Bayesian statistical analysis of circadian oscillations in fibroblasts
Bayesian statistical analysis of circadian oscillations in fibroblasts
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DOI：
10.1016/j.jtbi.2012.08.038
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发表时间：
2012-12-07
影响因子：
2
通讯作者：
Welsh, David K.
中科院分区：
文献类型：
--
作者：
Cohen, Andrew L.;Leise, Tanya L.;Welsh, David K.
从有限的实验数据中精确确定有噪声的生物振荡器的周期可能是具有挑战性的。通常的做法是计算一个特定时间过程的单个数字（点估计）。不确定性是应用于噪声数据的任何统计估计所固有的，因此我们对这种点估计的信心取决于数据的质量和数量。理想情况下，周期估计方法应该既产生对周期的准确点估计，又测量该点估计的不确定性。已知各种周期估计方法，但很少评估估计的不确定性，并且在实验文献中很少报道不确定性的测量。我们使用六种常见的方法比较点估计的准确性，其中只有一种方法也可以产生不确定性措施。然后，我们说明了一个新的贝叶斯方法估计周期，它优于其他六种方法的模拟数据的点估计的准确性，也提供了一个衡量的不确定性的优势。我们应用这种方法来分析在个别小鼠成纤维细胞的基因表达的昼夜节律振荡，并计算所需的细胞数量和采样时间，以减少周期估计的不确定性到一个理想的水平。该分析表明，由于有噪声的细胞内振荡器的随机可变性，实现窄的误差范围可能需要不切实际的大量细胞。此外，我们使用分层模型来确定内在细胞周期的分布，从而将每个细胞内随机基因表达引起的变异性与整个细胞群体的周期变异性分开。(C)2012爱思唯尔有限公司版权所有。
Precise determination of a noisy biological oscillator's period from limited experimental data can be challenging. The common practice is to calculate a single number (a point estimate) for the period of a particular time course. Uncertainty is inherent in any statistical estimator applied to noisy data, so our confidence in such point estimates depends on the quality and quantity of the data. Ideally, a period estimation method should both produce an accurate point estimate of the period and measure the uncertainty in that point estimate. A variety of period estimation methods are known, but few assess the uncertainty of the estimates, and a measure of uncertainty is rarely reported in the experimental literature. We compare the accuracy of point estimates using six common methods, only one of which can also produce uncertainty measures. We then illustrate the advantages of a new Bayesian method for estimating period, which outperforms the other six methods in accuracy of point estimates for simulated data and also provides a measure of uncertainty. We apply this method to analyze circadian oscillations of gene expression in individual mouse fibroblast cells and compute the number of cells and sampling duration required to reduce the uncertainty in period estimates to a desired level. This analysis indicates that, due to the stochastic variability of noisy intracellular oscillators, achieving a narrow margin of error can require an impractically large number of cells. In addition, we use a hierarchical model to determine the distribution of intrinsic cell periods, thereby separating the variability due to stochastic gene expression within each cell from the variability in period across the population of cells. (C) 2012 Elsevier Ltd. All rights reserved.