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Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data

Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the hetero...

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Autores principales: Tan, Qihua, Thomassen, Mads, Burton, Mark, Mose, Kristian Fredløv, Andersen, Klaus Ejner, Hjelmborg, Jacob, Kruse, Torben
Formato: Online Artículo Texto
Lenguaje:English
Publicado: De Gruyter 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6042830/
https://www.ncbi.nlm.nih.gov/pubmed/28753536
http://dx.doi.org/10.1515/jib-2017-0011
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author Tan, Qihua
Thomassen, Mads
Burton, Mark
Mose, Kristian Fredløv
Andersen, Klaus Ejner
Hjelmborg, Jacob
Kruse, Torben
author_facet Tan, Qihua
Thomassen, Mads
Burton, Mark
Mose, Kristian Fredløv
Andersen, Klaus Ejner
Hjelmborg, Jacob
Kruse, Torben
author_sort Tan, Qihua
collection PubMed
description Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.
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spelling pubmed-60428302019-01-28 Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data Tan, Qihua Thomassen, Mads Burton, Mark Mose, Kristian Fredløv Andersen, Klaus Ejner Hjelmborg, Jacob Kruse, Torben J Integr Bioinform Research Articles Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health. De Gruyter 2017-06-06 /pmc/articles/PMC6042830/ /pubmed/28753536 http://dx.doi.org/10.1515/jib-2017-0011 Text en ©2017, Qihua Tan, published by De Gruyter, Berlin/Boston http://creativecommons.org/licenses/by-nc-nd/3.0 This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.
spellingShingle Research Articles
Tan, Qihua
Thomassen, Mads
Burton, Mark
Mose, Kristian Fredløv
Andersen, Klaus Ejner
Hjelmborg, Jacob
Kruse, Torben
Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data
title Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data
title_full Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data
title_fullStr Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data
title_full_unstemmed Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data
title_short Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data
title_sort generalized correlation coefficient for non-parametric analysis of microarray time-course data
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6042830/
https://www.ncbi.nlm.nih.gov/pubmed/28753536
http://dx.doi.org/10.1515/jib-2017-0011
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