Graduate Group in Biostatistics, University of California, Davis, CA 95616, USA.
Department of Chemical Engineering, University of California, Davis, CA 95616, USA.
Int J Mol Sci. 2022 Jul 10;23(14):7628. doi: 10.3390/ijms23147628.
Making statistical inference on quantities defining various characteristics of a temporally measured biochemical process and analyzing its variability across different experimental conditions is a core challenge in various branches of science. This problem is particularly difficult when the amount of data that can be collected is limited in terms of both the number of replicates and the number of time points per process trajectory. We propose a method for analyzing the variability of smooth functionals of the growth or production trajectories associated with such processes across different experimental conditions. Our modeling approach is based on a spline representation of the mean trajectories. We also develop a bootstrap-based inference procedure for the parameters while accounting for possible multiple comparisons. This methodology is applied to study two types of quantities-the "time to harvest" and "maximal productivity"-in the context of an experiment on the production of recombinant proteins. We complement the findings with extensive numerical experiments comparing the effectiveness of different types of bootstrap procedures for various tests of hypotheses. These numerical experiments convincingly demonstrate that the proposed method yields reliable inference on complex characteristics of the processes even in a data-limited environment where more traditional methods for statistical inference are typically not reliable.
对随时间变化的生化过程的各种特征进行定义的数量进行统计推断，并分析其在不同实验条件下的变异性，这是各个科学分支的核心挑战。当可收集的数据量在重复次数和每个过程轨迹的时间点数量方面都受到限制时，这个问题尤其困难。我们提出了一种分析与这些过程相关的生长或生产轨迹的平滑函数的变异性的方法，这些轨迹在不同的实验条件下。我们的建模方法基于平均轨迹的样条表示。我们还开发了一种基于引导的参数推断程序，同时考虑了可能的多次比较。该方法应用于研究两种类型的数量 - “收获时间”和“最大生产力” - 在重组蛋白生产实验的背景下。我们用大量的数值实验补充了发现结果，这些实验比较了不同类型的引导程序在各种假设检验中的有效性。这些数值实验令人信服地证明，即使在数据有限的环境中，更传统的统计推断方法通常不可靠的情况下，该方法也可以对过程的复杂特征进行可靠的推断。
