DiffNetFDR: Differential network analysis with false discovery rate control

Bioinformatics
Xiao-Fei ZhangHong Yan

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. R package and Shiny app are available at https://github.com/Zhangxf-ccnu/DiffNetFDR. Supplementary data are available at Bioinformatics online.

References

May 13, 2014·Journal of the Royal Statistical Society. Series B, Statistical Methodology·Patrick DanaherDaniela M Witten
Sep 30, 2016·Scientific Reports·Xiao-Fei ZhangHong Yan
Jul 12, 2018·IEEE Transactions on Cybernetics·Le Ou-YangHong Yan

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