Albrecht Marco, Stichel Damian, Müller Benedikt, Merkle Ruth, Sticht Carsten, Gretz Norbert, Klingmüller Ursula, Breuhahn Kai, Matthäus Franziska
Complex Biological Systems Group (BIOMS/IWR), Heidelberg, Im Neuenheimer Feld 294, Heidelberg, 69120, Germany.
Systems Biology Group, Université du Luxembourg, 7, avenue du Swing, Belvaux, L-4367, Luxembourg.
BMC Bioinformatics. 2017 Jan 14;18(1):33. doi: 10.1186/s12859-016-1440-8.
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.
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).
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.
微阵列时间序列分析有望更深入地洞察刺激后细胞反应的动态变化。这类数据中的一个常见现象是，一些基因呈现快速、短暂的动态反应，而其他基因的表达则随时间缓慢变化。当表达动态表现出很大的异质性时，现有的检测显著表达动态的方法往往失效。此外，这些方法通常无法处理不规则和稀疏的测量数据。
本文提出的方法专门用于分析扰动反应。它结合了不同的评分来捕捉快速和短暂的动态变化以及缓慢的表达变化，并且在重复次数少和采样时间不规则的情况下表现良好。结果以表格形式给出，其中包括指向显示各个转录本表达动态的图表的链接。这些有助于快速识别检测的相关性，识别可能的假阳性，并区分基因表达的早期和晚期变化。该方法的扩展允许分析基因功能组的表达动态，从而快速概述细胞反应。此软件包的性能在源自用表皮生长因子（EGF）刺激的肺癌细胞的微阵列数据上进行了测试。
在此，我们描述了一种新的、高效的方法，用于分析稀疏和异质的时间进程数据，具有高检测灵敏度和透明度。它作为R软件包TTCA（转录本时间进程分析）实现，可以从综合R存档网络CRAN安装。源代码随附加文件1提供。
