Inference of data from multiple cell-lines or multiple time points typically relies on a single clustering and does not incorporate uncertainty in microarray data. We propose methodology to analyse these data that incorporates our error models and provides confidence statements about the classification of genes of unknown function. The value of the methodology is illustrated via simulations and on real datasets, and applied to k-means and hierarchical clustering. There is a need for statistical methods to address this question. The potential problems are formidable.
Analysis is likely to be of an exploratory nature as there are so many potential questions.
There will be multiple testing dilemmas. Phenotypes will be described as multivariate observations and can arise by divergent mechanisms so the statistical methods need to be able to handle mixture data. Interactions are expected to abound. Our idea is to borrow and modify data mining methods to address these problems. Multiple testing will be addressed.