Exploratory and inferential analysis of gene cluster neighborhood graphs
<p>Abstract</p> <p>Background</p> <p>Many different cluster methods are frequently used in gene expression data analysis to find groups of co-expressed genes. However, cluster algorithms with the ability to visualize the resulting clusters are usually preferred. The vis...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2009-09-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/10/288 |
| Summary: | <p>Abstract</p> <p>Background</p> <p>Many different cluster methods are frequently used in gene expression data analysis to find groups of co-expressed genes. However, cluster algorithms with the ability to visualize the resulting clusters are usually preferred. 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.</p> <p>Results</p> <p>In this paper recent extensions of <monospace>R</monospace> package <b>gcExplorer </b>are presented. <b>gcExplorer </b>is an interactive visualization toolbox for the investigation of the overall cluster structure as well as single clusters. The different visualization options including arbitrary node and panel functions are described in detail. Finally the toolbox can be used to investigate the quality of a given clustering graphically as well as theoretically by testing the association between a partition and a functional group under study.</p> <p>Conclusion</p> <p>It is shown that <b>gcExplorer </b>is a very helpful tool for a general exploration of microarray experiments. The identification of potentially interesting gene candidates or functional groups is substantially accelerated and eased. Inferential analysis on a cluster solution is used to judge its ability to provide insight into the underlying mechanistic biology of the experiment.</p> |
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| ISSN: | 1471-2105 |