JIP-test荧光数据及其它生理生态数据主成分综合分析(PCA)实例解析

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近年来，快速叶绿素荧光诱导动力学曲线(OJIP曲线)及其数据分析方法JIP-test在植物科学研究中的应用越来越广泛(BussottiF, et al., 2020; KalajiH M, et al., 2017; Pontes. D, 2019; Tsimilli-michael M, 2020)。OJIP曲线可以更直观地表现出差异，JIP-test则提供丰富的参数，由于其测定方便简单，逐渐成为科研工作者们研究光合作用原初光化学反应的有力工具。

JIP-test提供丰富的参数，PCA进行数据降维处理，两者结合，能够快速处理并分析大量的实验数据，(i)揭示影响实验的主要参数，并可(ii)聚类不同处理之间的差异，也可用于(iii)大数据分析并预测植物生长变化。下面通过三篇文章来详细介绍二者的结合应用。

近年来，全球范围内短期内涝等自然灾害频发，并且随着北半球高纬度地区秋冬季降水量的增加，这种情况的出现可能会更加频繁。研究结果表明，淹水温度是影响植物对该胁迫反应的重要因素。该试验研究了耐寒性不同的四种高羊茅在低温下对土壤水分过剩的光合机构响应，旨在验证Rubisco活性改变引起的叶片水溶性碳水化合物浓度变化是否会影响土壤水分过剩条件下的光适应。

图1. 主成分分析的向量图，显示了低温对照和低温淹水被调查变量之间的相关性

光是控制植物生长发育的主要因素。光不仅推动植物光合作用，光质和光照时间还驱动着植物主要的发育变化，如光形态发生、开花的光周期诱导、向光性、避荫以及防御等。为了评估光照条件对植物生理状态的影响，该研究在豌豆植株的早期发育过程中使用正常白光(W)、白色阴影(WS)、高光强蓝/红/远红组合光(BR)和低光强蓝/红/远红组合光(BRS)四种光照射，采用JIP-test来评估与光吸收和电子传输有关的PSII参数，并通过PCA技术聚类分析不同光照之间的差异。

图2. JIP-test参数和不同处理的主成分分析(Plant variants: W – white light; WS – white light with shadow; BR – blue and red light; BRS – blue and red light with shadow)

在大规模生态调查中，为了达成筛选目的和效率，一般使用有限的参数来对样本进行快速、简单的评价和生理分类。人们提出了许多形态、化学和生理指标来评价生态系统中的植物状况。其中，叶绿素的瞬时荧光分析(JIP-test)被认为特别适用于大型生态调查，并能在短时间内筛选出许多样品。

本研究中分析的最大数据集源自FunDivEUROPE项目(Functional Significance ofthe Forest Diversity in Europe, European Union, 7th FrameworkProgram)。此项目中分析了整个欧洲的森林生态系统，从地中海到欧洲北部地区，涵盖了丰富的差异树种组成。其中包括天然高大森林(In Italy, Spain, Romania,Poland,Finland, Baeten et al., 2013)和人工林场(In Finland and Germany, seeVerheyen et al.,2016)。

如何实现对叶绿素a荧光数据(JIP-test参数)、其它生理参数和基因、蛋白等分子数据组成的大数据库进行PCA分析？

通常我们可以使用学术界常用的商用数据分析软件进行PCA分析，如SPSS Statistics(IBM Corp)、Statistica(StatSoft Inc. 2011)和SAS(SAS Enterprise Miner; SAS Institute, Cary, NC)等。

在全球互联网化的大趋势下，也涌现出一批使用体验更佳、分析更加智能化的在线数据分析工具，如SPSSAU(QingSi Technology Ltd 2016-2020)、ClustVis(Metsalu, Tauno et al. 2015)等。

此外以R语言和Python为代表的计算机程序设计语言可以实现对大数据的快速智能处理、计算和制图，使用R语言和Python对JIP-test荧光数据进行PCA数据分析也已有非常成熟的语言包进行应用。

下期文章我们将以IBM SPSS Statistics 26为例详细介绍JIP-test荧光参数PCA分析操作方法，敬请期待！

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