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

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 us...

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Detalles Bibliográficos
Autores principales: Liu, Haodong, Li, Peng, Zhu, Mengyao, Wang, Xiaofei, Lu, Jianwei, Yu, Tianwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4933395/
https://www.ncbi.nlm.nih.gov/pubmed/27380516
http://dx.doi.org/10.1371/journal.pone.0158247
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author Liu, Haodong
Li, Peng
Zhu, Mengyao
Wang, Xiaofei
Lu, Jianwei
Yu, Tianwei
author_facet Liu, Haodong
Li, Peng
Zhu, Mengyao
Wang, Xiaofei
Lu, Jianwei
Yu, Tianwei
author_sort Liu, Haodong
collection PubMed
description 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.
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spelling pubmed-49333952016-07-18 Nonlinear Network Reconstruction from Gene Expression Data Using Marginal Dependencies Measured by DCOL Liu, Haodong Li, Peng Zhu, Mengyao Wang, Xiaofei Lu, Jianwei Yu, Tianwei PLoS One Research Article 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. Public Library of Science 2016-07-05 /pmc/articles/PMC4933395/ /pubmed/27380516 http://dx.doi.org/10.1371/journal.pone.0158247 Text en © 2016 Liu et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Liu, Haodong
Li, Peng
Zhu, Mengyao
Wang, Xiaofei
Lu, Jianwei
Yu, Tianwei
Nonlinear Network Reconstruction from Gene Expression Data Using Marginal Dependencies Measured by DCOL
title Nonlinear Network Reconstruction from Gene Expression Data Using Marginal Dependencies Measured by DCOL
title_full Nonlinear Network Reconstruction from Gene Expression Data Using Marginal Dependencies Measured by DCOL
title_fullStr Nonlinear Network Reconstruction from Gene Expression Data Using Marginal Dependencies Measured by DCOL
title_full_unstemmed Nonlinear Network Reconstruction from Gene Expression Data Using Marginal Dependencies Measured by DCOL
title_short Nonlinear Network Reconstruction from Gene Expression Data Using Marginal Dependencies Measured by DCOL
title_sort nonlinear network reconstruction from gene expression data using marginal dependencies measured by dcol
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4933395/
https://www.ncbi.nlm.nih.gov/pubmed/27380516
http://dx.doi.org/10.1371/journal.pone.0158247
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