Interactive visualization of clusters in microarray data: an efficient tool for improved metabolic analysis of E. coli

作     者：Scharl, Theresa Striedner, Gerald Poetschacher, Florentina Leisch, Friedrich Bayer, Karl 

作者机构：Univ Nat Resources & Appl Life Sci Dept Biotechnol A-1190 Vienna Austria Vienna Univ Technol Dept Stat & Probabil Theory A-1040 Vienna Austria Univ Munich Dept Stat D-80539 Munich Germany 

出 版 物：《MICROBIAL CELL FACTORIES》 (微生物细胞工厂)

年 卷 期：2009年第8卷第1期

页      面：37-37页

核心收录：

学科分类：0710[理学-生物学] 07[理学] 071009[理学-细胞生物学] 09[农学] 0901[农学-作物学] 0836[工学-生物工程] 090102[农学-作物遗传育种] 

基　　金：Austrian Kind/Knet Center of Biopharmaceutical Technology (ACBT) 

主　　题：Microarray Data Cluster Solution Limited Induction Cluster Centroid Neighborhood Graph 

摘      要：Background: Interpretation of comprehensive DNA microarray data sets is a challenging task for biologists and process engineers where scientific assistance of statistics and bioinformatics is essential. Interdisciplinary cooperation and concerted development of software-tools for simplified and accelerated data analysis and interpretation is the key to overcome the bottleneck in data-analysis workflows. This approach is exemplified by gcExplorer an interactive visualization toolbox based on cluster analysis. Clustering is an important tool in gene expression data analysis to find groups of co-expressed genes which can finally suggest functional pathways and interactions between genes. The visualization of gene clusters gives practitioners an understanding of the cluster structure of their data and makes it easier to interpret the cluster results. Results: In this study the interactive visualization toolbox gcExplorer is applied to the interpretation of E. coli microarray data. The data sets derive from two fedbatch experiments conducted in order to investigate the impact of different induction strategies on the host metabolism and product yield. The software enables direct graphical comparison of these two experiments. The identification of potentially interesting gene candidates or functional groups is substantially accelerated and eased. Conclusion: It was shown that gcExplorer is a very helpful tool to gain a general overview of microarray experiments. Interesting gene expression patterns can easily be found, compared among different experiments and combined with information about gene function from publicly available databases.