Detecting subnetwork-level dynamic correlations.
The biological regulatory system is highly dynamic. The correlations between many functionally related genes change over different biological conditions. Finding dynamic relations on the existing biological network may reveal important regulatory mechanisms. Currently no method is available to detect subnetwork-level dynamic correlations systematically on the genome-scale network. Two major issues hampered the development. The first is gene expression profiling data usually do not contain time course measurements to facilitate the analysis of dynamic relations, which can be partially addressed by using certain genes as indicators of biological conditions. Secondly, it is unclear how to effectively delineate subnetworks, and define dynamic relations between them.



Here we propose a new method named LANDD (Liquid Association for Network Dynamics Detection) to find subnetworks that show substantial dynamic correlations, as defined by subnetwork A is concentrated with Liquid Association scouting genes for subnetwork B. The method produces easily interpretable results because of its focus on subnetworks that tend to comprise functionally related genes. Also, the collective behaviour of genes in a subnetwork is a much more reliable indicator of underlying biological conditions compared to using single genes as indicators. We conducted extensive simulations to validate the method's ability to detect subnetwork-level dynamic correlations. Using a real gene expression dataset and the human protein-protein interaction network, we demonstrate the method links subnetworks of distinct biological processes, with both confirmed relations and plausible new functional implications. We also found signal transduction pathways tend to show extensive dynamic relations with other functional groups.



The R package is available at https://cran.r-project.org/web/packages/LANDD CONTACTS: yunba@pcom.edu, jwlu33@hotmail.com or tianwei.yu@emory.eduSupplementary information: Supplementary data are available at Bioinformatics online.
School of Software Engineering, Tongji University, Shanghai, China.
Yan
Y
Yan
School of Software Engineering, Tongji University, Shanghai, China.
Shangzhao
S
Qiu
Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA.
Zhuxuan
Z
Jin
School of Software Engineering, Tongji University, Shanghai, China.
Sihong
S
Gong
Department of Pharmaceutical Sciences, School of Pharmacy, Philadelphia College of Osteopathic Medicine, Suwanee, GA, USA.
Yun
Y
Bai
School of Software Engineering, Tongji University, Shanghai, China.|Institute of Advanced Translational Medicine, Tongji University, Shanghai, China.
Jianwei
J
Lu
Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA.
Tianwei
T
Yu
D003198:Computer Simulation; D053263:Gene Regulatory Networks; D006801:Humans; D060066:Protein Interaction Maps; D015398:Signal Transduction; D012984:Software; D049490:Systems Biology / Q000379:methods*; D059467:Transcriptome
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