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

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  • Haodong Liu
  • Peng Li
  • Mengyao Zhu
  • Xiaofei Wang
  • Jianwei Lu
  • Tianwei Yu

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.

Suggested Citation

  • Haodong Liu & Peng Li & Mengyao Zhu & Xiaofei Wang & Jianwei Lu & Tianwei Yu, 2016. "Nonlinear Network Reconstruction from Gene Expression Data Using Marginal Dependencies Measured by DCOL," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-12, July.
    • Handle: RePEc:plo:pone00:0158247
    DOI: 10.1371/journal.pone.0158247
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