Large sets of data, like expression profile from many samples, require analytic tools to reduce their complexity. Classical (bi-)clustering algorithms typically attribute elements (genes, arrays) to disjoint groups ("clusters"). Yet, in some cases overlapping cluster assignments would suit the biological reality much better.
The Iterative Signature Algorithm (ISA) was designed to overcome this and other limitations of standard clustering algorithms. It aims to reduce the complexity of very large sets of data by decomposing it into so-called "modules". In the context of gene expression data these modules consist of subsets of genes that exhibit a coherent expression profile only over a subset of microarray experiments. Genes and arrays may be attributed to multiple modules and the level of required coherence can be varied resulting in different "resolutions" of the modular mapping. Since the ISA does not rely on the computation of correlation matrices (like many other tools), it is extremely fast even for very large datasets.
ExpressionView is an R package that provides an interactive environment to explore biclusters identified in gene expression data. A sophisticated ordering algorithm is used to present the biclusters in a visually appealing layout. From this overview, the user can select individual biclusters and access all the biologically relevant data associated with it. The package is aimed to facilitate the collaboration between bioinformaticians and life scientists who are not familiar with the R language.