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Assessing biological and technical variation in destructively measured data

机译：评估破坏性测量数据的生物和技术变化

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摘要

The majority of experimental data are obtained by destructive measuring techniques. Inevitably, in all these data variation is present, sometimes small and negligible, sometimes large, preventing proper analysis and extraction of meaningful information by traditional statistical techniques altogether. In this paper, three systems are presented to analyse destructive (cross-sectional) data, including biological as well as technical variation. The first system involves ranking the data per measuring point in time which provides a pseudo fruit number that can be used in non-linear indexed regression analysis similar as for non-destructive (longitudinal) data. The rationale behind this is that the individual with the highest value at some point in time will resemble the most another individual with the highest value at previous or future times, and the second highest the second highest at previous times, and so on. The second system also relies on this ranking number, but is now converted into a probability, which is used in non-linear regression analysis with quantile functions. The third system is based on optimising the log likelihood of the density function derived from the applied model (i.e., the expected distribution) over the measured data. Simulated data are used to elucidate the power of the three systems. A dataset on mango colour is used to validate the systems on a real-world data set. Although all three systems perform satisfactorily with percentages variability accounted for (R-adj(2)) well over 90%, a clear preference cannot be given since the choice of the proper analysis system depends on the experimental conditions (number of data, individuals and sampling points in time). Non-linear indexed and non-linear regression with quantile functions delivered the most reliable estimates. The three systems open up the possibility to analyse and reanalyse destructively measured data providing a sufficient large number of individuals and a clear indication of the kinetic model is available.
机译：大多数实验数据是通过破坏性测量技术获得的。不可避免地，在所有这些数据变化中存在，有时小而忽略不计，有时大，通过传统的统计技术完全通过传统的统计技术进行适当的分析和提取有意义的信息。在本文中，提出了三个系统来分析破坏性（横截面）数据，包括生物学以及技术变异。第一系统涉及每次测量点的数据排列数据，该数据提供了可用于非线性索引的回归分析的伪果实数，类似于非破坏性（纵向）数据。这背后的理由是，在某个时间点的最高值的个人将类似于以前或未来时间最高的另一个人，并且在前次的第二个最高最高，等等。第二个系统还依赖于该排名，但现在转换为概率，该概率用于使用定量函数的非线性回归分析。第三系统基于优化从所测量的数据源自所施加的模型（即，预期分布）的浓度函数的日志似况。模拟数据用于阐明三个系统的功率。 Mango颜色的数据集用于验证真实数据集的系统。尽管所有三种系统令人满意地表现出百分比（R-ADJ（2））差异超过90％，但由于正确分析系统的选择取决于实验条件（数据数量，个人和数据数量，因此不能给出清晰的偏好采样点及时）。使用量级函数的非线性索引和非线性回归提供了最可靠的估计。这三个系统开辟了分析和重新达到破坏性测量数据的可能性，提供足够大量的数据，并且可以清楚地指示动力学模型。

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