【摘要】：基因表達分析是轉錄組學最基本的研究手段之一,對基因和異構體表達水平的計算及差異表達分析,有助于人們了解基因和剪切異構體的功能以及調控機制。作為當前主流的兩種大規(guī)�；虮磉_測量技術,基因芯片和基于高通量測序技術的RNA-Seq方法廣泛應用于轉錄組學研究領域,并且產生了海量的表達數據,為多平臺表達數據融合提供了可行性。本文的工作主要從以下兩方面展開研究:(1)多平臺下基因和異構體表達分析對比研究。首先介紹了廣泛使用的Affymetrix傳統(tǒng)3’基因芯片、外顯子芯片、較新的全轉錄組芯片,以及基于RNA-Seq技術的Illumina平臺這四個主流實驗平臺的技術原理。其次從基因表達水平計算和差異表達分析兩方面介紹了每個平臺下一些主流數據分析方法,分析了每個平臺下各數據分析方法的優(yōu)劣,并通過標準數據集對比分析了一些代表性方法的性能,獲得的對比研究結果為研究者選擇實驗平臺以及表達數據分析方法提供了參考。(2)融合多平臺表達數據的轉錄組差異表達分析。針對現(xiàn)有的多平臺差異表達分析研究方法存在的問題,本文提出了融合多平臺表達數據的差異表達檢測模型mpDE(multi-platform Differential Expression model)。該模型將不同實驗平臺表達數據和技術性測量誤差融入模型中,同時考慮了同一平臺在不同條件下的生物重復或技術重復的波動性,從而提高差異表達分析的準確度。本文將mpDE應用到三個人類數據集,并與單平臺的差異表達檢測結果和其他多平臺表達數據融合方法進行了對比。實驗結果表明,mpDE能夠獲得更加準確靈敏的差異表達分析結果。
[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.
【學位授予單位】：南京航空航天大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：Q811.4

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