ASIA unversity:Item 310904400/2624
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    题名: A systematic gene expression explorer tool for multiple and paired chips
    作者: Jong-Waye Ou
    贡献者: Department of Bioinformatics
    关键词: Multiple Chips;Paired Chips;Pathway Map;Gene Expression
    日期: 2008
    上传时间: 2009-11-06 14:32:58 (UTC+0)
    出版者: Asia University
    摘要: ABSTRACT
    The Gene Expression Explorer (GEE) Tool is an effective tool for analyzing quantitative gene expression profiles for multiple chips, paired chips, and pathway map. The GEE tool is developed using C++. With this program it is possible to (a) analyze the expression of individual genes, (b) analyze the expression of gene clusters, (c) analyze the histogram of gene expression pattern across multiple chips, (d) analyze differential gene expression with multiple data, (e) analyze the paired chip gene expression of histogram and ratios, (f) analyze the scatter plot of paired gene expression ratios, (g) analyze the pathway map with gene expression cluster. Analyses are performed in real-time and may be viewed and directly manipulated in images, scatter plots, histograms, expression profile plots and cluster analyses plots. The expression gene analysis results can be exported to text files, and the histogram chart of the gene expression pattern can be exported to a bitmap file. We have also applied the GEE tool to analyze multiple chips of 48 patients with liver primary tumor and non-tumor tissue. We have identified gene expression clustering that is over-expression, under-expression and no significant differential expression. Various user-defined thresholds of gene expression level will result in different gene expression clusters. Paired-chip analysis is a histogram of the distribution of gene expression ratios for hybridizations comparing gene expression. We have standard statistical frequency quantity to determine differential gene expression. The histogram is displayed in real time for user observation. Finally, the results of the analysis can be interpreted by enrichment of pathway analysis. Therefore, we connect the gene expression clusters to pathway map to allow the visualization of analysis results on metabolic pathways.
    显示于类别:[生物資訊與醫學工程學系 ] 博碩士論文

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