Class cover catch digraphs for latent class discovery in gene expression monitoring by DNA microarrays

Abstract

The purpose of this article is to introduce a data visualization technique for class cover catch digraphs which allows for the discovery of latent subclasses. We illustrate the technique via a pedagogical example and an application to data sets from artificial nose chemical sensing and gene expression monitoring by DNA microarrays. Of particular interest is the discovery of latent subclasses representing chemical concentration in the artificial nose data and two subtypes of acute lymphoblastic leukemia in the gene expression data and the associated conjectures pertaining to the geometry of these subclasses in their respective high-dimensional observation spaces.