TY  - JOUR
A1  - Albrecht, Marco
A1  - Stichel, Damian
A1  - Müller, Benedikt
A1  - Merkle, Ruth
A1  - Sticht, Carsten
A1  - Gretz, Norbert
A1  - Klingmüller, Ursula
A1  - Breuhahn, Kai
A1  - Matthäus, Franziska
T1  - TTCA: an R package for the identification of differentially expressed genes in time course microarray data
T2  - BMC Bioinformatics
N2  - 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.
KW  - Differential expression
KW  - Time series
KW  - EGF
KW  - Stimulation experiments
KW  - Gene ontology
KW  - Gene set analysis
Y1  - 2017
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/41445
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-414454
N1  - © 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.
VL  - 18
IS  - 33
SP  - 1
EP  - 13
ER  -