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研究生： 黃志龍
Jhih-Long Huang
論文名稱： 由細胞週期表現資料中尋找延遲調控的基因
Identifying co-regulated gene group from time-lagged gene cluster using cell cycle expression data
指導教授： 洪炯宗
Jorng-Tzong Horng
口試委員:
學位類別： 碩士
Master
系所名稱： 資訊電機學院 - 資訊工程學系
Department of Computer Science & Information Engineering
畢業學年度： 94
語文別： 英文
論文頁數： 48
中文關鍵詞： 微陣列 、相互調控基因 、基因表現 、細胞週期表現資料
外文關鍵詞： cell cycle expression data, gene expression, microarray, co-regulated gene
相關次數： 點閱：9  下載：0 
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    • 我們依循 q-cluster 方法的概念，希望能更進一步找出在細胞週期中相互調控的基因。我們應用我們的方法在酵母菌的基因表現資料上，為了驗證是否我們方法所得到的群落具有統計上的生物意義，我們透過計算超幾何分配來顯示，經過我們計算後，發現的確有某些群落與某個特殊功能有高度相關，除此之外，我們也發現有些群落對於尋找調控關係同樣有顯著的幫助。為了驗證我們的方法是否能正確將相關的基因歸類在一起，我們就跟現存的某些測量方法做比較，結果顯示我們方法的確將那些測量方法視為不相關，但卻是相關的基因歸類在一起。

    We propose a method follows q-cluster’s(Ji and Tan 2005) concept and further advance in finding cell-cycle regulated genes for cell cycle microarray data. We used our method to cluster for time series Yeast gene data. To assess the statistically biological significance of the obtained clusters, we used the P-value obtained from hypergeometric distribution to reveal. We found that several clusters showed a significant enrichment of genes of a particular functional category. Besides, several clusters further facilitate findings for TF-target relationships. In order to test whether our method could group related genes which other method is hard to group together. We compare our method with some measures such as Spearman Rank Correlation, Pearson Correlation and Event Method. The result of comparison demonstrates that our method indeed could group known related genes which these measures regard as weak association among them instead.

    Chapter 1 Introduction 1 1.1 Background 1 1.1.1 Microarray 1 1.1.2 Biclustering in time series expression data 2 1.1.3 Suffix tree 6 1.2 Motivation 7 1.3 Goal 7 Chapter 2 Related Works 8 2.1 Identifying time-lagged genes in gene expression data 8 2.2 Identifying periodically expressed genes 10 2.3 Biclusters in gene expression data 10 Chapter 3 Methods and Materials 12 3.1 Materials 12 3.2 System flow 12 3.3 Phase 1: Transform expression data into event strings 13 3.4 Phase 2: Using discretized matrix to construct the generalized tree 15 3.5 Phase 3: Obtain what similar patterns(items) occur simultaneously at same genes(transactions) 16 3.6 Phase 4: Further group genes according similar patterns’ positions 19 3.7 Phase 5: Functional enrichment 22 Chapter 4 Results 23 4.1 Clustering for genes with same or similar function 23 4.2 Comparison of other studies 27 4.3 Clustering for finding time-lagged co-regulated genes 30 Chapter 5 Conclusions 35 References 37

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