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Empirical Bayes method for reducing false discovery rates of correlation matrices with block diagonal structure
BACKGROUND: Correlation matrices are important in inferring relationships and networks between regulatory or signalling elements in biological systems. With currently available technology sample sizes for experiments are typically small, meaning that these correlations can be difficult to estimate....
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5389176/ https://www.ncbi.nlm.nih.gov/pubmed/28403823 http://dx.doi.org/10.1186/s12859-017-1623-y |
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author | Pacini, Clare Ajioka, James W. Micklem, Gos |
author_facet | Pacini, Clare Ajioka, James W. Micklem, Gos |
author_sort | Pacini, Clare |
collection | PubMed |
description | BACKGROUND: Correlation matrices are important in inferring relationships and networks between regulatory or signalling elements in biological systems. With currently available technology sample sizes for experiments are typically small, meaning that these correlations can be difficult to estimate. At a genome-wide scale estimation of correlation matrices can also be computationally demanding. RESULTS: We develop an empirical Bayes approach to improve covariance estimates for gene expression, where we assume the covariance matrix takes a block diagonal form. Our method shows lower false discovery rates than existing methods on simulated data. Applied to a real data set from Bacillus subtilis we demonstrate it’s ability to detecting known regulatory units and interactions between them. CONCLUSIONS: We demonstrate that, compared to existing methods, our method is able to find significant covariances and also to control false discovery rates, even when the sample size is small (n=10). The method can be used to find potential regulatory networks, and it may also be used as a pre-processing step for methods that calculate, for example, partial correlations, so enabling the inference of the causal and hierarchical structure of the networks. |
format | Online Article Text |
id | pubmed-5389176 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-53891762017-04-14 Empirical Bayes method for reducing false discovery rates of correlation matrices with block diagonal structure Pacini, Clare Ajioka, James W. Micklem, Gos BMC Bioinformatics Software BACKGROUND: Correlation matrices are important in inferring relationships and networks between regulatory or signalling elements in biological systems. With currently available technology sample sizes for experiments are typically small, meaning that these correlations can be difficult to estimate. At a genome-wide scale estimation of correlation matrices can also be computationally demanding. RESULTS: We develop an empirical Bayes approach to improve covariance estimates for gene expression, where we assume the covariance matrix takes a block diagonal form. Our method shows lower false discovery rates than existing methods on simulated data. Applied to a real data set from Bacillus subtilis we demonstrate it’s ability to detecting known regulatory units and interactions between them. CONCLUSIONS: We demonstrate that, compared to existing methods, our method is able to find significant covariances and also to control false discovery rates, even when the sample size is small (n=10). The method can be used to find potential regulatory networks, and it may also be used as a pre-processing step for methods that calculate, for example, partial correlations, so enabling the inference of the causal and hierarchical structure of the networks. BioMed Central 2017-04-12 /pmc/articles/PMC5389176/ /pubmed/28403823 http://dx.doi.org/10.1186/s12859-017-1623-y Text en © The Author(s) 2017 Open Access This 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 | Software Pacini, Clare Ajioka, James W. Micklem, Gos Empirical Bayes method for reducing false discovery rates of correlation matrices with block diagonal structure |
title | Empirical Bayes method for reducing false discovery rates of correlation matrices with block diagonal structure |
title_full | Empirical Bayes method for reducing false discovery rates of correlation matrices with block diagonal structure |
title_fullStr | Empirical Bayes method for reducing false discovery rates of correlation matrices with block diagonal structure |
title_full_unstemmed | Empirical Bayes method for reducing false discovery rates of correlation matrices with block diagonal structure |
title_short | Empirical Bayes method for reducing false discovery rates of correlation matrices with block diagonal structure |
title_sort | empirical bayes method for reducing false discovery rates of correlation matrices with block diagonal structure |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5389176/ https://www.ncbi.nlm.nih.gov/pubmed/28403823 http://dx.doi.org/10.1186/s12859-017-1623-y |
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