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A powerful weighted statistic for detecting group differences of directed biological networks
Complex disease is largely determined by a number of biomolecules interwoven into networks, rather than a single biomolecule. Different physiological conditions such as cases and controls may manifest as different networks. Statistical comparison between biological networks can provide not only new...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5054825/ https://www.ncbi.nlm.nih.gov/pubmed/27686331 http://dx.doi.org/10.1038/srep34159 |
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author | Yuan, Zhongshang Ji, Jiadong Zhang, Xiaoshuai Xu, Jing Ma, Daoxin Xue, Fuzhong |
author_facet | Yuan, Zhongshang Ji, Jiadong Zhang, Xiaoshuai Xu, Jing Ma, Daoxin Xue, Fuzhong |
author_sort | Yuan, Zhongshang |
collection | PubMed |
description | Complex disease is largely determined by a number of biomolecules interwoven into networks, rather than a single biomolecule. Different physiological conditions such as cases and controls may manifest as different networks. Statistical comparison between biological networks can provide not only new insight into the disease mechanism but statistical guidance for drug development. However, the methods developed in previous studies are inadequate to capture the changes in both the nodes and edges, and often ignore the network structure. In this study, we present a powerful weighted statistical test for group differences of directed biological networks, which is independent of the network attributes and can capture the changes in both the nodes and edges, as well as simultaneously accounting for the network structure through putting more weights on the difference of nodes locating on relatively more important position. Simulation studies illustrate that this method had better performance than previous ones under various sample sizes and network structures. One application to GWAS of leprosy successfully identifies the specific gene interaction network contributing to leprosy. Another real data analysis significantly identifies a new biological network, which is related to acute myeloid leukemia. One potential network responsible for lung cancer has also been significantly detected. The source R code is available on our website. |
format | Online Article Text |
id | pubmed-5054825 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-50548252016-10-19 A powerful weighted statistic for detecting group differences of directed biological networks Yuan, Zhongshang Ji, Jiadong Zhang, Xiaoshuai Xu, Jing Ma, Daoxin Xue, Fuzhong Sci Rep Article Complex disease is largely determined by a number of biomolecules interwoven into networks, rather than a single biomolecule. Different physiological conditions such as cases and controls may manifest as different networks. Statistical comparison between biological networks can provide not only new insight into the disease mechanism but statistical guidance for drug development. However, the methods developed in previous studies are inadequate to capture the changes in both the nodes and edges, and often ignore the network structure. In this study, we present a powerful weighted statistical test for group differences of directed biological networks, which is independent of the network attributes and can capture the changes in both the nodes and edges, as well as simultaneously accounting for the network structure through putting more weights on the difference of nodes locating on relatively more important position. Simulation studies illustrate that this method had better performance than previous ones under various sample sizes and network structures. One application to GWAS of leprosy successfully identifies the specific gene interaction network contributing to leprosy. Another real data analysis significantly identifies a new biological network, which is related to acute myeloid leukemia. One potential network responsible for lung cancer has also been significantly detected. The source R code is available on our website. Nature Publishing Group 2016-09-30 /pmc/articles/PMC5054825/ /pubmed/27686331 http://dx.doi.org/10.1038/srep34159 Text en Copyright © 2016, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Yuan, Zhongshang Ji, Jiadong Zhang, Xiaoshuai Xu, Jing Ma, Daoxin Xue, Fuzhong A powerful weighted statistic for detecting group differences of directed biological networks |
title | A powerful weighted statistic for detecting group differences of directed biological networks |
title_full | A powerful weighted statistic for detecting group differences of directed biological networks |
title_fullStr | A powerful weighted statistic for detecting group differences of directed biological networks |
title_full_unstemmed | A powerful weighted statistic for detecting group differences of directed biological networks |
title_short | A powerful weighted statistic for detecting group differences of directed biological networks |
title_sort | powerful weighted statistic for detecting group differences of directed biological networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5054825/ https://www.ncbi.nlm.nih.gov/pubmed/27686331 http://dx.doi.org/10.1038/srep34159 |
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