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A measure of agreement across numerous conditions: assessing when changes in network structures are tissue-specific

BACKGROUND: There is great interest to study how gene pathways change their structure across different tissues. The assessment of inter-study reliability of pathway changes across tissues can inform on the fraction of tissues with specific functional changes in network structure. However, there is a...

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Autores principales: Cáceres, Alejandro, Gonzalez, Juan R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6327576/
https://www.ncbi.nlm.nih.gov/pubmed/30626339
http://dx.doi.org/10.1186/s12864-018-5340-3
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author Cáceres, Alejandro
Gonzalez, Juan R.
author_facet Cáceres, Alejandro
Gonzalez, Juan R.
author_sort Cáceres, Alejandro
collection PubMed
description BACKGROUND: There is great interest to study how gene pathways change their structure across different tissues. The assessment of inter-study reliability of pathway changes across tissues can inform on the fraction of tissues with specific functional changes in network structure. However, there is a lack of agreement measures among studies that independently observe how a group of observations change across conditions. We, therefore, propose λ, a new inter-study reliability measure that determines the consistency to distinguish observations by condition. RESULTS: We derived λ’s distributional characteristics, determine its reliability properties and compared it with Cohen’s κ. We studied the co-expression structure of 287 gene pathways across four brain regions in two transcriptomic studies and applied λ to assess the inter-study reliability of the pathways’ brain-regional changes. Brain-related pathways showed highest λ; the top value was for the nicotine addiction pathway whose structure was reliably distinguishable among regions with dopaminergic projections. CONCLUSION: Our results offer novel substantial evidence that changes in network structure across tissues can be inferred independently of samples, algorithms and experiments (RNA-sequencing or microarrays). Reliability measures, such as λ, can inform on the tissues where changes in a network’s structure are likely functional. An R package is available at https://github.com/isglobal-brge/lambda. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12864-018-5340-3) contains supplementary material, which is available to authorized users.
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spelling pubmed-63275762019-01-15 A measure of agreement across numerous conditions: assessing when changes in network structures are tissue-specific Cáceres, Alejandro Gonzalez, Juan R. BMC Genomics Methodology Article BACKGROUND: There is great interest to study how gene pathways change their structure across different tissues. The assessment of inter-study reliability of pathway changes across tissues can inform on the fraction of tissues with specific functional changes in network structure. However, there is a lack of agreement measures among studies that independently observe how a group of observations change across conditions. We, therefore, propose λ, a new inter-study reliability measure that determines the consistency to distinguish observations by condition. RESULTS: We derived λ’s distributional characteristics, determine its reliability properties and compared it with Cohen’s κ. We studied the co-expression structure of 287 gene pathways across four brain regions in two transcriptomic studies and applied λ to assess the inter-study reliability of the pathways’ brain-regional changes. Brain-related pathways showed highest λ; the top value was for the nicotine addiction pathway whose structure was reliably distinguishable among regions with dopaminergic projections. CONCLUSION: Our results offer novel substantial evidence that changes in network structure across tissues can be inferred independently of samples, algorithms and experiments (RNA-sequencing or microarrays). Reliability measures, such as λ, can inform on the tissues where changes in a network’s structure are likely functional. An R package is available at https://github.com/isglobal-brge/lambda. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12864-018-5340-3) contains supplementary material, which is available to authorized users. BioMed Central 2019-01-09 /pmc/articles/PMC6327576/ /pubmed/30626339 http://dx.doi.org/10.1186/s12864-018-5340-3 Text en © The Author(s) 2019 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 Methodology Article
Cáceres, Alejandro
Gonzalez, Juan R.
A measure of agreement across numerous conditions: assessing when changes in network structures are tissue-specific
title A measure of agreement across numerous conditions: assessing when changes in network structures are tissue-specific
title_full A measure of agreement across numerous conditions: assessing when changes in network structures are tissue-specific
title_fullStr A measure of agreement across numerous conditions: assessing when changes in network structures are tissue-specific
title_full_unstemmed A measure of agreement across numerous conditions: assessing when changes in network structures are tissue-specific
title_short A measure of agreement across numerous conditions: assessing when changes in network structures are tissue-specific
title_sort measure of agreement across numerous conditions: assessing when changes in network structures are tissue-specific
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6327576/
https://www.ncbi.nlm.nih.gov/pubmed/30626339
http://dx.doi.org/10.1186/s12864-018-5340-3
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