On the Inference of Functional Circadian Networks Using Granger Causality.
On the Inference of Functional Circadian Networks Using Granger Causality.
复制标题
DOI：
10.1371/journal.pone.0137540
复制
发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Petzold LR
中科院分区：
文献类型：
--
作者：
Pourzanjani A;Herzog ED;Petzold LR
能够使用时间序列数据推断振荡网络（如哺乳动物大脑的视交叉上核（SCN））中的单向直接连接是困难的，但对于理解网络动力学至关重要。尽管已经开发了用于从时间序列数据推断网络的技术，但是还没有尝试使这些技术适应于推断振荡时间序列中的方向性连接，同时准确地区分直接连接和间接连接。在本文中，适应格兰杰因果关系的建议，允许在一般称为自适应频率格兰杰因果关系（AFGC）的昼夜节律网络和振荡网络的推断。此外，该方法的扩展，提出了推断网络与大量的细胞称为LASSO AFGC。该方法进行了验证，使用模拟数据从几个不同的网络。对于较小的网络，该方法能够识别所有单向直接连接，而不识别不存在的连接。对于多达20个细胞的较大网络，该方法在识别真假连接方面表现出优异的性能;这通过曲线下面积（AUC）96.88%来量化。我们注意到，该方法与其他基于格兰杰容限的方法一样，是基于对在细胞迹线之间传播的高频信号的检测。因此，它需要相对高的采样率和能够传播高频信号的网络。
Being able to infer one way direct connections in an oscillatory network such as the suprachiastmatic nucleus (SCN) of the mammalian brain using time series data is difficult but crucial to understanding network dynamics. Although techniques have been developed for inferring networks from time series data, there have been no attempts to adapt these techniques to infer directional connections in oscillatory time series, while accurately distinguishing between direct and indirect connections. In this paper an adaptation of Granger Causality is proposed that allows for inference of circadian networks and oscillatory networks in general called Adaptive Frequency Granger Causality (AFGC). Additionally, an extension of this method is proposed to infer networks with large numbers of cells called LASSO AFGC. The method was validated using simulated data from several different networks. For the smaller networks the method was able to identify all one way direct connections without identifying connections that were not present. For larger networks of up to twenty cells the method shows excellent performance in identifying true and false connections; this is quantified by an area-under-the-curve (AUC) 96.88%. We note that this method like other Granger Causality-based methods, is based on the detection of high frequency signals propagating between cell traces. Thus it requires a relatively high sampling rate and a network that can propagate high frequency signals.