Techniques for clustering gene expression data

G Kerr; H J Ruskin; M Crane; P Doolan

doi:10.1016/j.compbiomed.2007.11.001

Techniques for clustering gene expression data

Comput Biol Med. 2008 Mar;38(3):283-93. doi: 10.1016/j.compbiomed.2007.11.001. Epub 2007 Dec 3.

Authors

G Kerr¹, H J Ruskin, M Crane, P Doolan

Affiliation

¹ Biocomputation Research Lab (Modelling and Scientific Computing Group, School of Computing) and National Institute of Cellular Biotechnology, Dublin City University, Dublin 9, Ireland. gkerr@computing.dcu.ie

PMID: 18061589
DOI: 10.1016/j.compbiomed.2007.11.001

Abstract

Many clustering techniques have been proposed for the analysis of gene expression data obtained from microarray experiments. However, choice of suitable method(s) for a given experimental dataset is not straightforward. Common approaches do not translate well and fail to take account of the data profile. This review paper surveys state of the art applications which recognise these limitations and addresses them. As such, it provides a framework for the evaluation of clustering in gene expression analyses. The nature of microarray data is discussed briefly. Selected examples are presented for clustering methods considered.

Publication types

Review

MeSH terms

Algorithms*
Cluster Analysis
Fuzzy Logic
Gene Expression Profiling / methods*
Models, Statistical
Neural Networks, Computer
Oligonucleotide Array Sequence Analysis / methods*
Stochastic Processes