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A non-parametric and information theoretic algorithm for identifying differentially expressed genes in multiclass RNA-seq samples
다중 부류의 RNA-seq 표본에서 다르게 발현된 유전자를 확인하기 위한 비모수, 정보 이론적 알고리즘

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Authors
안재현
Advisor
김선
Major
공과대학 전기·컴퓨터공학부
Issue Date
2014-02
Publisher
서울대학교 대학원
Keywords
Differentially expressed genesinformation theoretic approachmulticlassRNA-seq
Description
학위논문 (석사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2014. 2. 김선.
Abstract
Gene expression in the whole cell can be routinely measured by microarray technologies or recently by using sequencing technologies. Using these technologies, identifying Differentially Expressed Genes (DEGs) among multiple phenotypes is one of the most important tasks in biology. Thus many methods for detecting DEGs between two groups has been developed. For example, T-test and relative entropy are used for detecting the difference between two probability distributions. When more than two phenotypes are considered, these methods are not applicable and other methods such as ANOVA F-test and Kruskal-Wallis are used for finding DEGs in the multiclass data. However, ANOVA F-test assumes a normal distribution and it is not designed to identify DEGs where gene are expressed distinctively in each of phenotypes. Kruskal-Wallis method, a non-parametric method, is more robust but sensitive to outliers. This thesis proposes a non-parametric and information theoretical approach for identifying DEGs. Our method can identify DEGs in the multiple class data and is less sensitive to outliers. In extensive experiments with simulated and real data, our method outperformed existing tools. In addition, a web service is implemented for the analysis of multi-class data: http://biohealth.snu.ac.kr/software/degselection
Language
English
URI
https://hdl.handle.net/10371/123030
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Electrical and Computer Engineering (전기·정보공학부)Theses (Master's Degree_전기·정보공학부)
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