Bayesian Hierarchical Clustering (BHC) is a simple, fast clustering algorithm capable of partitioning datasets made up of multiple static measurements or time courses whilst requiring minimal parameter input.
A number of different clustering approaches capable of handling time course data exist, with examples including SplineCluster (Heard et al., 2005) and BHC (Cooke et al., 2011). Typically, such methods will use some form of assessing co-expression, be it through a direct distance metric or comparing the resulting parameters of the gene expression being modelled in some way. However, this leaves open the query of identifying the upstream regulators responsible for the observed co-expression. TCAP sets out to answer this query by using a complex, information-rich distance measure when performing its clustering, which captures a complete regulatory interaction package within its clusters instead of merely co-expression.
Performing an analysis of large-scale expression data can be a daunting task, as any relevant information on the effects of the monitored treatment are diluted by tens of thousands of profiles that are left unaffected by the condition change. The Warwick team of CyVerse UK set out to create a number of apps that can get you from normalised expression time course data to concise biological hypotheses on regulatory functionality.