本文選題：微陣列數(shù)據(jù) + 統(tǒng)計(jì)推斷　； 參考：《第四軍醫(yī)大學(xué)》2009年碩士論文

【摘要】： 微陣列技術(shù)是生物技術(shù)變革的核心,允許研究者同時(shí)監(jiān)測成千上萬個(gè)基因的表達(dá)水平,已廣泛應(yīng)用于醫(yī)學(xué)研究。如何挖掘海量基因表達(dá)信息中的有用信息,并進(jìn)行生物學(xué)專業(yè)解釋是基因表達(dá)譜數(shù)據(jù)分析領(lǐng)域所面臨的一個(gè)重要挑戰(zhàn)。目前,針對海量基因表達(dá)數(shù)據(jù)不同學(xué)者和研究機(jī)構(gòu)提供了各種統(tǒng)計(jì)分析方法和工具。本研究將這些方法大致劃分為兩大類:單基因分析(Single Gene Analysis,SGA)、基因集分析(Gene Set Analysis,GSA)。其目的都是為了能篩選出有差異表達(dá)的基因,以得到疾病的控制和預(yù)測。單基因分析不能有效地解釋生物學(xué)特性,且沒有考慮基因間的相關(guān)性,因此結(jié)論非常有限。自2003年Mootha等提出基因富集分析方法以來,微陣列數(shù)據(jù)基因集分析引起了統(tǒng)計(jì)學(xué)者與生物信息學(xué)者的廣泛關(guān)注。然而,由于基因表達(dá)譜數(shù)據(jù)本身特有的多維、樣本量小以及基因間復(fù)雜的相關(guān)性等特點(diǎn),至今沒有一套成熟的理論和公認(rèn)有效的篩選差異表達(dá)基因集的方法。本碩士課題結(jié)合實(shí)際微陣列數(shù)據(jù)、利用計(jì)算機(jī)技術(shù)和蒙特卡羅模擬研究微陣列數(shù)據(jù)基因集的統(tǒng)計(jì)分析理論方法及其應(yīng)用,主要內(nèi)容包括基因集分析方法原假設(shè)的合理性、Ⅰ型錯(cuò)誤的控制、篩選差異表達(dá)基因集(Different Expression Gene set,DEGs)的有效性等等。目前作了以下工作: 1.簡要介紹微陣列實(shí)驗(yàn)基本概念、基因集注釋數(shù)據(jù)庫和單基因分析方法。在此基礎(chǔ)上廣泛復(fù)習(xí)和評價(jià)國內(nèi)外有關(guān)微陣列數(shù)據(jù)的基因集分析方法。按照基因集的定義、統(tǒng)計(jì)原假設(shè)框架與統(tǒng)計(jì)量理論分布的生成回顧和綜述了基因表達(dá)譜富集分析方法。 2.基因集分析原假設(shè)包括競爭性原假設(shè)(Q1)、自限性原假設(shè)(Q2)和混合型原假設(shè)(Q3)。更多的研究團(tuán)體認(rèn)為自限性原假設(shè)方法要好于基于競爭性原假設(shè)進(jìn)行的統(tǒng)計(jì)推斷,但究竟哪種原假設(shè)更合理目前尚無定論。為了探討此問題,本研究通過模擬實(shí)驗(yàn)進(jìn)行比較研究。結(jié)果表明,自限性原假設(shè)方法檢驗(yàn)效能較高,能識別出較多的差異表達(dá)基因集,但錯(cuò)誤發(fā)現(xiàn)率較高;而競爭性原假設(shè)方法則是通過削弱其檢驗(yàn)效能來達(dá)到較高的準(zhǔn)確性;混合型原假設(shè)方法識別出的差異表達(dá)基因數(shù)及檢驗(yàn)效能位于中間。我們建議進(jìn)行微陣列數(shù)據(jù)分析時(shí),如果條件允許可以采用不同原假設(shè)方法分析,否則采用混合型原假設(shè),因?yàn)樗朔薗1、Q2方法的主要缺陷。 3.由于基因集統(tǒng)計(jì)量的概率密度函數(shù)未知,故一般采用重排列或有放回抽樣方法得到其理論分布。通常會認(rèn)為重排列效果優(yōu)于反復(fù)抽樣,但是我們通過模擬實(shí)驗(yàn)發(fā)現(xiàn)兩種效果基本一致,ROC曲線分析結(jié)果顯示有放回抽樣方法得到的曲線下面積稍大于重排列方法,說明同等條件下自助法抽樣略優(yōu)于樣本重排列。 4.假定基因間相互獨(dú)立的前提下,借助SAS 9.13模擬產(chǎn)生數(shù)據(jù)集,比較不同基因集方法篩選差異表達(dá)基因集的有效性。結(jié)果顯示Efron’s GSA方法的特異度及靈敏度均高于其它方法,而SAFE、Globaltest方法的檢驗(yàn)效能僅次于Efron’s GSA方法。 5.由于基因間往往存在復(fù)雜的相關(guān)性,在模擬數(shù)據(jù)中納入這種相關(guān)關(guān)系。模擬實(shí)驗(yàn)分析結(jié)果發(fā)現(xiàn)Efron’s GSA對此類數(shù)據(jù)完全失去判別能力,幾乎不能識別任何差異表達(dá)基因集。而PCOT2、Globaltest方法的效果卻非常顯著,能很好地識別模擬數(shù)據(jù)設(shè)定的差異表達(dá)基因集。 6.采用不同基因集方法對兩個(gè)著名的微陣列實(shí)驗(yàn)數(shù)據(jù)進(jìn)行實(shí)例分析比較。結(jié)論進(jìn)一步證實(shí)考慮了基因間相關(guān)性基因集方法PCOT2、Globaltest優(yōu)于其他方法。而Globaltest方法能識別更多差異表達(dá)基因集,且模擬設(shè)定條件下錯(cuò)誤發(fā)現(xiàn)率比PCOT2低19%。綜合模擬及實(shí)例數(shù)據(jù)分析結(jié)果,我們更傾向于主張采用模型分析法,如Globaltest方法(構(gòu)建logistic隨機(jī)效應(yīng)模型)進(jìn)行基因集的分析。 本課題的創(chuàng)新點(diǎn)主要包括以下幾點(diǎn):①針對不同原假設(shè)、理論分布生成方法對基因集分析結(jié)果的影響做了模擬比較研究。②將基因間相關(guān)性從不同角度納入模擬實(shí)驗(yàn)數(shù)據(jù),分別模擬每個(gè)基因集內(nèi)部相關(guān)性,并基于此模擬數(shù)據(jù)進(jìn)行基因集方法檢驗(yàn)效能的比較。③模擬實(shí)驗(yàn)結(jié)果顯示基于模型構(gòu)建的基因集方法在數(shù)據(jù)分析時(shí)有效地考慮了基因間的相關(guān)性。④綜合實(shí)例比較后提出Globaltest是較有效的微陣列數(shù)據(jù)分析方法。 本課題主要是在微陣列數(shù)據(jù)基因集分析方法統(tǒng)計(jì)理論基礎(chǔ)上,對其所涉及的一些方法及相關(guān)問題進(jìn)行了探索和研究,并給出了我們認(rèn)為比較有效的基因表達(dá)譜數(shù)據(jù)分析法。期望能夠?yàn)殛兾魇】萍加?jì)劃攻關(guān)項(xiàng)目(微陣列數(shù)據(jù)差異表達(dá)信息挖掘及應(yīng)用研究,編號:2008K04-02)的下一步研究工作打下良好基礎(chǔ),為基因表達(dá)微陣列數(shù)據(jù)的統(tǒng)計(jì)分析方法,尤其是基因集分析提供參考。
[Abstract]:microarray technology is the core of biotechnology change , which allows researchers to monitor the expression level of thousands of genes at the same time and has been widely used in medical research . The aim is to screen out the differentially expressed genes in order to get the control and prediction of diseases . The single gene analysis can not effectively explain the biological characteristics , and therefore , it is very limited . Since the gene expression profiling data itself has many characteristics such as multi - dimensional , small sample size and complex correlation between genes , the main contents include the rationality of the original hypothesis of gene set analysis method , the control of type I error , the selection of different expression gene set ( DEGs ) , and so on . The following work is currently done :



1 . The basic concepts of microarray experiments , gene set annotation database and single gene analysis method are introduced briefly . On this basis , we review and evaluate the gene set analysis method about microarray data at home and abroad . According to the definition of gene set , the generation of statistical original hypothesis framework and statistical quantity theory distribution is reviewed and summarized . The analysis method of gene expression profiling is summarized .



2 . The original hypothesis of gene set analysis includes competitive source hypothesis ( Q1 ) , self - limiting original hypothesis ( Q2 ) and mixed primary hypothesis ( Q3 ) . More research groups believe that self - limiting original hypothesis method is better than statistical inference based on competitive original hypothesis .



3 . Because the probability density function of gene set statistics is unknown , the theoretical distribution is usually obtained by the method of re - arranging or sampling . It is usually considered that the re - arrangement effect is superior to the repeated sampling , but we find that the two effects are basically consistent through the simulation experiment , and the ROC curve analysis results show that the area under the curve obtained by the sampling method is slightly larger than that of the re - sampling method , so that the self - service method sampling under the same condition is slightly better than the sample rearranger .



4 . On the premise of mutual independence of genes , the data set was simulated by SAS 9.13 , and the validity of different gene set methods was compared . The results showed that the specificity and sensitivity of the method were higher than those of other methods .



5 . Because of the complex correlation among genes , this correlation was included in the simulation data . The results of the simulation experiment showed that the Eron ' s gsa completely lost the discrimination ability to such data , almost unable to identify any differentially expressed gene sets . The results of the PCOT2 and Globaltest methods were very significant , and the difference expression gene set of the simulated data set can be well recognized .



6 . Two well - known microarray experimental data were analyzed and compared with different gene sets . The conclusion further confirmed that the gene set method PCOT2 and Globaltest were better than other methods . The Globaltest method could identify more differentially expressed gene sets , and the error rate was 19 % lower than that of PCOT2 under simulated set conditions .



The innovation points of this project mainly include the following points : ( 1 ) the influence of the theory distribution generation method on the results of gene set analysis is simulated and compared for different original hypotheses . 鈶，

本文編號：1801933