- Poster presentation
- Open Access
Serendipitous discoveries in microarray analysis
© Ellingson et al; licensee BioMed Central Ltd. 2010
- Published: 23 July 2010
- Microarray Experiment
- Similar Expression Pattern
- Cerebellar Development
- Gene Expression Experiment
- Candidate Cluster
Scientists are capable of performing very large scale gene expression experiments with current microarray technologies. In order to find significance in the expression data, it is common to use clustering algorithms to group genes with similar expression patterns. Clusters will often contain related genes, such as co-regulated genes or genes in the same biological pathway. It is too expensive and time consuming to test all of the relationships found in large scale microarray experiments. There are many bioinformatics tools that can be used to infer the significance of microarray experiments and cluster analysis.
In this project we review several existing tools and used a combination of them to narrow down the number of significant clusters from a microarray experiment. Microarray data was obtained through the Cerebellar Gene Regulation in Time and Space (Cb GRiTS) database . The data was clustered using paraclique, a graph-based clustering algorithm. Each cluster was evaluated using Gene-Set Cohesion Analysis Tool (GCAT) , ONTO-Pathway Analysis , and Allen Brain Atlas data . The clusters with the lowest p-values in each of the three analysis methods were researched to determine good candidate clusters for further experimental confirmation of gene relationships.
While looking for genes important to cerebellar development, we serendipitously came across interesting clusters related to neural diseases. For example, we found two clusters that contain genes known to be associated with Parkinson’s disease, Huntington’s disease, and Alzheimer’s disease pathways. Both clusters scored low in all three analyses and have very similar expression patterns but at different expression levels. Such unexpected discoveries help unlock the real power of high throughput data analysis.
- Allen Brain Atlas: Home. Allen Institute for Brain Science. Web 2009. [http://www.brain-map.org/]Google Scholar
- Cb GRiTS Database. Web 2009. [http://grits.dglab.org]
- GCAT: Gene-set Cohesion Analysis Tool. The University of Memphis. Web 2009. [http://binf1.memphis.edu/gcat/]Google Scholar
- Intelligent Systems and Bioinformatics Laboratory. Web 2009. [http://vortex.cs.wayne.edu/ontoexpress/]
This article is published under license to BioMed Central Ltd.