Clustering DNA Microarray Data

Abstract

Proper interpretation of results of clustering of gene expression data from DNA microarray tests is one of major challenges in experiment data analysis. Interpretation problems arise due to the fact that different algorithms tend to produce different results, while some clusters appear to be invariant of an algorithm applied. A procedure described in this work can be a good starting point for a decision making process to evaluate biological relevance of clustering results obtained. In our view, any other similar approach aiming to discover biologically relevant clusters will have to include biological information. It would be probably beneficial if relevant biological knowledge could be incorporated on the input side of clustering algorithm rather than at the results post processing / interpretation stage, as described in this work. Making clustering algorithms make clustering decision biased towards biologically relevant groupings, thus forming ‘supervised clustering’ approach may be a motivation for further research in this area.