Network analysis based on low-rank method for mining information on integrated data of multi-cancers. (February 2019)

• Record Type: Journal Article
• Authors: Hou, Mi-Xiao; Gao, Ying-Lian; Liu, Jin-Xing; Dai, Ling-Yun; Kong, Xiang-Zhen; Shang, Junliang
• Is Part Of: Computational biology and chemistry. Volume 78(2019)
• Page Start: 468
• Page End: 473
• Publication Date: 2019-02
• Subjects: Noise reduction -- Gene co-expression network -- Multi-cancers -- Integrated data -- Abnormally expressed genes; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2018.11.027
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: RPCA was introduced into the gene expression data as a processing method to achieve data denoising and reconstruction. Influence by low-rank characteristics of RPCA, network construction was carried out on the reconstructed integrated data of multi-cancers, and some common cancer-related clues were detected. The RPCA-based denoising network mining model is reliable and efficient. 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.