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BMCBI
2011

Clustering gene expression data with a penalized graph-based metric

8 years 3 months ago
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 ...
Ariel E. Bayá, Pablo M. Granitto
Added 24 Aug 2011
Updated 24 Aug 2011
Type Journal
Year 2011
Where BMCBI
Authors Ariel E. Bayá, Pablo M. Granitto
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