Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome
This work addresses the need for effective data augmentation in microbiome research, enabling improved predictive models for disease diagnosis, though it is incremental as it extends existing augmentation concepts to a new data modality.
The paper tackled the problem of data augmentation for compositional data, specifically in microbiome analysis, by defining novel augmentation strategies based on compositional data analysis principles, resulting in new state-of-the-art performance for disease prediction tasks such as colorectal cancer, type 2 diabetes, and Crohn's disease.
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.