Edge-weighting of gene expression graphs

Kerr, Grainne, Perrin, Dimitri, Ruskin, Heather J, & Crane, Martin (2010) Edge-weighting of gene expression graphs. Advances in Complex Systems, 13(2), pp. 217-238.

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Abstract

In recent years, considerable research efforts have been directed to micro-array technologies and their role in providing simultaneous information on expression profiles for thousands of genes. These data, when subjected to clustering and classification procedures, can assist in identifying patterns and providing insight on biological processes. To understand the properties of complex gene expression datasets, graphical representations can be used. Intuitively, the data can be represented in terms of a bipartite graph, with weighted edges corresponding to gene-sample node couples in the dataset. Biologically meaningful subgraphs can be sought, but performance can be influenced both by the search algorithm, and, by the graph-weighting scheme and both merit rigorous investigation. In this paper, we focus on edge-weighting schemes for bipartite graphical representation of gene expression. Two novel methods are presented: the first is based on empirical evidence; the second on a geometric distribution. The schemes are compared for several real datasets, assessing efficiency of performance based on four essential properties: robustness to noise and missing values, discrimination, parameter influence on scheme efficiency and reusability. Recommendations and limitations are briefly discussed. Keywords: Edge-weighting; weighted graphs; gene expression; bi-clustering

Impact and interest:

2 citations in Scopus
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ID Code: 82672
Item Type: Journal Article
Refereed: No
DOI: 10.1142/S0219525910002505
ISSN: 1793-6802
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: World Scientific Publishing Company
Deposited On: 24 Mar 2015 03:22
Last Modified: 30 Mar 2015 05:05

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