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SABRE: a method for assessing the stability of gene modules in complex tissues and subject populations

BACKGROUND: Gene network inference (GNI) algorithms can be used to identify sets of coordinately expressed genes, termed network modules from whole transcriptome gene expression data. The identification of such modules has become a popular approach to systems biology, with important applications in...

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Autores principales: Shannon, Casey P., Chen, Virginia, Takhar, Mandeep, Hollander, Zsuzsanna, Balshaw, Robert, McManus, Bruce M., Tebbutt, Scott J., Sin, Don D., Ng, Raymond T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109843/
https://www.ncbi.nlm.nih.gov/pubmed/27842512
http://dx.doi.org/10.1186/s12859-016-1319-8
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author Shannon, Casey P.
Chen, Virginia
Takhar, Mandeep
Hollander, Zsuzsanna
Balshaw, Robert
McManus, Bruce M.
Tebbutt, Scott J.
Sin, Don D.
Ng, Raymond T.
author_facet Shannon, Casey P.
Chen, Virginia
Takhar, Mandeep
Hollander, Zsuzsanna
Balshaw, Robert
McManus, Bruce M.
Tebbutt, Scott J.
Sin, Don D.
Ng, Raymond T.
author_sort Shannon, Casey P.
collection PubMed
description BACKGROUND: Gene network inference (GNI) algorithms can be used to identify sets of coordinately expressed genes, termed network modules from whole transcriptome gene expression data. The identification of such modules has become a popular approach to systems biology, with important applications in translational research. Although diverse computational and statistical approaches have been devised to identify such modules, their performance behavior is still not fully understood, particularly in complex human tissues. Given human heterogeneity, one important question is how the outputs of these computational methods are sensitive to the input sample set, or stability. A related question is how this sensitivity depends on the size of the sample set. We describe here the SABRE (Similarity Across Bootstrap RE-sampling) procedure for assessing the stability of gene network modules using a re-sampling strategy, introduce a novel criterion for identifying stable modules, and demonstrate the utility of this approach in a clinically-relevant cohort, using two different gene network module discovery algorithms. RESULTS: The stability of modules increased as sample size increased and stable modules were more likely to be replicated in larger sets of samples. Random modules derived from permutated gene expression data were consistently unstable, as assessed by SABRE, and provide a useful baseline value for our proposed stability criterion. Gene module sets identified by different algorithms varied with respect to their stability, as assessed by SABRE. Finally, stable modules were more readily annotated in various curated gene set databases. CONCLUSIONS: The SABRE procedure and proposed stability criterion may provide guidance when designing systems biology studies in complex human disease and tissues. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1319-8) contains supplementary material, which is available to authorized users.
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spelling pubmed-51098432016-11-21 SABRE: a method for assessing the stability of gene modules in complex tissues and subject populations Shannon, Casey P. Chen, Virginia Takhar, Mandeep Hollander, Zsuzsanna Balshaw, Robert McManus, Bruce M. Tebbutt, Scott J. Sin, Don D. Ng, Raymond T. BMC Bioinformatics Methodology Article BACKGROUND: Gene network inference (GNI) algorithms can be used to identify sets of coordinately expressed genes, termed network modules from whole transcriptome gene expression data. The identification of such modules has become a popular approach to systems biology, with important applications in translational research. Although diverse computational and statistical approaches have been devised to identify such modules, their performance behavior is still not fully understood, particularly in complex human tissues. Given human heterogeneity, one important question is how the outputs of these computational methods are sensitive to the input sample set, or stability. A related question is how this sensitivity depends on the size of the sample set. We describe here the SABRE (Similarity Across Bootstrap RE-sampling) procedure for assessing the stability of gene network modules using a re-sampling strategy, introduce a novel criterion for identifying stable modules, and demonstrate the utility of this approach in a clinically-relevant cohort, using two different gene network module discovery algorithms. RESULTS: The stability of modules increased as sample size increased and stable modules were more likely to be replicated in larger sets of samples. Random modules derived from permutated gene expression data were consistently unstable, as assessed by SABRE, and provide a useful baseline value for our proposed stability criterion. Gene module sets identified by different algorithms varied with respect to their stability, as assessed by SABRE. Finally, stable modules were more readily annotated in various curated gene set databases. CONCLUSIONS: The SABRE procedure and proposed stability criterion may provide guidance when designing systems biology studies in complex human disease and tissues. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1319-8) contains supplementary material, which is available to authorized users. BioMed Central 2016-11-14 /pmc/articles/PMC5109843/ /pubmed/27842512 http://dx.doi.org/10.1186/s12859-016-1319-8 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 Methodology Article
Shannon, Casey P.
Chen, Virginia
Takhar, Mandeep
Hollander, Zsuzsanna
Balshaw, Robert
McManus, Bruce M.
Tebbutt, Scott J.
Sin, Don D.
Ng, Raymond T.
SABRE: a method for assessing the stability of gene modules in complex tissues and subject populations
title SABRE: a method for assessing the stability of gene modules in complex tissues and subject populations
title_full SABRE: a method for assessing the stability of gene modules in complex tissues and subject populations
title_fullStr SABRE: a method for assessing the stability of gene modules in complex tissues and subject populations
title_full_unstemmed SABRE: a method for assessing the stability of gene modules in complex tissues and subject populations
title_short SABRE: a method for assessing the stability of gene modules in complex tissues and subject populations
title_sort sabre: a method for assessing the stability of gene modules in complex tissues and subject populations
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109843/
https://www.ncbi.nlm.nih.gov/pubmed/27842512
http://dx.doi.org/10.1186/s12859-016-1319-8
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