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

• Type of Material: Article
• Authors: Haodong Liu, Tongji University; Peng Li, Tongji University; Mengyao Zhu, Tongji University; Xiaofei Wang, Shandong University of Science and Technology; Jianwei Lu, Tongji University; Tianwei Yu, Emory University
• Language: English
• Date: 2016-07-05
• Publisher: Public Library of Science
• Publication Version: Final Published Version
• Copyright Statement: © 2016 Liu et al
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1371/journal.pone.0158247
• Title of Journal or Parent Work: PLoS ONE
• ISSN: 1932-6203
• Volume: 11
• Issue: 7
• Start Page: e0158247
• End Page: e0158247
• Grant/Funding Information: National Natural Science Foundation of China (CN) 41476120 to Tianwei Yu.; National Natural Science Foundation of China (CN) 21477087 to Jianwei Lu.; This work was partially supported by NIH grant P30AI50409, 973 Program (No. 2013CB967101) of the Ministry of Science and Technology of China, Natural Science Foundation of China No.41476120, No.61572362, No.81571347 and No.21477087, and the Shanghai Eastern Scholar program.; National Natural Science Foundation of China (CN) 61572362 to Jianwei Lu.; National Institutes of Health P30AI50409 to Tianwei Yu.; Ministry of Science and Technology of the People's Republic of China 2013CB967101 to Jianwei Lu.; National Natural Science Foundation of China (CN) 81571347 to Jianwei Lu.

Abstract

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 https://cran.r-project.org/web/packages/nlnet.