气候变化领域集成服务门户(CCPortal): Disentangling direct from indirect relationships in association networks

CCPortal

DOI	10.1073/pnas.2109995119
	Disentangling direct from indirect relationships in association networks
	Xiao, Naijia; Zhou, Aifen; Kempher, Megan L.; Zhou, Benjamin Y.; Shi, Zhou Jason; Yuan, Mengting; Guo, Xue; Wu, Linwei; Ning, Daliang; Van Nostrand, Joy; Firestone, Mary K.; Zhou, Jizhong
发表日期	2022
ISSN	0027-8424
EISSN	1091-6490
卷号	119 期号:2
英文摘要	Networks are vital tools for understanding and modeling interactions in complex systems in science and engineering, and direct and indirect interactions are pervasive in all types of networks. However, quantitatively disentangling direct and indirect relationships in networks remains a formidable task. Here, we present a framework, called iDIRECT (Inference of Direct and Indirect Relationships with Effective Copula-based Transitivity), for quantitatively inferring direct dependencies in association networks. Using copula-based transitivity, iDIRECT eliminates/ameliorates several challenging mathematical problems, including ill-conditioning, self-looping, and interaction strength overflow. With simulation data as benchmark examples, iDIRECT showed high prediction accuracies. Application of iDIRECT to reconstruct gene regulatory networks in Escherichia coli also revealed considerably higher prediction power than the best-performing approaches in the DREAM5 (Dialogue on Reverse Engineering Assessment and Methods project, #5) Network Inference Challenge. In addition, applying iDIRECT to highly diverse grassland soil microbial communities in response to climate warming showed that the iDIRECT-processed networks were significantly different from the original networks, with considerably fewer nodes, links, and connectivity, but higher relative modularity. Further analysis revealed that the iDIRECTprocessed network was more complex under warming than the control and more robust to both random and target species removal (P < 0.001). As a general approach, iDIRECT has great advantages for network inference, and it should be widely applicable to infer direct relationships in association networks across diverse disciplines in science and engineering.
英文关键词	network analysis; direct relationship; indirect relationship; systems biology; climate change
语种	英语
WOS研究方向	Multidisciplinary Sciences
WOS类目	Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号	WOS:000768580200016
来源期刊	PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/280762
作者单位	University of Oklahoma System; University of Oklahoma - Norman; University of Oklahoma System; University of Oklahoma - Norman; University of California System; University of California San Francisco; The J David Gladstone Institutes; University of California System; University of California Berkeley; Tsinghua University; Utah System of Higher Education; Utah State University; United States Department of Energy (DOE); Lawrence Berkeley National Laboratory; University of Oklahoma System; University of Oklahoma - Norman
推荐引用方式 GB/T 7714	Xiao, Naijia,Zhou, Aifen,Kempher, Megan L.,et al. Disentangling direct from indirect relationships in association networks[J],2022,119(2).
APA	Xiao, Naijia.,Zhou, Aifen.,Kempher, Megan L..,Zhou, Benjamin Y..,Shi, Zhou Jason.,...&Zhou, Jizhong.(2022).Disentangling direct from indirect relationships in association networks.PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA,119(2).
MLA	Xiao, Naijia,et al."Disentangling direct from indirect relationships in association networks".PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 119.2(2022).