A novel coherence measure for discovering scaling biclusters from gene expression data.

Biclustering methods are used to identify a subset of genes that are co-regulated in a subset of experimental conditions in microarray gene expression data. Many biclustering algorithms rely on optimizing mean squared residue to discover biclusters from a gene expression dataset. Recently it has been proved that mean squared residue is only good in capturing constant and shifting biclusters. However, scaling biclusters cannot be detected using this metric. In this article, a new coherence measure called scaling mean squared residue (SMSR) is proposed. Theoretically it has been proved that the proposed new measure is able to detect the scaling patterns effectively and it is invariant to local or global scaling of the input dataset. The effectiveness of the proposed coherence measure in detecting scaling patterns has been demonstrated experimentally on artificial and real-life benchmark gene expression datasets. Moreover, biological significance tests have been conducted to show that the biclusters identified using the proposed measure are composed of functionally enriched sets of genes.