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Multivariate approach to the analysis of correlated RNA-seq data
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 박태성 | - |
dc.contributor.author | 박현진 | - |
dc.date.accessioned | 2017-07-19T08:48:24Z | - |
dc.date.available | 2017-07-19T08:48:24Z | - |
dc.date.issued | 2017-02 | - |
dc.identifier.other | 000000141736 | - |
dc.identifier.uri | https://hdl.handle.net/10371/131339 | - |
dc.description | 학위논문 (석사)-- 서울대학교 대학원 : 통계학과, 2017. 2. 박태성. | - |
dc.description.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. | - |
dc.description.tableofcontents | 1 Introduction 1
1.1 Background 1 1.2 Purpose 2 2 Material and Methods 3 2.1 Real RNA-seq datasets 3 2.1.1 Diet data 3 2.1.2 Toxicity data 4 2.2 Review of commonly used approach 5 2.2.1 edgeR 5 2.2.2 DESeq 5 2.2.3 limma+voom 6 2.3 Proposed approach : GEE method 7 3 Simulations 9 3.1 Simulation Settings 10 3.1.1 Different number of DEGs 10 3.1.2 Different value of φ 10 3.1.3 Different number of correlated datasets 10 3.2 Results of Simulation 11 4 Application to Real Data 15 5 Discussion 19 Bibliography 21 초록 23 | - |
dc.format | application/pdf | - |
dc.format.extent | 570888 bytes | - |
dc.format.medium | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | 서울대학교 대학원 | - |
dc.subject | RNA-seq | - |
dc.subject | DEGs | - |
dc.subject | Multivariate | - |
dc.subject.ddc | 519 | - |
dc.title | Multivariate approach to the analysis of correlated RNA-seq data | - |
dc.type | Thesis | - |
dc.contributor.AlternativeAuthor | Hyunjin Park | - |
dc.description.degree | Master | - |
dc.citation.pages | 29 | - |
dc.contributor.affiliation | 자연과학대학 통계학과 | - |
dc.date.awarded | 2017-02 | - |
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