JIP-test熒光數(shù)據(jù)及其它生理生態(tài)數(shù)據(jù)主成分綜合分析(PCA)實(shí)例解析
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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)。
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。
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。
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。
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。
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)。
圖3. 所選JIP-test參數(shù)的主成分分析
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！
本文參考文獻(xiàn)
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