Clustering gene expression data with a penalized graph-based metric

Background: The search for cluster structure in microarray datasets is a base problem for the so-called “-omic sciences”. A difficult problem in clustering is how to handle data with a manifold structure, i.e. data that is not shaped in the form of compact clouds of points, forming arbitrary shapes or paths embedded in a highdimensional space, as could be the case of some gene expression datasets. Results: In this work we introduce the Penalized k-Nearest-Neighbor-Graph (PKNNG) based metric, a new tool for evaluating distances in such cases. The new metric can be used in combination with most clustering algorithms. The PKNNG metric is based on a two-step procedure: first it constructs the k-Nearest-Neighbor-Graph of the dataset of interest using a low k-value and then it adds edges with a highly penalized weight for connecting the subgraphs produced by the first step. We discuss several possible schemes for connecting the different sub-graphs as well as penalization functions. We ...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Ariel E. Bayá, Pablo M. Granitto
• Keywords: Arbitrary Shapes, Biometrics, BMCBI 2011, Dendrogram, Expression Datasets