A nested unsupervised approach to identifying novel molecular subtypes

Abstract

In classification problems arising in genomics research it is common to study populations for which a broad class assignment is known (say, normal versus diseased) and one seeks undiscovered subclasses within one or both of the known classes. Formally, this problem can be thought of as an unsupervised analysis nested within a supervised one. Here we take the view that the nested unsupervised analysis can successfully utilize information from the entire data set for constructing and/or selecting useful predictors. Specifically, we propose a mixture model approach to the nested unsupervised problem, where the supervised information is used to develop latent classes which are in turn used for data mining and robust unsupervised analysis. Our solution is illustrated using data on molecular classification of lung adenocarcinoma.