Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg 85764, Germany.
TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising 85354, Germany.
Nucleic Acids Res. 2018 Nov 16;46(20):e119. doi: 10.1093/nar/gky675.
Temporal changes to the concentration of molecular species such as mRNA, which take place in response to various environmental cues, can often be modeled as simple continuous functions such as a single pulse (impulse) model. The simplicity of such functional representations can provide an improved performance on fundamental tasks such as noise reduction, imputation and differential expression analysis. However, temporal gene expression profiles are often studied with models that treat time as a categorical variable, neglecting the dependence between time points. Here, we present ImpulseDE2, a framework for differential expression analysis that combines the power of the impulse model as a continuous representation of temporal responses along with a noise model tailored specifically to sequencing data. We compare the simple categorical models to ImpulseDE2 and to other continuous models based on natural cubic splines and demonstrate the utility of the continuous approach for studying differential expression in time course sequencing experiments. A unique feature of ImpulseDE2 is the ability to distinguish permanently from transiently up- or down-regulated genes. Using an in vitro differentiation dataset, we demonstrate that this gene classification scheme can be used to highlight distinct transcriptional programs that are associated with different phases of the differentiation process.
时间变化的分子种类的浓度，如 mRNA，这是在响应于各种环境线索，往往可以作为简单的连续函数，如单个脉冲（脉冲）模型建模。这样的功能表示的简单性可以提供改进的性能上的基本任务，如降噪，插补和差异表达分析。然而，时间基因表达谱通常用模型来处理时间作为一个分类变量，忽略了时间点之间的依赖性。在这里，我们提出 ImpulseDE2 ，一个差异表达分析的框架，它结合了脉冲模型作为一个连续的时间响应表示的力量，以及专门为测序数据定制的噪声模型。我们比较了简单的分类模型与 ImpulseDE2 和其他基于自然三次样条的连续模型，并证明了连续方法在研究时间序列测序实验中的差异表达的实用性。ImpulseDE2 的一个独特的特点是能够区分永久性的和瞬时的上调或下调基因。使用体外分化数据集，我们证明了这种基因分类方案可以用来突出与分化过程的不同阶段相关的不同转录程序。
