Microarrays, pattern recognition and exploratory data analysis.
We explore the application of principal component based approaches to pattern recognition in microarray analysis. A comparative assessment is presented, based on predictive evaluation, following a review of some key methodology. On the basis of these results, we select principal component based linear discrimination for a further in-depth analysis. We are particularly interested in studying the use of principal component decomposition for the evaluation of the condition of estimation of the pooled covariance matrix. The results are used to give some guidance as to how principal components may be used as a data exploratory tool in the analysis of microarray data. Opportunities for further development are outlined as well as implications for wider statistical modelling of microarray data.
Mertens
Bart J A
BJ
Department of Medical Statistics, University of Leiden, P. O. Box 9604, 2300 RC Leiden, The Netherlands. b.mertens@lumc.nl
D019295:Computational Biology / Q000379:methods / Q000706:statistics & numerical data; D014644:Genetic Variation; D006801:Humans; D007938:Leukemia / Q000145:classification / Q000235:genetics; D009426:Netherlands; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D010363:Pattern Recognition, Automated; D011336:Probability; D013223:Statistics as Topic / Q000379:methods*