Picture of a black hole

Strathclyde Open Access research that creates ripples...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of research papers by University of Strathclyde researchers, including by Strathclyde physicists involved in observing gravitational waves and black hole mergers as part of the Laser Interferometer Gravitational-Wave Observatory (LIGO) - but also other internationally significant research from the Department of Physics. Discover why Strathclyde's physics research is making ripples...

Strathprints also exposes world leading research from the Faculties of Science, Engineering, Humanities & Social Sciences, and from the Strathclyde Business School.

Discover more...

Multidimensional partitioning and bi-partitioning: analysis and application to gene expression datasets

Higham, D.J. and Kalna, G. and Vass, J.K. (2008) Multidimensional partitioning and bi-partitioning: analysis and application to gene expression datasets. International Journal of Computer Mathematics, 85 (3/4). pp. 475-485. ISSN 0020-7160

[img]
Preview
PDF (kvh.pdf)
kvh.pdf

Download (203kB) | Preview

Abstract

Eigenvectors and, more generally, singular vectors, have proved to be useful tools for data mining and dimension reduction. Spectral clustering and reordering algorithms have been designed and implemented in many disciplines, and they can be motivated from several dierent standpoints. Here we give a general, unied, derivation from an applied linear algebra perspective. We use a variational approach that has the benet of (a) naturally introducing an appropriate scaling, (b) allowing for a solution in any desired dimension, and (c) dealing with both the clustering and bi-clustering issues in the same framework. The motivation and analysis is then backed up with examples involving two large data sets from modern, high-throughput, experimental cell biology. Here, the objects of interest are genes and tissue samples, and the experimental data represents gene activity. We show that looking beyond the dominant, or Fiedler, direction reveals important information.