DiffNetFDR : differential network analysis with false discovery rate control

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

9 Scopus Citations
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Author(s)

  • Xiao-Fei Zhang
  • Le Ou-Yang
  • Shuo Yang
  • Xiaohua Hu
  • Hong Yan

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)3184-3186
Journal / PublicationBioinformatics
Volume35
Issue number17
Online published24 Jan 2019
Publication statusPublished - 1 Sep 2019

Abstract

To identify biological network rewiring under different conditions, we develop a user-friendly R package, named DiffNetFDR, to implement two methods developed for testing the difference in different Gaussian graphical models. Compared to existing tools, our methods have the following features: (i) they are based on Gaussian graphical models which can capture the changes of conditional dependencies; (ii) they determine the tuning parameters in a data-driven manner; (iii) they take a multiple testing procedure to control the overall false discovery rate; and (iv) our approach defines the differential network based on partial correlation coefficients so that the spurious differential edges caused by the variants of conditional variances can be excluded. We also develop a Shiny application to provide easier analysis and visualization. Simulation studies are conducted to evaluate the performance of our methods. We also apply our methods to two real gene expression datasets. The effectiveness of our methods is validated by the biological significance of the identified differential networks.

Citation Format(s)

DiffNetFDR : differential network analysis with false discovery rate control. / Zhang, Xiao-Fei; Ou-Yang, Le; Yang, Shuo; Hu, Xiaohua; Yan, Hong.

In: Bioinformatics, Vol. 35, No. 17, 01.09.2019, p. 3184-3186.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review