  Data augmentation plays a key role in modern machine learning pipelines.
While numerous augmentation strategies have been studied in the context of
computer vision and natural language processing, less is known for other data
modalities. Our work extends the success of data augmentation to compositional
data, i.e., simplex-valued data, which is of particular interest in the context
of the human microbiome. Drawing on key principles from compositional data
analysis, such as the Aitchison geometry of the simplex and subcompositions, we
define novel augmentation strategies for this data modality. Incorporating our
data augmentations into standard supervised learning pipelines results in
consistent performance gains across a wide range of standard benchmark
datasets. In particular, we set a new state-of-the-art for key disease
prediction tasks including colorectal cancer, type 2 diabetes, and Crohn's
disease. In addition, our data augmentations enable us to define a novel
contrastive learning model, which improves on previous representation learning
approaches for microbiome compositional data. Our code is available at
https://github.com/cunningham-lab/AugCoDa.
