of gene co-expression network. In this paper, we apply a sparse and low-rank method which is Robust Principal Component Analysis (RPCA) to solve the noise problem for integrated data of multi-cancers from The Cancer Genome Atlas (TCGA). And then we build the gene co-expression network based on the integrated data after noise reduction. Finally, we perform nodes and pathways mining on the denoising networks. Experiments in this paper show that after denoising by RPCA, the gene expression data tend to be orderly and neat than before, and the constructed networks contain more pathway enrichment information than unprocessed data. Moreover, learning from the betweenness centrality of the nodes in the network, we find some abnormally expressed genes and pathways proven that are associated with many cancers from the denoised network. The experimental results indicate that our method is reasonable and effective, and we also find some candidate suspicious genes that may be linked to multi-cancers. |
| |
| Keywords: | Noise reduction Gene co-expression network Multi-cancers Integrated data Abnormally expressed genes |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
