以經驗模態分解改善時序基因晶片數據的週期訊號偵測與一個迴圈式實驗設計的基因晶片數據分析平台

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研究生：	陳昶瑞 Chen, Chaang Ray
論文名稱：	以經驗模態分解改善時序基因晶片數據的週期訊號偵測與一個迴圈式實驗設計的基因晶片數據分析平台 Empirical mode decomposition improved periodicity detection for time-series microarray analysis and a web tool for loop-design microarray data analysis
指導教授：	許志楧 Hsu, Ian C.
口試委員:	許志楧江啟勳許文郁黃鍔黃鎮剛
學位類別：	博士 Doctor
系所名稱：	原子科學院 - 生醫工程與環境科學系 Department of Biomedical Engineering and Environmental Sciences
論文出版年：	2015
畢業學年度：	103
語文別：	英文
論文頁數：	126
中文關鍵詞：	經驗模態分解、基因晶片、時序基因晶片、迴圈式實驗設計、週期性表現基因、蛋白質共同表現網路
外文關鍵詞：	Empirical mode decomposition, microarray, time-series microarray, loop-design, periodically expressed gene, protein co-expression network
相關次數：	點閱：1 下載：0
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隨著微陣列基因晶片的科技進步，我們可以同時量測多達數萬個基因的表現。藉由在時序基因晶片實驗中尋找與探討具有週期表現的基因，可以幫助我們進一步了解，基因在轉譯體內所扮演的重要角色、以及其調控的機制。但是，時序基因晶片數據充滿著大量的雜訊。我們目前並沒有深入的了解，時序數據的結構是如何影響週期性訊號偵測方法的效能表現。

本論文針對在時序基因晶片數據分析中，所面對的主要問題，並提出以經驗模態分解為基礎的新計算方法來探討這些難題。這些方法包括了：(1) 一個量化時序數據中震動複雜度的量測指標、(2) 一個在時序數據中搜尋週期性結構模式的演算方法，以及 (3) 建構週期性表現基因的蛋白質共同表現網路。

我們將這些新方法應用一組已發表、探討酵母菌代謝週期的實驗數據上。我們解析每個基因時序數據中的本徵模式函數，並評估週期性訊號偵測方法的效能表現。我們的結果顯示，在原始發表的數據分析結果中，可能遺漏了多達1,469個週期表現的基因。而造成無法辨識這些週期表現基因的原因是：從時序數據中解析得出的本徵模式函數之間的干擾。這些無法被辨識的週期表現基因的功能以核醣體新生、核醣核酸加工為主。另外，藉由進一步分析部分基因的蛋白質共同表現網路，我們驗證了其中56個週期表現基因。我們的結論顯示，經驗模態分解可以應用於時序基因晶片數據分析，改善既有的週期性訊號偵測方法。在未來，還可以應用於其他的時序基因晶片數據。

另外，在過去的文獻中曾指出，相較於基準參考式設計，迴圈式設計是更有效率、更可行的基因晶片實驗設計方法。但是，目前應用於迴圈設計基因晶片實驗的網站式數據分析工具是有限的。因此，我們依據迴圈設計基因晶片實驗所需求的統計與演算方法，建構了一個數據分析網站, THEME。它提了完整的工具，用來評估與視覺化基因晶片實驗數據的品質控管。透過直覺化的操作介面與流程，讓以生物學家為主的使用者，從上傳原始數據、到藉由複雜的統計分析來篩選具有顯著差異表現的基因，都可以輕鬆地完成。

With recent advances of microarray technology, we are able to monitor the expression of thousands of genes simultaneously. Detecting periodic signals from time-series microarray data is commonly used to facilitate the understanding of the critical roles and underlying mechanisms of regulatory transcriptomes. However, time-series microarray data is noisy. In particular, how the temporal data structure affects the performance of periodicity detection has remained elusive.

In this dissertation, we address key issues in time-series gene expression analysis. We present novel computational methods based on empirical mode decomposition (EMD) to meet the challenges in three areas (1) a metric to measure the complexity of oscillatory nature of time-series, (2) an algorithm to search periodic patterns from time-series, and (3) protein complex co-expression networks of periodically expressed genes.

We applied these methods to the yeast metabolic cycle (YMC) dataset to extract a series of intrinsic mode function (IMF) oscillations from the time-series data and to evaluate performance of periodicity detection methods. Our results show that 1,469 periodically expressed genes might have been under-detected in the original analysis because of interference between decomposed IMF oscillations. Most of the under-detected genes were mainly associated with ribosome biogenesis and RNA processing. Additionally, validated by our protein complex coexpression analysis, we confirmed that 56 genes were newly determined as periodic. We demonstrated that EMD can be used incorporating with existing periodicity detection methods to improve their performance. Our EMD approach can be applied to other time-series microarray studies.

In addition, a number of recent studies have shown that loop-design is more efficient than reference control design. However, limited loop-design web-based tools are available. We have developed the THEME that exploits all necessary data analysis tools for loop-design microarray studies. This web platform provides data assessment and visualization tools to evaluate the performance of microarray experimental procedures. Data analysis procedures, starting from uploading raw data files to retrieving DEG lists, can be flexibly operated with natural workflows with THEME.

Abstract ...................................i
摘要 .......................................ii
致謝 .......................................iii
Table of Content ...........................iv
1    Background                                                     1
    1.1    Microarray Technology....................................2
        1.1.1 Biological Background ................................2
        1.1.2 Experimental Design ..................................4
        1.1.3 Gene Expression Analysis .............................6
    1.2    Time-series Microarray Analysis ........................16
    1.3    Empirical Mode Decomposition ...........................18
    1.4    Protein Co-expression ..................................19    
    1.5    Gene Enrichment Analysis ...............................20
    1.6    Our Approach ...........................................21
2    Analyzing Time-series Microarray data Using Empirical Mode Decomposition                                                      23
    2.1    Introduction ...........................................23
    2.2    Methods ................................................26
        2.2.1 Microarray Data Preprocessing .......................26
        2.2.2 Empirical Mode Decomposition ........................27
        2.2.3 Periodicity Detection ...............................27
        2.2.4 Protein Complex Co-expression analysis ..............28
        2.2.5 Gene Enrichment Analysis ............................29
    2.3    Results ................................................29
        2.3.1 Reanalysis of the Yeast Metabolic Cycle Dataset .....29
        2.3.2 Non-periodic Probesets Exhibit a Higher Degree of
              Oscillations ........................................30
        2.3.3 Identification of Under-detected Periodicity ........31
        2.3.4 Validation of Under-detected Genes ..................32
        2.3.5 Most Under-detected Genes Were Associated with the
              Oxidative Phase of the Metabolic Cycle ..............35
    2.4 Discussion ................................................37
    2.5 Conclusions ...............................................41
3    THEME: a web tool for loop-design microarray data analysis    43
    3.1    Introduction ...........................................43
    3.2    Statistical models and hypothesis test .................46
        3.2.1 Log linear model ....................................46
        3.2.2 Hypothesis testing ..................................49
    3.3    Results ................................................51
        3.3.1 Laboratory Information Management System ............51
        3.3.2 Data quality assessment .............................53
        3.3.3 Detection of differentially expressed genes .........55
        3.3.4 Implementation ......................................57
        3.3.5 Usage and documentation .............................58
        3.3.6 THEME supported publications ........................59
    3.4    Discussion .............................................65
    3.5    Conclusions ............................................67
4    Conclusions                                                   69
    4.1    Summary ................................................69
    4.2    Limitations and Future Directions ......................70
Figures ...........................................................75
Tables ...........................................................115
References .......................................................119

                                

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