Nonlinear Dimensionality Reduction for Cluster Identification in Metagenomic Samples

Abstract

We investigate the potential of modern nonlinear dimensionality reduction techniques for an interactive cluster detection in bioinformatics applications. We demonstrate that recent non-parametric techniques such as t-distributed stochastic neighbor embedding (t-SNE) allow a cluster identification which is superior to direct clustering of the original data or cluster detection based on classical parametric dimensionality reduction approaches. Non-parametric approaches, however, display quadratic complexity which makes them unsuitable in interactive devices. As speedup, we propose kernel-t-SNE as a fast parametric counterpart based on t-SNE.