Principal Subsimplex Analysis
Compositional data, which are data that lie in a simplex, naturally arise in many scientific domains such as geochemistry, microbiology, and economics. In such domains, obtaining sensible lower-dimensional representations and modes of variation plays an important role. A typical approach to the problem is applying a log-ratio transformation followed by principal component analysis (PCA). However, this approach has several potential weaknesses: it can amplify variation in minor variables and obscure important variation within major variables; it is not directly applicable to datasets containing zeros, and zero imputation methods can give highly variable results; it has limited ability to capture linear patterns present on the simplex. In this paper, we propose novel methods that produce nested sequences of simplices of decreasing dimensions analogous to backwards principal component analysis. These nested sequences offer both interpretable lower dimensional representations and linear modes of variation. In addition, our methods are applicable without any modification to datasets containing zeros. We demonstrate our methods on simulated data and on relative abundances of diatom species during the late Pliocene. Supplementary materials and R implementations for this article are available online.