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bioRxiv · 10.1101/2020.01.09.899898

Discriminant Analysis of Principle Component analyses of Physiological Data.

Haidar, O.·Ball, S. T. M.·Barrett-Jolley, R.
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Abstract

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.

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Haidar, O., Ball, S. T. M., Barrett-Jolley, R.. 2020-01-09. Discriminant Analysis of Principle Component analyses of Physiological Data.. https://doi.org/10.1101/2020.01.09.899898

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