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A powerful score-based statistical test for group difference in weighted biological networks
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  • 作者:Jiadong Ji ; Zhongshang Yuan ; Xiaoshuai Zhang ; Fuzhong Xue
  • 关键词:Network medicine ; Systems epidemiology ; Score ; based statistical test ; Network comparison
  • 刊名:BMC Bioinformatics
  • 出版年:2016
  • 出版时间:December 2016
  • 年:2016
  • 卷:17
  • 期:1
  • 全文大小:1,286 KB
  • 参考文献:1.Barabasi AL, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human disease. Nat Rev Genet. 2011;12(1):56–68.PubMedCentral CrossRef PubMed
    2.Bedelbaeva K, Snyder A, Gourevitch D, Clark L, Zhang XM, Leferovich J, et al. Lack of p21 expression links cell cycle control and appendage regeneration in mice. Proc Natl AcadSci U S A. 2010;107(13):5845–50.CrossRef
    3.Schadt EE. Molecular networks as sensors and drivers of common human diseases. Nature. 2009;461(7261):218–23.CrossRef PubMed
    4.Barabasi AL, Oltvai ZN. Network biology: understanding the cell’s functional organization. Nat Rev Genet. 2004;5(2):101–13.CrossRef PubMed
    5.Albert R. Scale-free networks in cell biology. J Cell Sci. 2005;118(Pt 21):4947–57.CrossRef PubMed
    6.Wu X, Jiang R, Zhang MQ, Li S. Network-based global inference of human disease genes. Mol Syst Biol. 2008;4:189.PubMedCentral CrossRef PubMed
    7.Taylor IW, Linding R, Warde-Farley D, Liu Y, Pesquita C, Faria D, et al. Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat Biotechnol. 2009;27(2):199–204.CrossRef PubMed
    8.Laenen G, Thorrez L, Bornigen D, Moreau Y. Finding the targets of a drug by integration of gene expression data with a protein interaction network. Mol Biosyst. 2013;9(7):1676–85.CrossRef PubMed
    9.Yang B, Zhang J, Yin Y, Zhang Y. Network-based inference framework for identifying cancer genes from gene expression data. Biomed Res Int. 2013;2013:401649.PubMedCentral PubMed
    10.Wu B, Li C, Du Z, Yao Q, Wu J, Feng L, et al. Network based analyses of gene expression profile of LCN2 overexpression in esophageal squamous cell carcinoma. Sci Rep. 2014;4:5403.PubMedCentral PubMed
    11.Hafeman DM, Schwartz S. Opening the Black Box: a motivation for the assessment of mediation. Int J Epidemiol. 2009;38(3):838–45.CrossRef PubMed
    12.Haring R, Wallaschofski H. Diving through the “-omics”: the case for deep phenotyping and systems epidemiology. OMICS. 2012;16(5):231–4.PubMedCentral CrossRef PubMed
    13.Lund E, Dumeaux V. Systems epidemiology in cancer. Cancer Epidemiol Biomarkers Prev. 2008;17(11):2954–7.CrossRef PubMed
    14.de la Fuente A. From ‘differential expression’ to ‘differential networking’ - identification of dysfunctional regulatory networks in diseases. Trends Genet. 2010;26(7):326–33.CrossRef PubMed
    15.Ji J, Yuan Z, Zhang X, Li F, Xu J, Liu Y, et al. Detection for pathway effect contributing to disease in systems epidemiology with a case-control design. BMJ Open. 2015;5(1):e006721.PubMedCentral CrossRef PubMed
    16.Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559.PubMedCentral CrossRef PubMed
    17.Zhang B, Li H, Riggins RB, Zhan M, Xuan J, Zhang Z, et al. Differential dependency network analysis to identify condition-specific topological changes in biological networks. Bioinformatics. 2009;25(4):526–32.PubMedCentral CrossRef PubMed
    18.Valcarcel B, Wurtz P, Seicha BNK, Tukiainen T, Kangas AJ, Soininen P, et al. A differential network approach to exploring differences between biological states: an application to prediabetes. PLoS One. 2011;6(9):e24702.PubMedCentral CrossRef PubMed
    19.Yates PD, Mukhopadhyay ND. An inferential framework for biological network hypothesis tests. BMC Bioinformatics. 2013;14:94.PubMedCentral CrossRef PubMed
    20.Reverter A, Ingham A, Lehnert SA, Tan SH, Wang Y, Ratnakumar A, et al. Simultaneous identification of differential gene expression and connectivity in inflammation, adipogenesis and cancer. Bioinformatics. 2006;22(19):2396–404.CrossRef PubMed
    21.Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol. 2005;4(1):Article17.PubMed
    22.Gill R, Datta S, Datta S. A statistical framework for differential network analysis from microarray data. BMC Bioinformatics. 2010;11:95.PubMedCentral CrossRef PubMed
    23.Kim J, Wozniak JR, Mueller BA, Shen X, Pan W. Comparison of statistical tests for group differences in brain functional networks. Neuroimage. 2014;101:681–94.PubMedCentral CrossRef PubMed
    24.Fleiss JL. On the distribution of a linear combination of independent chi squares. J Am Stat Assoc. 1971.
    25.Zhang FR, Huang W, Chen SM, Sun LD, Liu H, Li Y, et al. Genomewide association study of leprosy. N Engl J Med. 2009;361(27):2609–18.CrossRef PubMed
    26.Tothill RW, Tinker AV, George J, Brown R, Fox SB, Lade S, et al. Novel molecular subtypes of serous and endometrioid ovarian cancer linked to clinical outcome. Clin Cancer Res. 2008;14(16):5198–208.CrossRef PubMed
    27.Fresno VJA, Casado E, de Castro J, Cejas P, Belda-Iniesta C, Gonzalez-Baron M. PI3K/Aktsignalling pathway and cancer. Cancer Treat Rev. 2004;30(2):193–204.CrossRef
    28.Rose SL. Notch signaling pathway in ovarian cancer. Int J Gynecol Cancer. 2009;19(4):564–6.CrossRef PubMed
    29.Groeneweg JW, Foster R, Growdon WB, Verheijen R, Rueda BR. Notch signaling in serous ovarian cancer. J Ovarian Res. 2014;7(1):95.PubMedCentral CrossRef PubMed
    30.Fukushima A. DiffCorr: an R package to analyze and visualize differential correlations in biological networks. Gene. 2013;518(1):209–14.CrossRef PubMed
    31.Rao W, Li H, Song F, Zhang R, Yin Q, Wang Y, et al. OVA66 increases cell growth, invasion and survival via regulation of IGF-1R-MAPK signaling in human cancer cells. Carcinogenesis. 2014;35(7):1573–81.CrossRef PubMed
    32.Liu MX, Siu MK, Liu SS, Yam JW, Ngan HY, Chan DW. Epigenetic silencing of microRNA-199b-5p is associated with acquired chemoresistance via activation of JAG1-Notch1 signaling in ovarian cancer. Oncotarget. 2014;5(4):944–58.PubMedCentral CrossRef PubMed
    33.Wang LL, Cai HQ, Dong XQ, Zhang LW, Jiang SS, Zhao N, et al. Differentially expressed gene profiles in the serum before and after the ultrasound-guided ethanol sclerotherapy in patients with ovarian endometriomas. Clin Biochem. 2015;48(16-17):1131–7.CrossRef PubMed
    34.Galic V, Shawber CJ, Reeves C, Shah M, Murtomaki A, Wright J, et al. NOTCH2 expression is decreased in epithelial ovarian cancer and is related to the tumor histological subtype. Pathol Discov. 2013;1:4.PubMedCentral CrossRef PubMed
    35.Kulic I, Robertson G, Chang L, Baker JH, Lockwood WW, Mok W, et al. Loss of the Notch effector RBPJ promotes tumorigenesis. J Exp Med. 2015;212(1):37–52.PubMedCentral CrossRef PubMed
    36.Sinnathamby G, Zerfass J, Hafner J, Block P, Nickens Z, Hobeika A, et al. ADAM metallopeptidase domain 17 (ADAM17) is naturally processed through major histocompatibility complex (MHC) class I molecules and is a potential immunotherapeutic target in breast, ovarian and prostate cancers. Clin Exp Immunol. 2011;163(3):324–32.PubMedCentral CrossRef PubMed
    37.Xiong M, Feghali-Bostwick CA, Arnett FC, Zhou X. A systems biology approach to genetic studies of complex diseases. FEBS Lett. 2005;579(24):5325–32.CrossRef PubMed
  • 作者单位:Jiadong Ji (1)
    Zhongshang Yuan (1)
    Xiaoshuai Zhang (1)
    Fuzhong Xue (1)

    1. Department of Biostatistics, School of Public Health, Shandong University, PO Box 100, Jinan, 250012, Shandong, China
  • 刊物主题:Bioinformatics; Microarrays; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Combinatorial Libraries; Algorithms;
  • 出版者:BioMed Central
  • ISSN:1471-2105
文摘
Background Complex disease is largely determined by a number of biomolecules interwoven into networks, rather than a single biomolecule. A key but inadequately addressed issue is how to test possible differences of the networks between two groups. Group-level comparison of network properties may shed light on underlying disease mechanisms and benefit the design of drug targets for complex diseases. We therefore proposed a powerful score-based statistic to detect group difference in weighted networks, which simultaneously capture the vertex changes and edge changes.

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