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近年來，快速葉綠素?zé)晒庹T導(dǎo)動(dòng)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(cè)定方便簡(jiǎn)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(cè)植物生長(zhǎng)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(huì)更加頻繁。研究結(jié)果表明，淹水溫度是影響植物對(duì)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(duì)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(huì)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(duì)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(zhǎng)發(fā)育的主要因素。光不僅推動(dòng)植物光合作用，光質(zhì)和光照時(shí)間還驅(qū)動(dòng)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(píng)估光照條件對(duì)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(píng)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(duì)樣本進(jìn)行快速、簡(jiǎn)單的評(píng)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(píng)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(chǎng)(In Finland and Germany, seeVerheyen et al.,2016)。

如何實(shí)現(xiàn)對(duì)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(shì)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(duì)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(duì)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(qǐng)期待！

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