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Multivariate approach to the analysis of correlated RNA-seq data
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- Authors
- Advisor
- 박태성
- Major
- 자연과학대학 통계학과
- Issue Date
- 2017-02
- Publisher
- 서울대학교 대학원
- Keywords
- RNA-seq ; DEGs ; Multivariate
- Description
- 학위논문 (석사)-- 서울대학교 대학원 : 통계학과, 2017. 2. 박태성.
- Abstract
- High-throughput RNA-seq technology has emerged as a powerful tool for understanding the molecular basis of phenotype variation in biology, including disease. Recently, some correlated RNA-seq datasets started to be generated. While there have been several approaches proposed for identifying the differentially expressed genes (DEGs), not many methods can analyze correlated RNA-seq data. We expect the simultaneous analysis of correlated RNA-seq data to increase of power of detecting DEGs. In this paper, we propose a multivariate method to find DEGs on correlated RNA-seq data based on the Generalized Estimating Equations (GEE) approach. The advantage of the proposed method is to consider correlated RNA-seq data simultaneously while accounting for correlations. Through real data analysis and simulation studies, we show that our multivariate approach has higher power of detecting DEGs than the existing methods.
- Language
- English
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