IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v72y2016i4p1216-1225.html
   My bibliography  Save this article

CCor: A whole genome network‐based similarity measure between two genes

Author

Listed:
  • Yiming Hu
  • Hongyu Zhao

Abstract

Measuring the similarity between genes is often the starting point for building gene regulatory networks. Most similarity measures used in practice only consider pairwise information with a few also consider network structure. Although theoretical properties of pairwise measures are well understood in the statistics literature, little is known about their statistical properties of those similarity measures based on network structure. In this article, we consider a new whole genome network‐based similarity measure, called CCor, that makes use of information of all the genes in the network. We derive a concentration inequality of CCor and compare it with the commonly used Pearson correlation coefficient for inferring network modules. Both theoretical analysis and real data example demonstrate the advantages of CCor over existing measures for inferring gene modules.

Suggested Citation

  • Yiming Hu & Hongyu Zhao, 2016. "CCor: A whole genome network‐based similarity measure between two genes," Biometrics, The International Biometric Society, vol. 72(4), pages 1216-1225, December.
    • Handle: RePEc:bla:biomet:v:72:y:2016:i:4:p:1216-1225
    DOI: 10.1111/biom.12508
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/biom.12508
    Download Restriction: no
    File URL: https://libkey.io/10.1111/biom.12508?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Zhang Bin & Horvath Steve, 2005. "A General Framework for Weighted Gene Co-Expression Network Analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 4(1), pages 1-45, August.
    2. Dobra, Adrian & Hans, Chris & Jones, Beatrix & Nevins, J.R.Joseph R. & Yao, Guang & West, Mike, 2004. "Sparse graphical models for exploring gene expression data," Journal of Multivariate Analysis, Elsevier, vol. 90(1), pages 196-212, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gong, Tingnan & Zhang, Weiping & Chen, Yu, 2023. "Uncovering block structures in large rectangular matrices," Journal of Multivariate Analysis, Elsevier, vol. 198(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhixiang Lin & Tao Wang & Can Yang & Hongyu Zhao, 2017. "On joint estimation of Gaussian graphical models for spatial and temporal data," Biometrics, The International Biometric Society, vol. 73(3), pages 769-779, September.
    2. Yixuan Qiu & Jing Lei & Kathryn Roeder, 2023. "Gradient-based sparse principal component analysis with extensions to online learning," Biometrika, Biometrika Trust, vol. 110(2), pages 339-360.
    3. Ruiz Vargas, E. & Mitchell, D.G.V. & Greening, S.G. & Wahl, L.M., 2014. "Topology of whole-brain functional MRI networks: Improving the truncated scale-free model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 151-158.
    4. Ahrum Son & Hyunsoo Kim & Jolene K. Diedrich & Casimir Bamberger & Daniel B. McClatchy & Stuart A. Lipton & John R. Yates, 2024. "Using in vivo intact structure for system-wide quantitative analysis of changes in proteins," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    5. Giraud Christophe & Huet Sylvie & Verzelen Nicolas, 2012. "Graph Selection with GGMselect," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(3), pages 1-52, February.
    6. Yan Guo & Hui Yu & Haocan Song & Jiapeng He & Olufunmilola Oyebamiji & Huining Kang & Jie Ping & Scott Ness & Yu Shyr & Fei Ye, 2021. "MetaGSCA: A tool for meta-analysis of gene set differential coexpression," PLOS Computational Biology, Public Library of Science, vol. 17(5), pages 1-15, May.
    7. Paci, Lucia & Consonni, Guido, 2020. "Structural learning of contemporaneous dependencies in graphical VAR models," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    8. Xue Jiang & Han Zhang & Xiongwen Quan & Zhandong Liu & Yanbin Yin, 2017. "Disease-related gene module detection based on a multi-label propagation clustering algorithm," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-17, May.
    9. Mandel, Antoine & Landini, Simone & Gallegati, Mauro & Gintis, Herbert, 2015. "Price dynamics, financial fragility and aggregate volatility," Journal of Economic Dynamics and Control, Elsevier, vol. 51(C), pages 257-277.
    10. Tabak, Benjamin M. & Serra, Thiago R. & Cajueiro, Daniel O., 2009. "The expectation hypothesis of interest rates and network theory: The case of Brazil," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(7), pages 1137-1149.
    11. Wessel N. van Wieringen & Carel F. W. Peeters & Renee X. de Menezes & Mark A. van de Wiel, 2018. "Testing for pathway (in)activation by using Gaussian graphical models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1419-1436, November.
    12. Codazzi, Laura & Colombi, Alessandro & Gianella, Matteo & Argiento, Raffaele & Paci, Lucia & Pini, Alessia, 2022. "Gaussian graphical modeling for spectrometric data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    13. Byol Kim & Song Liu & Mladen Kolar, 2021. "Two‐sample inference for high‐dimensional Markov networks," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 939-962, November.
    14. Blum Yuna & Houée-Bigot Magalie & Causeur David, 2016. "Sparse factor model for co-expression networks with an application using prior biological knowledge," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(3), pages 253-272, June.
    15. Agius Phaedra & Ying Yiming & Campbell Colin, 2009. "Bayesian Unsupervised Learning with Multiple Data Types," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-29, June.
    16. Billy Chang & Rafal Kustra & Weidong Tian, 2013. "Functional-Network-Based Gene Set Analysis Using Gene-Ontology," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-13, February.
    17. Wu, Cen & Zhang, Qingzhao & Jiang, Yu & Ma, Shuangge, 2018. "Robust network-based analysis of the associations between (epi)genetic measurements," Journal of Multivariate Analysis, Elsevier, vol. 168(C), pages 119-130.
    18. Peter Langfelder & Rui Luo & Michael C Oldham & Steve Horvath, 2011. "Is My Network Module Preserved and Reproducible?," PLOS Computational Biology, Public Library of Science, vol. 7(1), pages 1-29, January.
    19. Wang, Hao, 2010. "Sparse seemingly unrelated regression modelling: Applications in finance and econometrics," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2866-2877, November.
    20. Nikolaos Petrakis & Stefano Peluso & Dimitris Fouskakis & Guido Consonni, 2020. "Objective methods for graphical structural learning," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(3), pages 420-438, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:biomet:v:72:y:2016:i:4:p:1216-1225. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.