eprintid: 22488
rev_number: 15
eprint_status: archive
userid: 1589
dir: disk0/00/02/24/88
datestamp: 2017-01-16 10:41:52
lastmod: 2024-02-29 21:42:37
status_changed: 2017-01-16 10:41:52
type: article
metadata_visibility: show
creators_name: Albrecht, Marco
creators_name: Stichel, Damian
creators_name: Müller, Benedikt
creators_name: Merkle, Ruth
creators_name: Sticht, Carsten
creators_name: Gretz, Norbert
creators_name: Klingmüller, Ursula
creators_name: Breuhahn, Kai
creators_name: Matthäus, Franziska
title: TTCA: an R package for the identification of differentially expressed genes in time course microarray data
subjects: 004
subjects: 570
divisions: 61002
divisions: 708000
divisions: 850300
divisions: 912000
abstract: 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.
date: 2017
publisher: BioMed Central; Springer
id_scheme: DOI
ppn_swb: 1655723227
own_urn: urn:nbn:de:bsz:16-heidok-224886
language: eng
bibsort: ALBRECHTMATTCAANRPAC2017
full_text_status: public
publication: BMC Bioinformatics
volume: 18
number: 33
place_of_pub: London; Berlin; Heidelberg
pagerange: 1-11
issn: 1471-2105
citation:   Albrecht, Marco ; Stichel, Damian ; Müller, Benedikt ; Merkle, Ruth ; Sticht, Carsten ; Gretz, Norbert ; Klingmüller, Ursula ; Breuhahn, Kai ; Matthäus, Franziska  (2017) TTCA: an R package for the identification of differentially expressed genes in time course microarray data.  BMC Bioinformatics, 18 (33).  pp. 1-11.  ISSN 1471-2105     
document_url: https://archiv.ub.uni-heidelberg.de/volltextserver/22488/1/12859_2016_Article_1440.pdf