Classification for high-throughput data with an optimal subset of principal components.
High-throughput data have been widely used in biological and medical studies to discover gene and protein functions. Due to the high dimensionality, principal component analysis (PCA) is often involved for data dimension reduction. However, when a few principal components (PCs) are selected for dimension reduction or considered for dimension determination, they are typically ranked by their variances, eigenvalues. However, this approach is not always effective in subsequent multivariate analysis, particularly classification. To maximize information from data with a subset of the components, we apply a different ranking criterion, canonical variate criterion, which considers within- and between-group variance rather than total variance in the classical criterion. Four prevalent classification methods are considered and compared using leave-one-out cross-validation. These methods are illustrated with three real high-throughput data sets, two microarray data sets and a nuclear magnetic resonance spectra data set.
Song
Joon Jin
JJ
Department of Mathematical Sciences, University of Arkansas, Fayetteville, AR 72701, USA. jjsong@uark.edu
Ren
Yuan
Y
Yan
Fenglan
F
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000235:genetics; D019295:Computational Biology; D003198:Computer Simulation; D016208:Databases, Factual; D016002:Discriminant Analysis; D005260:Female; D023281:Genomics; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis / Q000235:genetics; D009682:Magnetic Resonance Spectroscopy; D051379:Mice; D020411:Oligonucleotide Array Sequence Analysis; D025341:Principal Component Analysis / Q000379:methods*; D040901:Proteomics