Johnson Michael L, Pipes Lenore, Veldhuis Paula P, Farhy Leon S, Nass Ralf, Thorner Michael O, Evans William S
Department of Pharmacology, University of Virginia Health System, Charlottesville, VA, USA.
Methods Enzymol. 2009;454:367-404. doi: 10.1016/S0076-6879(08)03815-9.
This work presents a new approach to the analysis of aperiodic pulsatile heteroscedastic time-series data, specifically hormone pulsatility. We have utilized growth hormone (GH) concentration time-series data as an example for the utilization of this new algorithm. While many previously published approaches used for the analysis of GH pulsatility are both subjective and cumbersome to use, AutoDecon is a nonsubjective, standardized, and completely automated algorithm. We have employed computer simulations to evaluate the true-positive, the false-positive, the false-negative, and the sensitivity percentages of several of the routinely employed algorithms when applied to GH concentration time-series data. Based on these simulations, it was concluded that this new algorithm provides a substantial improvement over the previous methods. This novel method has many direct applications in addition to hormone pulsatility, for example, to time-domain fluorescence lifetime measurements, as the mathematical forms that describe these experimental systems are both convolution integrals.
这项工作提出了一种分析非周期性脉动异方差时间序列数据(特别是激素脉动)的新方法。我们以生长激素(GH)浓度时间序列数据为例,来说明这种新算法的应用。虽然许多先前发表的用于分析GH脉动的方法既主观又使用不便,但自动去卷积算法(AutoDecon)是一种非主观、标准化且完全自动化的算法。我们采用计算机模拟来评估几种常规使用的算法应用于GH浓度时间序列数据时的真阳性、假阳性、假阴性和灵敏度百分比。基于这些模拟得出的结论是,这种新算法比以前的方法有显著改进。这种新方法除了在激素脉动方面有许多直接应用外,例如在时域荧光寿命测量中也有应用,因为描述这些实验系统的数学形式都是卷积积分。