生物資源暨農學院: 農藝學研究所指導教授: 蔡政安李沛洵Li, Pei-HsunPei-HsunLi2017-03-062018-07-112017-03-062018-07-112016http://ntur.lib.ntu.edu.tw//handle/246246/275882During the past few years, RNA-Seq technology has been widely employed for studying the transcriptome since it has clear advantages over the other transcriptomic technologies. The most popular use of RNA-seq applications is to identify differentially expressed genes. In addition, gene set analysis (GSA) aims to determine whether a predefined gene set, in which the genes share a common biological function, is correlated with the pheno-type. To date, many GSA approaches have been developed for identifying differentially expressed gene sets using microarray data. However, these methods are not directly ap-plicable to RNA-seq data due to intrinsic difference between two data structures. When testing the differential expression of gene sets, there is a critical assumption that the mem-bers in each gene set are sampled independently in most GSA methods. It means that the genes within a gene set don’t share a common biological function. In order to resolve this issue, we propose a GSA method based on the De-correlation (DECO) algorithm by Dougu Nam (2010) to remove the correlation bias in the expression of each gene set. We study the performance of our proposed method compared with other GSA methods through simulation studies under various scenarios combining with four different normal-ization methods. As a result, we found that our proposed method outperforms the others in terms of Type I error rate and empirical power.4583901 bytesapplication/pdf論文公開時間: 2019/7/26論文使用權限: 同意有償授權(權利金給回饋學校)RNA-Seq基因集分析差異表現DECO理論相關偏差gene set analysisdifferentially expressedDECOcorrelation biasthesis10.6342/NTU201601170http://ntur.lib.ntu.edu.tw/bitstream/246246/275882/1/ntu-105-R03621207-1.pdf