Article (Scientific journals)
TTCA: an R package for the identification of differentially expressed genes in time course microarray data
Albrecht, Marco; Stichel, Damian; Müller, Benedikt et al.
2017In BMC Bioinformatics, 18 (1), p. 33
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Abstract :
[en] 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.
Disciplines :
Genetics & genetic processes
Author, co-author :
Albrecht, Marco ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit ; Universität Heidelberg > Institut für wissenschaftliches Rechnen
Stichel, Damian
Müller, Benedikt
Merkle, Ruth
Sticht, Carsten
Gretz, Norbert
Klingmüller, Ursula
Breuhahn, Kai
Matthäus, Franziska
External co-authors :
Language :
Title :
TTCA: an R package for the identification of differentially expressed genes in time course microarray data
Publication date :
14 January 2017
Journal title :
BMC Bioinformatics
Publisher :
BioMed Central
Volume :
Issue :
Pages :
Peer reviewed :
Peer Reviewed verified by ORBi
Focus Area :
Systems Biomedicine
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since 31 January 2017


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