【摘要】：基因表達(dá)分析是轉(zhuǎn)錄組學(xué)最基本的研究手段之一,對(duì)基因和異構(gòu)體表達(dá)水平的計(jì)算及差異表達(dá)分析,有助于人們了解基因和剪切異構(gòu)體的功能以及調(diào)控機(jī)制。作為當(dāng)前主流的兩種大規(guī)�；虮磉_(dá)測(cè)量技術(shù),基因芯片和基于高通量測(cè)序技術(shù)的RNA-Seq方法廣泛應(yīng)用于轉(zhuǎn)錄組學(xué)研究領(lǐng)域,并且產(chǎn)生了海量的表達(dá)數(shù)據(jù),為多平臺(tái)表達(dá)數(shù)據(jù)融合提供了可行性。本文的工作主要從以下兩方面展開研究:(1)多平臺(tái)下基因和異構(gòu)體表達(dá)分析對(duì)比研究。首先介紹了廣泛使用的Affymetrix傳統(tǒng)3’基因芯片、外顯子芯片、較新的全轉(zhuǎn)錄組芯片,以及基于RNA-Seq技術(shù)的Illumina平臺(tái)這四個(gè)主流實(shí)驗(yàn)平臺(tái)的技術(shù)原理。其次從基因表達(dá)水平計(jì)算和差異表達(dá)分析兩方面介紹了每個(gè)平臺(tái)下一些主流數(shù)據(jù)分析方法,分析了每個(gè)平臺(tái)下各數(shù)據(jù)分析方法的優(yōu)劣,并通過標(biāo)準(zhǔn)數(shù)據(jù)集對(duì)比分析了一些代表性方法的性能,獲得的對(duì)比研究結(jié)果為研究者選擇實(shí)驗(yàn)平臺(tái)以及表達(dá)數(shù)據(jù)分析方法提供了參考。(2)融合多平臺(tái)表達(dá)數(shù)據(jù)的轉(zhuǎn)錄組差異表達(dá)分析。針對(duì)現(xiàn)有的多平臺(tái)差異表達(dá)分析研究方法存在的問題,本文提出了融合多平臺(tái)表達(dá)數(shù)據(jù)的差異表達(dá)檢測(cè)模型mpDE(multi-platform Differential Expression model)。該模型將不同實(shí)驗(yàn)平臺(tái)表達(dá)數(shù)據(jù)和技術(shù)性測(cè)量誤差融入模型中,同時(shí)考慮了同一平臺(tái)在不同條件下的生物重復(fù)或技術(shù)重復(fù)的波動(dòng)性,從而提高差異表達(dá)分析的準(zhǔn)確度。本文將mpDE應(yīng)用到三個(gè)人類數(shù)據(jù)集,并與單平臺(tái)的差異表達(dá)檢測(cè)結(jié)果和其他多平臺(tái)表達(dá)數(shù)據(jù)融合方法進(jìn)行了對(duì)比。實(shí)驗(yàn)結(jié)果表明,mpDE能夠獲得更加準(zhǔn)確靈敏的差異表達(dá)分析結(jié)果。
[Abstract]:Gene expression analysis is one of the most basic research methods in transcriptome. The calculation of gene and isomer expression level and differential expression analysis are helpful to understand the function and regulation mechanism of gene and shear isomer. As two kinds of large-scale gene expression measurement techniques, gene chip and RNA-Seq method based on high-throughput sequencing technology are widely used in the field of transcriptome research, and produce a large amount of expression data. It provides the feasibility for multi-platform expression data fusion. The main works of this paper are as follows: (1) the comparative analysis of gene and isomer expression in multi-platform. Firstly, the technical principles of traditional Affymetrix 3'gene chip, exon chip, new full transcriptome chip and Illumina platform based on RNA-Seq technology are introduced. Secondly, from two aspects of gene expression level calculation and differential expression analysis, this paper introduces some mainstream data analysis methods under each platform, and analyzes the advantages and disadvantages of each data analysis method under each platform. The performance of some representative methods is compared with the standard data set. The results provided a reference for the researchers to choose the experimental platform and the method of expression data analysis. (2) the transcriptome differential expression analysis combined with multi-platform expression data. Aiming at the problems existing in the existing research methods of multi-platform differential expression analysis, a differential expression detection model (mpDE) (multi-platform Differential Expression model).) based on fusion of multi-platform expression data is proposed in this paper. The model integrates different experimental platform data and technical measurement error into the model and considers the volatility of biological repetition or technical repetition of the same platform under different conditions so as to improve the accuracy of differential expression analysis. In this paper, mpDE is applied to three human datasets, and the results of differential expression detection on single platform and other multi-platform expression data fusion methods are compared. The experimental results show that MMP DE can obtain more accurate and sensitive differential expression analysis results.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：Q811.4

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