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Unexpected links reflect the noise in networks
BACKGROUND: Gene covariation networks are commonly used to study biological processes. The inference of gene covariation networks from observational data can be challenging, especially considering the large number of players involved and the small number of biological replicates available for analys...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5480421/ https://www.ncbi.nlm.nih.gov/pubmed/27737689 http://dx.doi.org/10.1186/s13062-016-0155-0 |
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author | Yambartsev, Anatoly Perlin, Michael A. Kovchegov, Yevgeniy Shulzhenko, Natalia Mine, Karina L. Dong, Xiaoxi Morgun, Andrey |
author_facet | Yambartsev, Anatoly Perlin, Michael A. Kovchegov, Yevgeniy Shulzhenko, Natalia Mine, Karina L. Dong, Xiaoxi Morgun, Andrey |
author_sort | Yambartsev, Anatoly |
collection | PubMed |
description | BACKGROUND: Gene covariation networks are commonly used to study biological processes. The inference of gene covariation networks from observational data can be challenging, especially considering the large number of players involved and the small number of biological replicates available for analysis. RESULTS: We propose a new statistical method for estimating the number of erroneous edges in reconstructed networks that strongly enhances commonly used inference approaches. This method is based on a special relationship between sign of correlation (positive/negative) and directionality (up/down) of gene regulation, and allows for the identification and removal of approximately half of all erroneous edges. Using the mathematical model of Bayesian networks and positive correlation inequalities we establish a mathematical foundation for our method. Analyzing existing biological datasets, we find a strong correlation between the results of our method and false discovery rate (FDR). Furthermore, simulation analysis demonstrates that our method provides a more accurate estimate of network error than FDR. CONCLUSIONS: Thus, our study provides a new robust approach for improving reconstruction of covariation networks. REVIEWERS: This article was reviewed by Eugene Koonin, Sergei Maslov, Daniel Yasumasa Takahashi. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13062-016-0155-0) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5480421 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-54804212017-06-23 Unexpected links reflect the noise in networks Yambartsev, Anatoly Perlin, Michael A. Kovchegov, Yevgeniy Shulzhenko, Natalia Mine, Karina L. Dong, Xiaoxi Morgun, Andrey Biol Direct Research BACKGROUND: Gene covariation networks are commonly used to study biological processes. The inference of gene covariation networks from observational data can be challenging, especially considering the large number of players involved and the small number of biological replicates available for analysis. RESULTS: We propose a new statistical method for estimating the number of erroneous edges in reconstructed networks that strongly enhances commonly used inference approaches. This method is based on a special relationship between sign of correlation (positive/negative) and directionality (up/down) of gene regulation, and allows for the identification and removal of approximately half of all erroneous edges. Using the mathematical model of Bayesian networks and positive correlation inequalities we establish a mathematical foundation for our method. Analyzing existing biological datasets, we find a strong correlation between the results of our method and false discovery rate (FDR). Furthermore, simulation analysis demonstrates that our method provides a more accurate estimate of network error than FDR. CONCLUSIONS: Thus, our study provides a new robust approach for improving reconstruction of covariation networks. REVIEWERS: This article was reviewed by Eugene Koonin, Sergei Maslov, Daniel Yasumasa Takahashi. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13062-016-0155-0) contains supplementary material, which is available to authorized users. BioMed Central 2016-10-13 /pmc/articles/PMC5480421/ /pubmed/27737689 http://dx.doi.org/10.1186/s13062-016-0155-0 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Yambartsev, Anatoly Perlin, Michael A. Kovchegov, Yevgeniy Shulzhenko, Natalia Mine, Karina L. Dong, Xiaoxi Morgun, Andrey Unexpected links reflect the noise in networks |
title | Unexpected links reflect the noise in networks |
title_full | Unexpected links reflect the noise in networks |
title_fullStr | Unexpected links reflect the noise in networks |
title_full_unstemmed | Unexpected links reflect the noise in networks |
title_short | Unexpected links reflect the noise in networks |
title_sort | unexpected links reflect the noise in networks |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5480421/ https://www.ncbi.nlm.nih.gov/pubmed/27737689 http://dx.doi.org/10.1186/s13062-016-0155-0 |
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