Exploratory and Inferential Analysis of Gene Cluster Neighborhood Graphs

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. In this paper recent extensions of R package
gcExplorer are presented. gc-Explorer 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. It is shown that gcExplorer 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.