Exploiting the Geometry of Gene Expression Patterns for Unsupervised Learning

Typical gene expression clustering algorithms are restricted to a specific underlying pattern model while overlooking the possibility that other information carrying patterns may co-exist in the data. This may potentially lead to a large bias in the results. In this paper we discuss a new method that is able to cluster simultaneously various types of patterns. Our method is based on the observation that many of the patterns that are considered significant to infer gene function and regulatory mechanisms all share the geometry of linear manifolds