TimeCycle: Topology Inspired MEthod for the Detection of Cycling Transcripts in Circadian Time-Series Data
TimeCycle: Topology Inspired MEthod for the Detection of Cycling Transcripts in Circadian Time-Series Data
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TimeCycle：拓扑启发的方法，用于检测昼夜节律时间序列数据中的循环转录本
DOI：
10.1101/2020.11.19.389981
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Braun, R
中科院分区：
文献类型：
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作者：
Ness-Cohn, E;Braun, R
昼夜节律驱动所有组织中成千上万个基因的振荡表达。最近高通量转录组学的革命，加上生物钟对人类健康的重大影响，激发了人们对昼夜节律谱研究的兴趣，以发现受昼夜节律控制的基因。结果我们提出了一种基于拓扑的节律检测方法TimeCycle，用于识别循环转录本。对于给定的时间序列，该方法利用动态系统理论中的一种数据转换技术——时延嵌入来重构状态空间。在嵌入空间中，Takens定理证明了节奏信号的动态将呈现圆形模式。嵌入的圆度使用持久同调（一种识别数据拓扑特征的代数方法）作为持久性分数计算。通过将持久性分数与自举零分布进行比较，确定了循环基因。合成和生物数据的结果都突出了TimeCycle在一系列采样方案、重复数量和缺失数据中识别循环基因的能力。通过对竞争方法的比较，突出了各自的优势，为循环检测方法的优化选择提供了指导。可用性和实现实现时间循环的完整文档的开源R包可在：https://nesscoder.github.io/TimeCycle/.Supplementary information补充数据可在bioinformaticsonline上获得。
MotivationThe circadian rhythm drives the oscillatory expression of thousands of genes across all tissues. The recent revolution in high-throughput transcriptomics, coupled with the significant implications of the circadian clock for human health, has sparked an interest in circadian profiling studies to discover genes under circadian control.ResultWe present TimeCycle: a topology-based rhythm detection method designed to identify cycling transcripts. For a given time-series, the method reconstructs the state space using time-delay embedding, a data transformation technique from dynamical systems theory. In the embedded space, Takens’ theorem proves that the dynamics of a rhythmic signal will exhibit circular patterns. The degree of circularity of the embedding is calculated as a persistence score using persistent homology, an algebraic method for discerning the topological features of data. By comparing the persistence scores to a bootstrapped null distribution, cycling genes are identified. Results in both synthetic and biological data highlight TimeCycle’s ability to identify cycling genes across a range of sampling schemes, number of replicates and missing data. Comparison to competing methods highlights their relative strengths, providing guidance as to the optimal choice of cycling detection method.Availabilityand implementationA fully documented open-source R package implementing TimeCycle is available at: https://nesscoder.github.io/TimeCycle/.Supplementary informationSupplementary data are available atBioinformaticsonline.