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National Chung Hsing University Institutional Repository - NCHUIR > 生命科學院 > 基因體暨生物資訊學研究所 > 依資料類型分類 > 碩博士論文 >  應用統計分析Alpha1H T型鈣離子通道基因剔除小鼠大腦基因晶片

Please use this identifier to cite or link to this item: http://nchuir.lib.nchu.edu.tw/handle/309270000/153794

標題: 應用統計分析Alpha1H T型鈣離子通道基因剔除小鼠大腦基因晶片
Application of statistical methods on microarray analysis of the α1H T-type calcium channel knockout mice
作者: 鍾玉鈴
Jung, Yu-Ling
Contributors: 蔡孟勳
Meng-Hsiun Tsai
關鍵字: 微陣列;鈣離子通道;F值篩選器;變異數分析;皮爾森相關係數
Microarray;calcium channel;F-selector;ANOVA;Pearson correlation coefficient
日期: 2012
Issue Date: 2013-11-19 12:02:17 (UTC+8)
Publisher: 基因體暨生物資訊學研究所
摘要: 腦科學在二十一世紀已成為生命科學的尖端研究領域之一,因為人類的大腦仍存在許多未知的部份,特別是各種腦功能的分子機轉仍不清楚。本論文旨在針對鈣離子通道家族中的α1H T型鈣離子通道在小鼠腦內海馬的基因表達圖譜進行分析,以篩選參與學習與記憶之重要分子群。經生物實驗證實,α1H T型鈣離子通道在海馬中高度表達,且海馬又與情境學習與空間記憶等重要腦功能相關聯,動物實驗也已證實α1H T 型鈣離子通道在依賴海馬學習試驗上扮演一個重要的角色現(Plos one reference);然而,其分子機轉尚不清楚。微陣列目前是用以篩選致病基因群中發展最為成熟的工具,但對於微陣晶片雜合後所獲得的龐大資料該如何分析仍未有一個明確的標準。為了勾勒出α1H T型鈣離子通道影響依賴海馬學習及記憶之基因群及分子路徑,我們針對α1H T 型鈣離子通道基因剔除小鼠大腦基因晶片樣本進行分析。首先對樣本進行正規化,再以變異數分析(ANOVA)找出具有差異性表現的基因作為標靶基因,其中在過去研究上,ANOVA通常都以P-value找到具有差異性表現的基因作為標靶基因,但本研究則採用F值取代傳統的P value尋找標靶基因,進而利用皮爾森相關係數建立網路關係。希望這些標靶基因可進一步的運用在研究腦部科學上,也希望這些調控網路可以提供功能性實驗及建立記憶分子傳遞路徑時較精準的研究方向。
This thesis aims to improve the efficiency and accuracy of microarray analysis on mice brains. Of all the voltage-gated calcium channels, the α1H T-type Ca2+ channel(Cav3.2) is highly expressed in the hippocampus, where is correlated with contextual learning and spatial memory. It has been demonstrated that the Cav3.2 gene plays a critical role in hippocampal-dependent learning. However, the underlying molecular mechanism is yet unclear. Microarray hybridizations have been done for isolated hippocampi of the Cav3.2 knockout mice in order to screen for gene clusters correlated to hippocampal-dependent learning. This research further analyze the microarray data computational algorithms to filter out gene clusters of interest. Min-max normalization was first used to normalize the data. The ANOVA was next applied to detect significance of expression differences among the filtered gene clusters. Finally, Pearson correlation coefficients were calculated to identify the regulatory network of the correlated gene clusters. The advantage of this analytic model is that it approaches complicated gene expression datasets in a less time-consuming way. It also helps biological functions and regulations in relation to targeting genes. In the future, these selected targeting gene clusters can be used for further functional researches in brain science, and these regulatory networks will provide researchers more precise direction of investigation.
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