IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0087446.html
   My bibliography  Save this article

Inferring Nonlinear Gene Regulatory Networks from Gene Expression Data Based on Distance Correlation

Author

Listed:
  • Xiaobo Guo
  • Ye Zhang
  • Wenhao Hu
  • Haizhu Tan
  • Xueqin Wang

Abstract

Nonlinear dependence is general in regulation mechanism of gene regulatory networks (GRNs). It is vital to properly measure or test nonlinear dependence from real data for reconstructing GRNs and understanding the complex regulatory mechanisms within the cellular system. A recently developed measurement called the distance correlation (DC) has been shown powerful and computationally effective in nonlinear dependence for many situations. In this work, we incorporate the DC into inferring GRNs from the gene expression data without any underling distribution assumptions. We propose three DC-based GRNs inference algorithms: CLR-DC, MRNET-DC and REL-DC, and then compare them with the mutual information (MI)-based algorithms by analyzing two simulated data: benchmark GRNs from the DREAM challenge and GRNs generated by SynTReN network generator, and an experimentally determined SOS DNA repair network in Escherichia coli. According to both the receiver operator characteristic (ROC) curve and the precision-recall (PR) curve, our proposed algorithms significantly outperform the MI-based algorithms in GRNs inference.

Suggested Citation

  • Xiaobo Guo & Ye Zhang & Wenhao Hu & Haizhu Tan & Xueqin Wang, 2014. "Inferring Nonlinear Gene Regulatory Networks from Gene Expression Data Based on Distance Correlation," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-7, February.
  • Handle: RePEc:plo:pone00:0087446
    DOI: 10.1371/journal.pone.0087446
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0087446
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0087446&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0087446?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
    ---><---

    Citations

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


    Cited by:

    1. Chaudhuri, Arin & Hu, Wenhao, 2019. "A fast algorithm for computing distance correlation," Computational Statistics & Data Analysis, Elsevier, vol. 135(C), pages 15-24.
    2. Yanzhu Hu & Huiyang Zhao & Xinbo Ai, 2016. "Inferring Weighted Directed Association Network from Multivariate Time Series with a Synthetic Method of Partial Symbolic Transfer Entropy Spectrum and Granger Causality," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-25, November.
    3. Islam Shofiqul & Anand Sonia & Hamid Jemila & Thabane Lehana & Beyene Joseph, 2017. "Comparing the performance of linear and nonlinear principal components in the context of high-dimensional genomic data integration," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(3), pages 199-216, 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:plo:pone00:0087446. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.