Edinburgh Napier University

  Microarray time-course data relate to the recorded activity of thousands of genes, in parallel, over multiple discrete points in time during a biological process. Existing techniques that attempt to support the exploratory analysis of this data rely on static clustering views, interactive clustering views or coordinated clustering and graph views and are limited in that they fail to account for less dominant patterns in the data such as those that involve a subset of genes or a limited interval of the time-course. In this paper, we describe an alternative approach which avoids this limitation by using combined parallel views to present different complementary aspects of the data (i.e. timing, activity and change-in-activity). An example of how the views are combined to reveal significant patterns in the data (including those which cannot be found using clustering based techniques) is described and used to illustrate the benefits of combined parallel views to support exploratory-analysis of this type of data.