TY - JOUR T1 - Discriminant Analysis of Principle Component analyses of Physiological Data JF - bioRxiv DO - 10.1101/2020.01.09.899898 SP - 2020.01.09.899898 AU - Omar Haidar AU - Samuel Ball AU - Richard Barrett-Jolley Y1 - 2020/01/01 UR - http://biorxiv.org/content/early/2020/01/09/2020.01.09.899898.abstract N2 - 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, Adegenet2, and using this we find that DAPC detects differences between synthetic physiological datasets with significantly greater accuracy than traditional PCA. ER -