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

BMC Bioinformatics

Methodology Article

Damian Stichel¹  Franziska Matthäus²  Marco Albrecht³  Ruth Merkle⁴  Ursula Klingmüller⁴  Benedikt Müller⁵  Kai Breuhahn⁵  Norbert Gretz⁶  Carsten Sticht⁶ 

[1] Complex Biological Systems Group (BIOMS/IWR), Heidelberg, Im Neuenheimer Feld 294, 69120, Heidelberg, Germany;CCU Neuropathology Group, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 221, 69120, Heidelberg, Germany;Complex Biological Systems Group (BIOMS/IWR), Heidelberg, Im Neuenheimer Feld 294, 69120, Heidelberg, Germany;Frankfurt Institute for Advanced Studies (FIAS), Goethe University Frankfurt, Ruth-Moufang-Straße 1, 60438, Frankfurt am Main, Germany;Complex Biological Systems Group (BIOMS/IWR), Heidelberg, Im Neuenheimer Feld 294, 69120, Heidelberg, Germany;Systems Biology Group, Université du Luxembourg, 7, avenue du Swing, L-4367, Belvaux, Luxembourg;German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany;Translational Lung Research Center (TLRC), Member of the German Center for Lung Research (DZL), Im Neuenheimer Feld 430, 69120, Heidelberg, Germany;Institute of Pathology, Heidelberg University Hospital, Im Neuenheimer Feld 672, 69120, Heidelberg, Germany;Medical Research Center, Medical Faculty Mannheim, University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany;

关键词: Differential expression;    Time series;    EGF;    Stimulation experiments;    Gene ontology;    Gene set analysis;   

DOI  :  10.1186/s12859-016-1440-8

 received in 2016-07-08, accepted in 2016-12-21,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundThe 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.ResultsThe 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).ConclusionHere 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.

【 授权许可】

CC BY   
© The Author(s) 2017

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