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Multivariate approach to the analysis of correlated RNA-seq data

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Authors
박현진
Advisor
박태성
Major
자연과학대학 통계학과
Issue Date
2017
Publisher
서울대학교 대학원
Keywords
RNA-seqDEGsMultivariate
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
URI
http://hdl.handle.net/10371/131339
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College of Natural Sciences (자연과학대학)Dept. of Statistics (통계학과)Theses (Master's Degree_통계학과)
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