Gene expression data classification with Kernel principal component analysis.
One important feature of the gene expression data is that the number of genes M far exceeds the number of samples N. Standard statistical methods do not work well when N < M. Development of new methodologies or modification of existing methodologies is needed for the analysis of the microarray data. In this paper, we propose a novel analysis procedure for classifying the gene expression data. This procedure involves dimension reduction using kernel principal component analysis (KPCA) and classification with logistic regression (discrimination). KPCA is a generalization and nonlinear version of principal component analysis. The proposed algorithm was applied to five different gene expression datasets involving human tumor samples. Comparison with other popular classification methods such as support vector machines and neural networks shows that our algorithm is very promising in classifying gene expression data.
Liu
Zhenqiu
Z
Bioinformatics Cell, US Army Medical Research and Materiel Command, 110 North Market Street, Frederick, MD 21703, USA. liu@stat.ohio-state.edu
Chen
Dechang
D
Bensmail
Halima
H