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PLoS One. 2016 Jul 5;11(7):e0158247. doi: 10.1371/journal.pone.0158247. eCollection 2016.

Nonlinear Network Reconstruction from Gene Expression Data Using Marginal Dependencies Measured by DCOL.

Liu H1, Li P2, Zhu M1, Wang X3, Lu J1,4, Yu T5.

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School of Software Engineering, Tongji University, Shanghai, China.
School of Life Sciences and Technology, Tongji University, Shanghai, China.
College of Information Science and Engineering, Shandong University of Science and Technology, Shandong, China.
Institute of Translational Medicine, Tongji University, Shanghai, China.
Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America.


Reconstruction of networks from high-throughput expression data is an important tool to identify new regulatory relations. Given that nonlinear and complex relations exist between biological units, methods that can utilize nonlinear dependencies may yield insights that are not provided by methods using linear associations alone. We have previously developed a distance to measure predictive nonlinear relations, the Distance based on Conditional Ordered List (DCOL), which is sensitive and computationally efficient on large matrices. In this study, we explore the utility of DCOL in the reconstruction of networks, by combining it with local false discovery rate (lfdr)-based inference. We demonstrate in simulations that the new method named nlnet is effective in recovering hidden nonlinear modules. We also demonstrate its utility using a single cell RNA seq dataset. The method is available as an R package at

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