Analysis of variance–principal component analysis: A soft tool for proteomic discovery Original Research Article

A soft tool for detection of biomarkers in high dimensional data sets has been developed. The tool combines analysis of variance (ANOVA) and principal component analysis (PCA). Covariations are separated using ANOVA into main effects and interaction. The covariances for each effect are combined with the pure error and subjected to PCA. If the main effect is significant compared to the residual error, the first principal component will span this source of variation. This technique avoids rotation of the principal components and when significant the variable loadings are amenable to interpretation. ANOVA–PCA is demonstrated as a tool for optimization of a proteomic assay for biomarkers. Two independent sets of matrix assisted laser desorption/ionization-mass spectra (MALDI-MS) were collected from amniotic fluids. These studies gave consistent biomarkers for premature delivery.