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Current Bioinformatics

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ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

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Research Article

A New Model of Identifying Differentially Expressed Genes via Weighted Network Analysis Based on Dimensionality Reduction Method

Author(s): Mi-Xiao Hou, Jin-Xing Liu*, Ying-Lian Gao, Junliang Shang, Sha-Sha Wu and Sha-Sha Yuan

Volume 14, Issue 8, 2019

Page: [762 - 770] Pages: 9

DOI: 10.2174/1574893614666181220094235

Price: $65

Abstract

Background: As a method to identify Differentially Expressed Genes (DEGs), Non- Negative Matrix Factorization (NMF) has been widely praised in bioinformatics. Although NMF can make DEGs to be easily identified, it cannot provide more associated information for these DEGs.

Objective: 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. Dimensionality reduction is worth considering in this condition.

Methods: In this paper, we provide a new framework 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. The combination of NMF and WGCNA as a novel model accomplishes the analysis of DEGs for cholangiocarcinoma (CHOL).

Results: Some hub genes from DEGs are highlighted in the co-expression network. Candidate pathways and genes are also discovered in the most relevant module of CHOL.

Conclusion: The experiments indicate that our framework 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, cholangiocarcinoma, gene transcripts.

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[1]
Lee DD, Seung HS. Learning the parts of objects by non-negative matrix factorization. Nature 1999; 401(6755): 788-91.
[2]
Shi J, Luo Z. Research on the Advances of nonnegative matrix factorization and its application in bioinformatics. Comput Eng Sci 2010; 32: 117-23.
[3]
Heger A, Holm L. Sensitive pattern discovery with ‘fuzzy’ alignments of distantly related proteins. Bioinformatics 2003; 19(Suppl. 1): i130-7.
[4]
Jung I, Lee J, Lee SY, Kim D. Application of nonnegative matrix factorization to improve profile-profile alignment features for fold recognition and remote homolog detection. BMC Bioinformatics 2008; 9(1): 298.
[5]
Kim PM, Tidor B. Subsystem identification through dimensionality reduction of large-scale gene expression data. Genome Res 2003; 13(7): 1706-18.
[6]
Chagoyen M, Carmona-Saez P, Shatkay H, Carazo JM, Pascual-Montano A. Discovering semantic features in the literature: a foundation for building functional associations. BMC Bioinformatics 2006; 7(1): 41.
[7]
Dai LY, Feng CM, Liu JX, Zheng CH, Yu J, Hou MX. Robust Nonnegative Matrix Factorization via Joint Graph Laplacian and Discriminative Information for Identifying Differentially Expressed Genes. Complexity 2017; 2017(40): 1-11.
[8]
Guan N, Tao D, Luo Z, Yuan B. NeNMF: An Optimal Gradient Method for Nonnegative Matrix Factorization. IEEE Trans Signal Process 2012; 60(6): 2882-98.
[9]
Cai D, He X, Han J, Huang TS. Graph regularized nonnegative matrix factorization for data representation. Pattern Analysis and Machine Intelligence. IEEE Transactions on 2011; 33(8): 1548-60.
[10]
Langfelder P, Horvath S. WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics 2008; 9(1): 559.
[11]
Zhang B, Horvath S. A General Framework For Weighted Gene Co-Expression Network Analysis Stat Appl Genet Mol Biol 2005. 4: Article17.
[12]
Plaisier CL, Horvath S, Huertas-Vazquez A, et al. A systems genetics approach implicates USF1, FADS3, and other causal candidate genes for familial combined hyperlipidemia. PLoS Genet 2009; 5(9)e1000642
[13]
DiLeo MV, Strahan GD, den Bakker M, Hoekenga OA. Weighted correlation network analysis (WGCNA) applied to the tomato fruit metabolome. PLoS One 2011; 6(10)e26683
[14]
Malki K, Tosto MG, Jumabhoy I, et al. Integrative mouse and human mRNA studies using WGCNA nominates novel candidate genes involved in the pathogenesis of major depressive disorder. Pharmacogenomics 2013; 14(16): 1979-90.
[15]
Pei G, Chen L, Zhang W. WGCNA Application to Proteomic and Metabolomic Data Analysis. Methods Enzymol 2017; 585: 135-58.
[16]
Ray S, Bandyopadhyay S. A NMF based approach for integrating multiple data sources to predict HIV-1-human PPIs. BMC Bioinformatics 2016; 17(1): 121.
[17]
Li Y, Liu Z, Li Q, et al. Computational Discovery of Molecular Mechanisms in Wheat Cold Resistance from RNA-seq Data
[18]
Lee DD, Seung HS. Algorithms for non-negative matrix factorization. Adv Neural Inf Process Syst 2001; 13: 556-62.
[19]
Song C, Lei P, Wang T. Gene Co-expression Network Analysis Based on WGCNA Algorithm-Theory and Implementation in R Software. Genomics and Applied Biology 2013; 32(1): 135-41.
[20]
Network CGAR. Integrated genomic characterization of oesophageal carcinoma. Nature 2017; 541(7636): 169-75.
[21]
Rizvi S, Gores GJ. Pathogenesis, diagnosis, and management of cholangiocarcinoma. Gastroenterology 2013; 145(6): 1215-29.
[22]
Candes EJ, Li X, Ma Y, Wright J. Robust principal component analysis? J Assoc Comput Mach 2011; 58(3): article no. 11.
[23]
Liu JX, Zheng CH, Xu Y. Extracting plants core genes responding to abiotic stresses by penalized matrix decomposition. Comput Biol Med 2012; 42(5): 582-9.
[24]
Liu JX, Xu Y, Zheng CH, Kong H, Lai ZH. RPCA-based tumor classification using gene exprssion data. IEEE/ACM Trans Comput Biol Bioinform 2015; 12(4): 964-70.
[25]
Yuanying C. Peng: Fatty acid metabolism and cancer development. Sci Bull 2016; (19): 1473-9.
[26]
Shannon P, Markiel A, Ozier O, et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res 2003; 13(11): 2498-504.
[27]
Isomoto H, Kobayashi S, Werneburg NW, et al. Interleukin 6 upregulates myeloid cell leukemia-1 expression through a STAT3 pathway in cholangiocarcinoma cells. Hepatology 2005; 42(6): 1329-38.
[28]
Zhao PO, Li X, Lu Y, Liu L. Downregulated expression of PHLDA1 protein is associated with a malignant phenotype of cholangiocarcinoma. Oncol Lett 2015; 10(2): 895-900.
[29]
Zhou M, Ouyang J, Takeshi T, Yoshiro M. Effect and mechanism of human serum amyloid A family on tumor metastasis. China J Cancer Prev Treat 2010; 17(21): 1701-4.
[30]
Conte M, Franceschi C, Sandri M, Salvioli S. Perilipin 2 and Age-Related Metabolic Diseases: A New Perspective. Trends Endocrinol Metab 2016; 27(12): 893-903.

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