Abstract

Speed and Yang with Smyth (2008) outlined six main phases in genomics, proteomics and metabolomics microarray experiments. They suggested that statisticians could assist in the design of experiments in each phase of such an experiment. That being the case, the experiments potentially involve multiple randomizations (Brien and Bailey, 2006) and are multiphase. Consequently, a multiphase framework for their design will be explored, the first step of which is to list out the phases in the experiment. One set of six phases for the physical conduct of microarray experiments will be described and the sources of variability that affect these phases discussed. The multiphase design of an example microarray experiment will be investigated, beginning with the simplest option of completely randomizing in every phase and then examining the consequences of batching in one or more phases and of not randomizing in all phases. To examine the properties of a design, a mixed model and ANOVA that include terms and sources for all the identified phases will be derived. For this, the factor-allocation description of Brien, Harch, Correll and Bailey (2011) will be used. It is argued that the multiphase framework used is flexible, promotes the consideration of randomization in all phases and facilitates the identification of all sources of variability at play in a microarray experiment.