Functional Modeling of Plant Growth Dynamics
Functional Modeling of Plant Growth Dynamics
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DOI：
10.2135/tppj2017.09.0007
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发表时间：
2017-09
期刊：
影响因子：
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通讯作者：
Yuhang Xu;Yumou Qiu;James c. Schnable
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文献类型：
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作者：
Yuhang Xu;Yumou Qiu;James c. Schnable
自动化植物表型分析的最新进展使得能够在不同的发育时间尺度上从相同的植物收集广泛的性状的时间序列测量。时间序列表型数据集的可用性增加了对用于比较不同植物基因型和不同处理条件之间的变化模式的统计方法的兴趣。两种广泛使用的建模随时间增长的方法是逐点方差分析（ANOVA）和参数S形曲线拟合。逐点方差分析产生不连续的生长曲线，这不能反映植物生长模式的真实动态。相比之下，将参数模型拟合到时间序列观测值确实捕捉到了生长趋势，但是这些模型需要关于植物生长的真实模式的假设。根据物种、处理方案和采样的植物生命周期子集，该假设并不总是成立。在这里，我们介绍了一种不同的方法--函数方差分析--它可以产生连续的生长曲线，而不需要对植物生长模式进行假设。我们比较和验证这种方法使用的数据从实验测量两个玉米（玉米ssp。玉米）基因型下的两个水分有效性处理超过21天的时间。函数方差分析能够对植物性状随时间变化的动态进行非参数估计，而无需对曲线形状进行假设。除了估计性状值随时间的平滑曲线外，函数方差分析还同时估计这些曲线的导数-例如生长率。使用两种不同的二次抽样策略，我们证明，这种功能方差分析方法，使植物之间的生长曲线的比较表型在非重叠的日子，估计精度几乎没有减少。这意味着基于功能ANOVA的方法可以允许在给定固定数量的表型基础设施和人员的单个实验中对更大数量的样品和生物学重复进行评分。
Recent advances in automated plant phenotyping have enabled the collection time series measurements from the same plants of a wide range of traits over different developmental time scales. The availability of time series phenotypic datasets has increased interest in statistical approaches for comparing patterns of change between different plant genotypes and different treatment conditions. Two widely used methods of modeling growth over time are point-wise analysis of variance (ANOVA) and parametric sigmoidal curve fitting. Point-wise ANOVA yields discontinuous growth curves, which do not reflect the true dynamics of growth patterns in plants. In contrast, fitting a parametric model to a time series of observations does capture the trend of growth, however these models require assumptions regarding the true pattern of plant growth. Depending on the species, treatment regime, and subset of the plant lifecycle sampled this assumptions will not always hold true. Here we introduce a different approach – functional ANOVA – which yields continuous growth curves without requiring assumptions regarding patterns of plant growth. We compare and validate this approach using data from an experiment measuring growth of two maize (Zea mays ssp. mays) genotypes under two water availability treatments over a 21-day period. Functional ANOVA enables a nonparametric estimation of the dynamics of changes in plant traits over time without assumptions regarding curve shape. In addition to estimating smooth curves of trait values over time, functional ANOVA also estimates the the derivatives of these curves – e.g. growth rates – simultaneously. Using two different subsampling strategies, we demonstrate that this functional ANOVA method enables the comparison of growth curves between plants phenotyped on non-overlapping days with little reduction in estimation accuracy. This means functional ANOVA based approaches can allow larger numbers of samples and biological replicates to be scored in a single experiment given fixed amounts of phenotyping infrastructure and personnel.