Disentangling direct from indirect relationships in association networks

Author:

Xiao Naijia12,Zhou Aifen12,Kempher Megan L.12,Zhou Benjamin Y.3,Shi Zhou Jason1245,Yuan Mengting126,Guo Xue127ORCID,Wu Linwei12,Ning Daliang12ORCID,Van Nostrand Joy1238,Firestone Mary K.6,Zhou Jizhong12910ORCID

Affiliation:

1. Institute for Environmental Genomics, University of Oklahoma, Norman, OK 73019;

2. Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK 73019;

3. Glomics Inc., Norman, OK 73072;

4. Data Science and Biotechnology Institute, Gladstone Institutes, University of California, San Francisco, CA 94158;

5. Chan Zuckerberg Biohub, San Francisco, CA 94158;

6. Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA 94704;

7. State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China;

8. Office of Research and Graduate Studies, Utah State University, Logan, UT 84322;

9. Earth and Environmental Sciences, Lawrence Berkeley National Laboratory, Berkeley, CA 94705;

10. School of Civil Engineering and Environmental Sciences, University of Oklahoma, Norman, OK 73019

Abstract

Significance Networks are fundamental units for studying complex systems, but reconstructing networks from large-scale experimental data is very challenging in systems biology and microbial ecology, primarily due to the difficulty in unraveling direct and indirect interactions. By tackling several mathematical challenges, this study provides a conceptual framework for disentangling direct and indirect relationships in association networks. The application of iDIRECT (Inference of Direct and Indirect Relationships with Effective Copula-based Transitivity) to synthetic, gene expression, and microbial community data demonstrates that it is a powerful, robust, and reliable tool for network inference. The framework developed here will greatly enhance our capability to discern network interactions in various complex systems and allow scientists to address research questions that could not be approached previously.

Funder

DOE-BER Genomic Sciences Program

DOE ENIGMA

DOE BER

NSF

Publisher

Proceedings of the National Academy of Sciences

Subject

Multidisciplinary

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