Phenotype Driven Data Augmentation Methods for Transcriptomic Data

Abstract

With machine learning taking over biomedical applications, working with transcriptomic data on supervised learning tasks is challenging due to high dimensionality, low patient numbers and class imbalances. Machine learning models tend to overfit these data and do not generalise well on out-of-distribution samples. Data augmentation strategies help alleviate this by introducing synthetic data points and acting as regularisers. However, existing approaches are either computationally intensive, require population parametric estimates or generate insufficiently diverse samples. To address these challenges, we introduce two classes of phenotype driven data augmentation approaches - signature-dependent and signature-independent. The signature-dependent methods assume the existence of distinct gene signatures describing some phenotype and are simple, non-parametric, and novel data augmentation methods. The signature-independent methods are a modification of the established Gamma-Poisson and Poisson sampling methods for gene expression data. As case studies, we apply our augmentation methods to transcriptomic data of colorectal and breast cancer. Through discriminative and generative experiments with external validation, we show that our methods improve patient stratification by 5 - 15% over other augmentation methods in different cases. The study additionally provides insights into the limited benefits of over-augmenting data. The code is hosted on GitHub, and includes a link to the augmented datasets.