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近年來，快速葉綠素?zé)晒庹T導(dǎo)動力學(xué)曲線(OJIP曲線)及其數(shù)據(jù)分析方法JIP-test在植物科學(xué)研究中的應(yīng)用越來越廣泛(BussottiF, et al., 2020; KalajiH M, et al., 2017; Pontes. D, 2019; Tsimilli-michael M, 2020)。OJIP曲線可以更直觀地表現(xiàn)出差異，JIP-test則提供豐富的參數(shù)，由于其測定方便簡單，逐漸成為科研工作者們研究光合作用原初光化學(xué)反應(yīng)的有力工具。

JIP-test提供豐富的參數(shù)，PCA進(jìn)行數(shù)據(jù)降維處理，兩者結(jié)合，能夠快速處理并分析大量的實(shí)驗(yàn)數(shù)據(jù)，(i)揭示影響實(shí)驗(yàn)的主要參數(shù)，并可(ii)聚類不同處理之間的差異，也可用于(iii)大數(shù)據(jù)分析并預(yù)測植物生長變化。下面通過三篇文章來詳細(xì)介紹二者的結(jié)合應(yīng)用。

近年來，全球范圍內(nèi)短期內(nèi)澇等自然災(zāi)害頻發(fā)，并且隨著北半球高緯度地區(qū)秋冬季降水量的增加，這種情況的出現(xiàn)可能會更加頻繁。研究結(jié)果表明，淹水溫度是影響植物對該脅迫反應(yīng)的重要因素。該試驗(yàn)研究了耐寒性不同的四種高羊茅在低溫下對土壤水分過剩的光合機(jī)構(gòu)響應(yīng)，旨在驗(yàn)證Rubisco活性改變引起的葉片水溶性碳水化合物濃度變化是否會影響土壤水分過剩條件下的光適應(yīng)。

圖1. 主成分分析的向量圖，顯示了低溫對照和低溫淹水被調(diào)查變量之間的相關(guān)性

光是控制植物生長發(fā)育的主要因素。光不僅推動植物光合作用，光質(zhì)和光照時(shí)間還驅(qū)動著植物主要的發(fā)育變化，如光形態(tài)發(fā)生、開花的光周期誘導(dǎo)、向光性、避蔭以及防御等。為了評估光照條件對植物生理狀態(tài)的影響，該研究在豌豆植株的早期發(fā)育過程中使用正常白光(W)、白色陰影(WS)、高光強(qiáng)藍(lán)/紅/遠(yuǎn)紅組合光(BR)和低光強(qiáng)藍(lán)/紅/遠(yuǎn)紅組合光(BRS)四種光照射，采用JIP-test來評估與光吸收和電子傳輸有關(guān)的PSII參數(shù)，并通過PCA技術(shù)聚類分析不同光照之間的差異。

圖2. JIP-test參數(shù)和不同處理的主成分分析(Plant variants: W – white light; WS – white light with shadow; BR – blue and red light; BRS – blue and red light with shadow)

在大規(guī)模生態(tài)調(diào)查中，為了達(dá)成篩選目的和效率，一般使用有限的參數(shù)來對樣本進(jìn)行快速、簡單的評價(jià)和生理分類。人們提出了許多形態(tài)、化學(xué)和生理指標(biāo)來評價(jià)生態(tài)系統(tǒng)中的植物狀況。其中，葉綠素的瞬時(shí)熒光分析(JIP-test)被認(rèn)為特別適用于大型生態(tài)調(diào)查，并能在短時(shí)間內(nèi)篩選出許多樣品。

本研究中分析的最大數(shù)據(jù)集源自FunDivEUROPE項(xiàng)目(Functional Significance ofthe Forest Diversity in Europe, European Union, 7th FrameworkProgram)。此項(xiàng)目中分析了整個(gè)歐洲的森林生態(tài)系統(tǒng)，從地中海到歐洲北部地區(qū)，涵蓋了豐富的差異樹種組成。其中包括天然高大森林(In Italy, Spain, Romania,Poland,Finland, Baeten et al., 2013)和人工林場(In Finland and Germany, seeVerheyen et al.,2016)。

如何實(shí)現(xiàn)對葉綠素a熒光數(shù)據(jù)(JIP-test參數(shù))、其它生理參數(shù)和基因、蛋白等分子數(shù)據(jù)組成的大數(shù)據(jù)庫進(jìn)行PCA分析？

通常我們可以使用學(xué)術(shù)界常用的商用數(shù)據(jù)分析軟件進(jìn)行PCA分析，如SPSS Statistics(IBM Corp)、Statistica(StatSoft Inc. 2011)和SAS(SAS Enterprise Miner; SAS Institute, Cary, NC)等。

在全球互聯(lián)網(wǎng)化的大趨勢下，也涌現(xiàn)出一批使用體驗(yàn)更佳、分析更加智能化的在線數(shù)據(jù)分析工具，如SPSSAU(QingSi Technology Ltd 2016-2020)、ClustVis(Metsalu, Tauno et al. 2015)等。

此外以R語言和Python為代表的計(jì)算機(jī)程序設(shè)計(jì)語言可以實(shí)現(xiàn)對大數(shù)據(jù)的快速智能處理、計(jì)算和制圖，使用R語言和Python對JIP-test熒光數(shù)據(jù)進(jìn)行PCA數(shù)據(jù)分析也已有非常成熟的語言包進(jìn)行應(yīng)用。

下期文章我們將以IBM SPSS Statistics 26為例詳細(xì)介紹JIP-test熒光參數(shù)PCA分析操作方法，敬請期待！

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