Omranian Nooshin, Mueller-Roeber Bernd, Nikoloski Zoran
1] Department of Molecular Biology, University of Potsdam, Karl-Liebknecht-Str. 24-25, Haus 20, 14476 Potsdam, Germany [2] Systems Biology and Mathematical Modelling Group, Max Planck Institute for Molecular Plant Physiology, Am Muehlenberg 1, 14476 Potsdam, Germany.
Department of Molecular Biology, University of Potsdam, Karl-Liebknecht-Str. 24-25, Haus 20, 14476 Potsdam, Germany.
Sci Rep. 2015 Mar 11;5:8937. doi: 10.1038/srep08937.
Time-series data from multicomponent systems capture the dynamics of the ongoing processes and reflect the interactions between the components. The progression of processes in such systems usually involves check-points and events at which the relationships between the components are altered in response to stimuli. Detecting these events together with the implicated components can help understand the temporal aspects of complex biological systems. Here we propose a regularized regression-based approach for identifying breakpoints and corresponding segments from multivariate time-series data. In combination with techniques from clustering, the approach also allows estimating the significance of the determined breakpoints as well as the key components implicated in the emergence of the breakpoints. Comparative analysis with the existing alternatives demonstrates the power of the approach to identify biologically meaningful breakpoints in diverse time-resolved transcriptomics data sets from the yeast Saccharomyces cerevisiae and the diatom Thalassiosira pseudonana.
来自多组分系统的时间序列数据捕捉了正在进行的过程的动态，并反映了各组分之间的相互作用。此类系统中过程的进展通常涉及检查点和事件，在这些检查点和事件处，各组分之间的关系会根据刺激而改变。检测这些事件以及与之相关的组分有助于理解复杂生物系统的时间特征。在此，我们提出一种基于正则化回归的方法，用于从多变量时间序列数据中识别断点和相应的片段。结合聚类技术，该方法还能够估计所确定断点的显著性以及与断点出现相关的关键组分。与现有替代方法的比较分析表明，该方法能够在来自酿酒酵母和假微型海链藻的不同时间分辨转录组学数据集中识别出具有生物学意义的断点。
