Uncovering interactions in the frequency domain.
Uncovering interactions in the frequency domain.
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DOI：
10.1371/journal.pcbi.1000087
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发表时间：
2008-05-30
影响因子：
4.3
通讯作者：
Feng J
中科院分区：
文献类型：
--
作者：
Guo S;Wu J;Ding M;Feng J
振荡活动在调节从亚细胞、细胞和网络到整个生物体的水平上的生物过程中起着关键作用，并且通常涉及大量相互作用的元件。我们揭示了这个问题，通过引入一种新的方法，称为部分格兰杰因果关系，可靠地揭示在多变量数据与外生输入和频域中的潜变量的相互作用模式。该方法被广泛的测试与玩具模型，并成功地应用于实验数据集，包括（1）基因芯片数据的HeLa细胞周期;（2）在体内多电极阵列（MEA）记录的局部场电位（LFPs）从下颞叶皮层的绵羊;和（3）在体内LFPs记录从分布在右半球的猕猴。当基于同时记录的多变量时间数据预测网络（基因网络、蛋白质网络、代谢网络或神经元网络）的结构时，主要工具是贝叶斯网络或格兰杰因果关系。我们关注的是格兰杰因果关系。近年来，由于在分子生物学（微阵列基因数据）和生理学（多神经元的多电极阵列记录）等领域存在大量可用的时间数据，格兰杰因果关系变得越来越重要。然而，所有估计格兰杰因果关系的方法都倾向于忽略潜变量，而潜变量在实验数据中普遍存在。在这里，我们开发了一种方法，可以消除潜在变量的影响，在预测网络结构。然后将该方法扩展到频域。该方法消除潜在变量影响的能力在玩具模型中得到了广泛的验证，然后应用于基因电路，神经元网络和大脑区域网络。在时域和频域中，我们的方法可以用来检测网络结构时，多维时间数据。
Oscillatory activity plays a critical role in regulating biological processes at levels ranging from subcellular, cellular, and network to the whole organism, and often involves a large number of interacting elements. We shed light on this issue by introducing a novel approach called partial Granger causality to reliably reveal interaction patterns in multivariate data with exogenous inputs and latent variables in the frequency domain. The method is extensively tested with toy models, and successfully applied to experimental datasets, including (1) gene microarray data of HeLa cell cycle; (2) in vivo multi-electrode array (MEA) local field potentials (LFPs) recorded from the inferotemporal cortex of a sheep; and (3) in vivo LFPs recorded from distributed sites in the right hemisphere of a macaque monkey. When predicting the structure of a network (a gene network, a protein network, a metabolic network or a neuronal network) based upon simultaneously recorded multi-variable temporal data, a major tool is either the Bayesian network or the Granger causality. We focused on the Granger causality, and it has become increasingly important in recent years because of the huge body of temporal data available in, for example, molecular biology (microarray gene data) and physiology (multi-electrode array recordings of multi-neurons). However, all methods of estimating the Granger causality tend to ignore latent variables, which are ubiquitous in experimental data. Here, we have developed a method that can eliminate the influence of latent variables in predicting the network structure. The method is then extended to the frequency domain. The ability of the method to eliminate the influence of latent variables is extensively verified in toy models and then applied to a gene circuit, a neuronal network, and a network of brain areas. Both in the time and frequency domains, our approach can be used to detect a network structure when multi-dimensional temporal data are available.
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DOI：
10.1098/rstb.2005.1641
发表时间：
2005-05-29
影响因子：
6.3
作者：
Eichler, M
通讯作者：
Eichler, M
影响因子：
3.7
作者：
GEWEKE, JF
通讯作者：
GEWEKE, JF
影响因子：
16.2
作者：
Kreiman, G;Hung, CP;DiCarlo, JJ
通讯作者：
DiCarlo, JJ
DOI：
10.1073/pnas.96.16.9379
发表时间：
1999-08-03
影响因子：
11.1
作者：
Ishai, A;Ungerleider, LG;Haxby, JV
通讯作者：
Haxby, JV
影响因子：
56.9
作者：
Nelson, DE;Ihekwaba, AEC;White, MRH
通讯作者：
White, MRH