Leveraging additional knowledge to support coherent bicluster discovery in gene expression data

Abstract

The increasing availability of gene expression data has encouraged the development of purposely-built intelligent data analysis techniques. Grouping genes characterized by similar expression patterns is a widely accepted – and often mandatory – analysis step. Despite the fact that a number of biclustering methods have been developed to discover clusters of genes exhibiting a similar expression profile under a subgroup of experimental conditions, approaches driven by similarity measures based on expression profiles alone may lead to groups that are biologically meaningless. The integration of additional information, such as functional annotations, into biclustering algorithms can instead provide an effective support for identifying meaningful gene associations. In this paper we propose a new biclustering approach called Additional Information Driven Iterative Signature Algorithm, AID-ISA. It supports the extraction of biologically relevant biclusters by leveraging additional knowledge. We show that AID-ISA allows the discovery of coherent biclusters in baker's yeast and human gene expression data sets.