主成分分析與叢集分析於DNA微陣列數據前處理的應用與實作;Application and Implementation of PCA and Clustering in DNA Microarray data Preprocessing

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Title:	主成分分析與叢集分析於DNA微陣列數據前處理的應用與實作;Application and Implementation of PCA and Clustering in DNA Microarray data Preprocessing
Authors:	張美虹;Chang,Mei-hong
Contributors:	系統生物與生物資訊研究所
Keywords:	微陣列晶片;R語言;主成分分析;叢集分析;Microarray;R language;Principal component analysis;Clustering
Date:	2014-07-14
Issue Date:	2014-10-15 14:45:08 (UTC+8)
Publisher:	國立中央大學
Abstract:	近年來，微陣列基因晶片的分析軟體越來越容易取得，卻也都不盡完善。商用或制式化的分析套件也限制了分析人員的分析效率、使用彈性。此篇論文，為了改善基因晶片數據分析的品質與效率，建構更符合產業自產生物晶片分析流程。透過R統計語言的撰寫進行整合由其他人開發的分析軟體，並以建構了一個進行主成分分析(Principal Component Analysis，簡稱PCA)與叢集分析(Clustering)流程的模組。藉此取代先前使用的多種分析軟體，以自動化於微陣列晶片分析品質評估一環的主成分分析與叢集分析流程為訴求進行實作。在完成分析模組後，自動化分析流程取代了原有耗時的分析過程。將單一個案在此分析步驟所需時間縮減百分之八十以上，操作的簡便也使分析人員容易作業。確實達到了提升分析工作的效率。而R語言撰寫上的彈性，便於在未來建構出符合產業自產生物晶片分析的作業流程。 ;In order to improve the efficiency of quality assessment in microarray data preprocessing, an automated analysis pipeline for Clustering and PCA (principal component analysis) was developed using R language. We successfully replaced the previously use of third-party analysis software, using the automated analysis module and integrated the module into the routine pipeline for microarray analysis. The automated analysis pipeline for Principal Component Analysis and Clustering reduced processing time by almost 80% compared to previous approach, showing that the project goal was met. The R-based package is also flexible enough to be readily incorporated into other bioinformatics applications.
Appears in Collections:	[Institute of Systems Biology and Bioinformatics] Electronic Thesis & Dissertation

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