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.
The Iterative Signature Algorithm (ISA) was designed 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.
The Ping-Pong Algorithm (PPA) is a method for integrating tabular data sets that share a common dimension. A typical example for this is gene expression across a set of tissues and drug-response data for the same tissues. The PPA finds co-modules in the two data sets, i.e. genes that are co-expressed in some tissues that have similar responses to a subset of the drugs. The co-modules can overlap; the PPA is efficient and scales well to large data sets. A Matlab implementation is provided.