Techniques for clustering gene expression data.
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.
Kerr
G
G
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
Ruskin
H J
HJ
Crane
M
M
Doolan
P
P
D000465:Algorithms; D016000:Cluster Analysis; D017143:Fuzzy Logic; D020869:Gene Expression Profiling / Q000379:methods*; D015233:Models, Statistical; D016571:Neural Networks, Computer; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D013269:Stochastic Processes