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Identifying Differentially Expressed Genes via Weighted Gene Co-expression Network Analysis based on NMF

Author(s):

Mi-Xiao Hou, Jin-Xing Liu*, Junliang Shang and Sha-Sha Wu  

Abstract:


Non-negative matrix factorization (NMF) has been applied to the field of bioinformatics for many years. As a method to identify differentially expressed genes (DEGs), NMF method has also been widespread. Although NMF can make the DEGs easily be identified, it cannot provide more associated information for these DEGs. The methods of network analysis can be used to analyze the correlation of genes, but they caused more data redundancy and great complexity in gene association analysis of high dimensions. And dimensionality reduction is worth considering in this condition. In this paper, we provide a new idea by combining the merits of two: NMF is applied to select DEGs for dimensionality reduction, and then weighted gene co-expression network analysis (WGCNA) is introduced to cluster on DEGs into similar function modules. Candidate pathways are discovered in the most relevant module of cholangiocarcinoma (CHOL). And some hub genes from DEGs are highlighted in the co-expression network. And we also present some candidate genes and pathways that have not yet been confirmed. The combination of NMF and WGCNA as a novel model accomplishes the analysis of DEGs for CHOL. The experiments indicate that the method is effective and the works also provide some useful clues to the reaches of CHOL.

Keywords:

Non-negative matrix factorization, weighted gene co-expression network analysis, differentially expressed genes, gene expression data

Affiliation:

Qufu Normal University - School of Information Science and Engineering Rizhao, Qufu Normal University - School of Information Science and Engineering Rizhao, Qufu Normal University - School of Information Science and Engineering Rizhao, Qufu Normal University - School of Information Science and Engineering Rizhao



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