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      首页 >> 科研前線 >>PEA植物效率分析儀 >> JIP-test熒光數據及其它生理生態數據主成分綜合分析(PCA)實例解析
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      JIP-test熒光數據及其它生理生態數據主成分綜合分析(PCA)實例解析

      歡迎關注「漢莎科技集團」微信公眾號!

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      近年來,快速葉綠素熒光誘導動力學曲線(OJIP曲線)及其數據分析方法JIP-test在植物科學研究中的應用越來越廣泛(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出差異,JIP-test則提供豐富的參數,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反應的有力工具。

      在植物科學實驗中,測定的實驗數據非常多,比如光合作用參數、植物生長指標、各種酶活性以及分子實驗數據等,再加上JIP-test本身提供的幾十種參數,豐富實驗數據的同時,也會給后期的處理帶來很大的工作量。因此,采用準確的數據處理分析方法尤其重要。
      主成分分析法(PCA)是數據挖掘中常用的一種降維算法。所謂降維,就是把具有相關性的變量數目減少,用較少的變量來取代原先變量。在植物科學研究的實際應用中,各種參數相互之間會有影響,通過PCA分析處理后,會得到有限的幾個主成分,由其代表實驗參數就可以說明實驗問題了,也就是所謂的降維(KalajiH M, et al., 2018;Goltsev V, et al., 2012)。

      1590139275499104.png

      JIP-test提供豐富的參數,PCA進行數據降維處理,兩者結合,能夠快速處理并分析大量的實驗數據,(i)揭示影響實驗的主要參數,并可(ii)聚類不同處理之間的差異,也可用于(iii)大數據分析并預測植物生長變化。下面通過三篇文章來詳細介紹二者的結合應用。

      1. 解析參數間的相關性,篩選出可禁用詞匯解釋問題的參數(Jurczyk B,2015)

      1590139314505291.png

      近年來,全球范圍內短期內澇等自然災害頻發,并且隨著北半球高緯度地區秋冬季降水量的增加,這種情況的出現可能會更加頻繁。研究結果表明,淹水溫度是影響植物對該脅迫反應的重要因素。該試驗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構響應,旨在驗證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。

      通過研究低溫淹水對葉綠素 a 熒光參數、水溶性碳水化合物(WSC)、Rubisco活化酶基因表達(RcaA)Rubisco活性(RA)的影響,并進行PCA主成分分析,以減少需要進行詳細分析的參數數量,并篩選出能禁用詞匯解釋問題的參數。

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      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查變量之間的相關性

      淹水脅迫會直接導致植物水下組織供氧不足,缺氧后植物會加速使用碳儲備進而導致碳源供應不足。主成分分析證實,由圖1可以看出,淹水脅迫后,被測變量之間的關系發生顯著性改變。在對照條件時,水溶性碳水化合物與能量傳遞效率相關參數(ETo/TRo、ETo/ABS、ETo/RC)有很高的正相關性,說明WSC的積累在對照條件下是不受限制的;淹水后,WSC與能量耗散效率(DIo/CS、DIo/RC)呈正相關,說明能量轉移的干擾可能限制了WSC的濃度。
      另一方面,WSC與描述單個活性反應中心效率的參數高度相關,揭示了類囊體膜可能也因淹水受到損傷。此外,qP和RcaA在對照植株中的表達之間的強相關性可能表明這兩個性狀的調控機制相似,可能與ADP/ATP比值有關。在淹水條件下,qP和RcaA的表達不相關,提示另一個因素可能調節RcaA轉錄水平。
      總的來說低溫淹水后,酶活性劇烈下降,光反應階段吸收的光能過剩,維持較高的WSC含量能夠激活光合作用適應寒冷的熱耗散機制,有助于耗散掉過剩光能。
      2. 聚類分析不同處理之間的差異(Zhiponova M, 2020)

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      光是控制植物生長發育的主要因素。光不僅推動植物光合作用,光質和光照時間還驅動著植物主要的發育變化,如光形態發生、開花的光周期誘導、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的影響,該研究在豌豆植株的早期發育過程中使用正常白光(W)、白色陰影(WS)、高光強藍/紅/遠紅組合光(BR)和低光強藍/紅/遠紅組合光(BRS)四種光照射,采用JIP-test來評估與光吸收和電子傳輸有關的PSII參數,并通過PCA技術聚類分析不同光照之間的差異。

      1590139378707751.png

      圖2. JIP-test參數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)

      對獲得的JIP-test參數進行主成分分析表明,盡管不同處理之間存在重疊,但它們對光合機構的影響差異還是很容易區分的。
      PC1根據PSII活性分離出不同的處理,較低的值表示更高的PSII性能(低光吸收、高光化學和電子傳輸效率);PC2則對應PSI活性,較高的值表明PSI性能較高。
      W處理表現出PSI和PSII的禁用詞匯綜合性能;WS處理表現出PSI和PSII的禁用詞匯綜合性能;BR處理表現出受損的PSII和完整的PSI活性;BRS處理表現出低PSI和完整的PSII性能。
      結合其他生理數據和主成分分析可以揭示光合作用與開花的關系。具有高PSII表現(-PC1)的W和BRS處理在其發育后期發育出相同數量的花,而具有抑制PSII活性(+PC1)的WS和BR植株發育后期沒有開花。
      研究結果表明,PIABS在PC1上最相關,可作為預測豌豆開花數量的最準確指標。
      3. 通過PCA技術對大樣本試驗進行數據分析(Bussotti F, 2020)

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      在大規模生態調查中,為了達成篩選目的和效率,一般使用有限的參數來對樣本進行快速、簡單的評價和生理分類。人們提出了許多形態、化學和生理指標來評價生態系統中的植物狀況。其中,葉綠素的瞬時熒光分析(JIP-test)被認為特別適用于大型生態調查,并能在短時間內篩選出許多樣品。

      JIP-test提供了五十多個參數來評估植物光合機構的光化學性質和功能,這些參數可以描述光化學過程在能量吸收、俘獲和電子傳輸方面的不同階段。
      該研究采用主成分分析法(PCA)對過去在野外條件(森林、人工林和牧場)和實驗室條件中獲得的43987個測量數據進行分析,目的是探討JIP-test參數之間的關系,以選擇最合適的參數來捕捉植物光合效率的變異性及其對逆境的響應。

      1590139418984953.jpeg

      本研究中分析的最大數據集源自FunDivEUROPE項目(Functional Significance ofthe Forest Diversity in Europe, European Union, 7th FrameworkProgram)。此項目中分析了整個歐洲的森林生態系統,從地中海到歐洲北部地區,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)。

      通過PCA技術分析發現,所選的JIP-test參數形成了三個很好分離的簇。其中兩個位于PC1(Cluster 1&2)上,一個(Cluster 3)位于PC2上。每一組參數描述了不同的生理過程:光能捕獲和光化學階段(Cluster 1)、耗散(Cluster 2)和熱階段(Cluster 3)。基于PCA分析,該研究認為樣品的整體光合性能可以用PITOT來表示,或者用Fv/Fm和ΔVIP共同來表示。

      1590139444116807.png

      圖3. 所選JIP-test參數的主成分分析

      在大多數情況下,植物的光合性能可用Fv/Fm和ΔVIP來描述。經過驗證,Fv/Fm和ΔVIP能夠有效地代表各種調查(野外和實驗室)、氣候和時間跨度、植物物種和功能群(針葉樹和闊葉樹物種、草本植物)中樣本的變異性。因此,可用于探索性調查,以篩選大樣本植物的光合性能,以及它們對不同生態條件的適應性。
      在林業或生態學調查方面,以JIP-test為代表的植被葉綠素熒光特性的大規模野外調查對驗證無人機或衛星遙感觀測結果具有重要意義,遙感觀測數據和野外實地調查數據之間的銜接將是今后生態學研究的一個重要領域(Bussotti F, 2020)!
      總述
      以上實例說明,PCA分析與JIP-test結合應用越來越廣泛,大大提高了數據分析效率,能夠快速判斷實驗處理后的主要變化,并分析主要影響因素,從而對實驗材料進行預判。近年來,PCA在植物科學研究中的應用呈上升趨勢,相信科研工作者們會開發出更多更好的應用方向。

      1590154378958915.png

      如何實現對葉綠素a熒光數據(JIP-test參數)、其它生理參數和基因、蛋白等分子數據組成的大數據庫進行PCA分析?

      通常我們可以使用學術界常用的商用數據分析軟件進行PCA分析,如SPSS Statistics(IBM Corp)、Statistica(StatSoft Inc. 2011)和SAS(SAS Enterprise Miner; SAS Institute, Cary, NC)等。

      在全球互聯網化的大趨勢下,也涌現出一批使用體驗更佳、分析更加智能化的在線數據分析工具,如SPSSAU(QingSi Technology Ltd 2016-2020)、ClustVis(Metsalu, Tauno et al. 2015)等。

      此外以R語言和Python為代表的計算機程序設計語言可以實現對大數據的快速智能處理、計算和制圖,使用R語言和Python對JIP-test熒光數據進行PCA數據分析也已有非常成熟的語言包進行應用。

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

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