Experimental correlation analysis of bicluster coherence measures and gene ontology information

Biclustering algorithms have become popular tools for gene expression data analysis. They can identify local patterns defined by subsets of genes and subsets of samples, which cannot be detected by traditional clustering algorithms. In spite of being useful, biclustering is an NP-hard problem. Therefore, the majority of biclustering algorithms look for biclusters optimizing a pre-established coherence measure. Many heuristics and validation measures have been proposed for biclustering over the last 20 years. However, there is a lack of an extensive comparison of bicluster coherence measures on practical scenarios. To deal with this lack, this paper experimentally analyzes 17 bicluster coherence measures and external measures calculated from information obtained in the gene ontologies. In this analysis, results were produced by 10 algorithms from the literature in 19 gene expression datasets. According to the experimental results, a few pairs of strongly correlated coherence measures could be identified, which suggests redundancy. Moreover, the pairs of strongly correlated measures might change when dealing with normalized or non-normalized data and biclusters enriched by different ontologies. Finally, there was no clear relation between coherence measures and assessment using information from gene ontology.