A Markovian Entropy Measure for the Analysis of Calcium Activity Time Series.
A Markovian Entropy Measure for the Analysis of Calcium Activity Time Series.
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DOI：
10.1371/journal.pone.0168342
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Saha MS
中科院分区：
文献类型：
--
作者：
Marken JP;Halleran AD;Rahman A;Odorizzi L;LeFew MC;Golino CA;Kemper P;Saha MS
分析钙活性动态的方法通常依赖于时间序列数据中视觉上可区分的特征，例如尖峰、波或振荡。然而，诸如发育中的神经系统之类的系统表现出复杂、不规则的钙活性，这使得使用此类方法不太合适。相反，对于此类系统，存在一类方法（包括信息论、功率谱和分形分析方法），它们使用时间序列的更基本属性来分析观察到的钙动力学。我们在此类中提出了一种新的分析方法，即马尔可夫熵度量，这是一种易于实现的钙时间序列分析方法，它将观察到的钙活动表示为马尔可夫过程的实现，并根据过程状态之间转换的可预测性水平描述其动态。我们将我们的和其他常用的钙分析方法应用于非洲爪蟾神经祖细胞的数据集，该数据集显示不规则的钙活动，以及来自小鼠突触神经元的数据集，该数据集显示通过视觉可区分的特征很好地描述的活动时间序列。我们发现马尔可夫熵度量能够区分两个数据集中的生物学不同群体，并且与表现出不规则钙活动的数据集中的其他方法相比，它可以在更大程度上分离生物学不同的群体。这些结果支持使用马尔可夫熵测量来分析钙动力学的好处，特别是对于使用不易区分特征的时间序列数据的研究。
Methods to analyze the dynamics of calcium activity often rely on visually distinguishable features in time series data such as spikes, waves, or oscillations. However, systems such as the developing nervous system display a complex, irregular type of calcium activity which makes the use of such methods less appropriate. Instead, for such systems there exists a class of methods (including information theoretic, power spectral, and fractal analysis approaches) which use more fundamental properties of the time series to analyze the observed calcium dynamics. We present a new analysis method in this class, the Markovian Entropy measure, which is an easily implementable calcium time series analysis method which represents the observed calcium activity as a realization of a Markov Process and describes its dynamics in terms of the level of predictability underlying the transitions between the states of the process. We applied our and other commonly used calcium analysis methods on a dataset from Xenopus laevis neural progenitors which displays irregular calcium activity and a dataset from murine synaptic neurons which displays activity time series that are well-described by visually-distinguishable features. We find that the Markovian Entropy measure is able to distinguish between biologically distinct populations in both datasets, and that it can separate biologically distinct populations to a greater extent than other methods in the dataset exhibiting irregular calcium activity. These results support the benefit of using the Markovian Entropy measure to analyze calcium dynamics, particularly for studies using time series data which do not exhibit easily distinguishable features.
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