Discriminant Analysis of Principle Component analyses of Physiological Data

There are many situations in physiological and pharmacological analyses where multivariate data is collected. Frequently these are analysed with t-tests and multiple (Bonferroni) comparisons or ANOVA with post-hoc test. Increasingly, even with more powerful computers many variables and it seems that feature reduction would be a useful approach. The most commonly used method is principle component analyses, but in this report we compare this to a technique developed for genetic analyses, discriminant analysis of principle component (DAPC) analyses. A simple to use and well-maintained library exists for DAPC analyses, Adegenet, and using this we find that DAPC detects differences between synthetic physiological datasets with significantly greater accuracy than traditional PCA.