TTCA: an R package for the identification of differentially expressed genes in time course microarray data
| العنوان: | TTCA: an R package for the identification of differentially expressed genes in time course microarray data |
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| المؤلفون: | Ursula Klingmüller, Damian Stichel, Benedikt Müller, Kai Breuhahn, Franziska Matthäus, Norbert Gretz, Ruth Merkle, Marco Albrecht, Carsten Sticht |
| المصدر: | BMC Bioinformatics BMC Bioinformatics, 18(1), 33. BioMed Central (2017). |
| بيانات النشر: | BioMed Central; Springer, 2017. |
| سنة النشر: | 2017 |
| مصطلحات موضوعية: | 0301 basic medicine, Time series, Microarray, Computer science, Gene Expression, Computational biology, computer.software_genre, Biochemistry, 570 Life sciences, 03 medical and health sciences, Differential expression, Structural Biology, Stimulation experiments, Gene expression, Humans, ddc:610, Molecular Biology, Gene, 004 Data processing Computer science, Oligonucleotide Array Sequence Analysis, EGF, Microarray analysis techniques, Gene Expression Profiling, Methodology Article, Applied Mathematics, Computational Biology, Computer Science Applications, Gene set analysis, 030104 developmental biology, Gene ontology, Data mining, Genetics & genetic processes [F10] [Life sciences], DNA microarray, Génétique & processus génétiques [F10] [Sciences du vivant], computer |
| الوصف: | 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. |
| وصف الملف: | application/pdf; application/octet-stream |
| اللغة: | English |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0c3090df1a651a770f479c9e7780917c http://archiv.ub.uni-heidelberg.de/volltextserver/22488/ |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....0c3090df1a651a770f479c9e7780917c |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |