Application of statistical methods on microarray analysis of the α1H T-type calcium channel knockout mice

• Main Authors: Yu-Ling Jung, 鍾玉鈴
• Other Authors: Meng-Hsiun Tsai
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/f65hqh

碩士 === 國立中興大學 === 基因體暨生物資訊學研究所 === 100 === 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.