Annotation-based distance measures for patient subgroup discovery in clinical microarray studies

Abstract

Background: Clustering algorithms are widely used in the analysis of microarray data. In clinical studies, they are often applied to find groups of co-regulated genes. Clustering, however, can also stratify patients by similarity of their gene expression profiles, thereby defining novel disease entities based on molecular characteristics. Several distance-based cluster algorithms have been suggested, but little attention has been given to the choice of the distance measure between patients. Even with the Euclidean metric, including and excluding genes from the analysis leads to different distances between the same objects, and consequently different clustering results. Methodology: We describe a novel clustering algorithm, in which gene selection is used to derive biologically meaningful clusterings of samples. Our method combines expression data and functional annotation data. According to gene annotations, candidate gene sets with specific functional characterizations are generated. Each set defines a different distance measure between patients, and consequently different clusterings. These clusterings are filtered using a novel resampling based significance measure. Significant clusterings are reported together with the underlying gene sets and their functional definition. Conclusions: Our method reports clusterings defined by biologically focused sets of genes. In annotation driven clusterings, we have recovered clinically relevant patient subgroups through biologically plausible sets of genes, as well as novel subgroupings. We conjecture that our method has the potential to reveal so far unknown, clinically relevant classes of patients in an unsupervised manner.