AugmentedPCA: A Python Package of Supervised and Adversarial Linear Factor Models

TL;DR

AugmentedPCA introduces a Python package that extends PCA with supervised and adversarial objectives, providing reproducible solutions that improve classification and interpretability in real-world biological datasets.

Contribution

It offers the first reproducible linear analog of deep autoencoder methods with supervised and adversarial objectives, implemented as an open-source Python package.

Findings

Supervised augmentation improves classification accuracy.

Principal components better reflect class labels.

Facilitates gene identification related to cancer types.

Abstract

Deep autoencoders are often extended with a supervised or adversarial loss to learn latent representations with desirable properties, such as greater predictivity of labels and outcomes or fairness with respects to a sensitive variable. Despite the ubiquity of supervised and adversarial deep latent factor models, these methods should demonstrate improvement over simpler linear approaches to be preferred in practice. This necessitates a reproducible linear analog that still adheres to an augmenting supervised or adversarial objective. We address this methodological gap by presenting methods that augment the principal component analysis (PCA) objective with either a supervised or an adversarial objective and provide analytic and reproducible solutions. We implement these methods in an open-source Python package, AugmentedPCA, that can produce excellent real-world baselines. We demonstrate the utility of these factor models on an open-source, RNA-seq cancer gene expression dataset, showing that augmenting with a supervised objective results in improved downstream classification performance, produces principal components with greater class fidelity, and facilitates identification of genes aligned with the principal axes of data variance with implications to development of specific types of cancer.