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TTCA: an R package for the identification of differentially expressed genes in time course microarray data

BACKGROUND: The analysis of microarray time series promises a deeper insight into the dynamics of the cellular response following stimulation. A common observation in this type of data is that some genes respond with quick, transient dynamics, while other genes change their expression slowly over ti...

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Autores principales: Albrecht, Marco, Stichel, Damian, Müller, Benedikt, Merkle, Ruth, Sticht, Carsten, Gretz, Norbert, Klingmüller, Ursula, Breuhahn, Kai, Matthäus, Franziska
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5237546/
https://www.ncbi.nlm.nih.gov/pubmed/28088176
http://dx.doi.org/10.1186/s12859-016-1440-8
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author Albrecht, Marco
Stichel, Damian
Müller, Benedikt
Merkle, Ruth
Sticht, Carsten
Gretz, Norbert
Klingmüller, Ursula
Breuhahn, Kai
Matthäus, Franziska
author_facet Albrecht, Marco
Stichel, Damian
Müller, Benedikt
Merkle, Ruth
Sticht, Carsten
Gretz, Norbert
Klingmüller, Ursula
Breuhahn, Kai
Matthäus, Franziska
author_sort Albrecht, Marco
collection PubMed
description BACKGROUND: The analysis of microarray time series promises a deeper insight into the dynamics of the cellular response following stimulation. A common observation in this type of data is that some genes respond with quick, transient dynamics, while other genes change their expression slowly over time. The existing methods for detecting significant expression dynamics often fail when the expression dynamics show a large heterogeneity. Moreover, these methods often cannot cope with irregular and sparse measurements. RESULTS: The method proposed here is specifically designed for the analysis of perturbation responses. It combines different scores to capture fast and transient dynamics as well as slow expression changes, and performs well in the presence of low replicate numbers and irregular sampling times. The results are given in the form of tables including links to figures showing the expression dynamics of the respective transcript. These allow to quickly recognise the relevance of detection, to identify possible false positives and to discriminate early and late changes in gene expression. An extension of the method allows the analysis of the expression dynamics of functional groups of genes, providing a quick overview of the cellular response. The performance of this package was tested on microarray data derived from lung cancer cells stimulated with epidermal growth factor (EGF). CONCLUSION: Here we describe a new, efficient method for the analysis of sparse and heterogeneous time course data with high detection sensitivity and transparency. It is implemented as R package TTCA (transcript time course analysis) and can be installed from the Comprehensive R Archive Network, CRAN. The source code is provided with the Additional file 1. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1440-8) contains supplementary material, which is available to authorized users.
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spelling pubmed-52375462017-01-18 TTCA: an R package for the identification of differentially expressed genes in time course microarray data Albrecht, Marco Stichel, Damian Müller, Benedikt Merkle, Ruth Sticht, Carsten Gretz, Norbert Klingmüller, Ursula Breuhahn, Kai Matthäus, Franziska BMC Bioinformatics Methodology Article BACKGROUND: The analysis of microarray time series promises a deeper insight into the dynamics of the cellular response following stimulation. A common observation in this type of data is that some genes respond with quick, transient dynamics, while other genes change their expression slowly over time. The existing methods for detecting significant expression dynamics often fail when the expression dynamics show a large heterogeneity. Moreover, these methods often cannot cope with irregular and sparse measurements. RESULTS: The method proposed here is specifically designed for the analysis of perturbation responses. It combines different scores to capture fast and transient dynamics as well as slow expression changes, and performs well in the presence of low replicate numbers and irregular sampling times. The results are given in the form of tables including links to figures showing the expression dynamics of the respective transcript. These allow to quickly recognise the relevance of detection, to identify possible false positives and to discriminate early and late changes in gene expression. An extension of the method allows the analysis of the expression dynamics of functional groups of genes, providing a quick overview of the cellular response. The performance of this package was tested on microarray data derived from lung cancer cells stimulated with epidermal growth factor (EGF). CONCLUSION: Here we describe a new, efficient method for the analysis of sparse and heterogeneous time course data with high detection sensitivity and transparency. It is implemented as R package TTCA (transcript time course analysis) and can be installed from the Comprehensive R Archive Network, CRAN. The source code is provided with the Additional file 1. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1440-8) contains supplementary material, which is available to authorized users. BioMed Central 2017-01-14 /pmc/articles/PMC5237546/ /pubmed/28088176 http://dx.doi.org/10.1186/s12859-016-1440-8 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 Methodology Article
Albrecht, Marco
Stichel, Damian
Müller, Benedikt
Merkle, Ruth
Sticht, Carsten
Gretz, Norbert
Klingmüller, Ursula
Breuhahn, Kai
Matthäus, Franziska
TTCA: an R package for the identification of differentially expressed genes in time course microarray data
title TTCA: an R package for the identification of differentially expressed genes in time course microarray data
title_full TTCA: an R package for the identification of differentially expressed genes in time course microarray data
title_fullStr TTCA: an R package for the identification of differentially expressed genes in time course microarray data
title_full_unstemmed TTCA: an R package for the identification of differentially expressed genes in time course microarray data
title_short TTCA: an R package for the identification of differentially expressed genes in time course microarray data
title_sort ttca: an r package for the identification of differentially expressed genes in time course microarray data
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5237546/
https://www.ncbi.nlm.nih.gov/pubmed/28088176
http://dx.doi.org/10.1186/s12859-016-1440-8
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