次世代定序資料之基因富集分析

Gene Set Enrichment Analysis of RNA-Seq data

李沛洵(Pei-Hsun Li)

指導教授 ： 蔡政安

國立臺灣大學/生物資源暨農學院/農藝學研究所/碩士(2016年)

https://doi.org/10.6342/NTU201601170

摘要

無資料

關鍵字

RNA-Seq ； 基因集分析 ； 差異表現 ； DECO理論 ； 相關偏差

並列摘要

During 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.

並列關鍵字

RNA-Seq ； gene set analysis ； differentially expressed ； DECO ； correlation bias

參考文獻

1. Tian, L., et al., Discovering statistically significant pathways in expression profiling studies. Proc Natl Acad Sci U S A, 2005. 102(38): p. 13544-9.

2. Subramanian, A., et al., Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A, 2005. 102(43): p. 15545-50.

3. Wang, Z., M. Gerstein, and M. Snyder, RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet, 2009. 10(1): p. 57-63.

4. Nam, D., De-correlating expression in gene-set analysis. Bioinformatics, 2010. 26(18): p. i511-6.

5. Robinson, M.D. and A. Oshlack, A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol, 2010. 11(3): p. R25.