XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data

TL;DR

XOmiVAE is an interpretable deep learning model based on variational autoencoders that explains gene contributions and latent features in cancer classification and clustering using high-dimensional omics data.

Contribution

It introduces XOmiVAE, a novel VAE-based model that provides interpretability for deep learning in biomedical omics, enabling understanding of gene and latent dimension contributions.

Findings

XOmiVAE accurately explains gene contributions in cancer classification.

The model aligns with biomedical knowledge and literature.

XOmiVAE can explain both supervised and unsupervised deep learning results.

Abstract

The lack of explainability is one of the most prominent disadvantages of deep learning applications in omics. This "black box" problem can undermine the credibility and limit the practical implementation of biomedical deep learning models. Here we present XOmiVAE, a variational autoencoder (VAE) based interpretable deep learning model for cancer classification using high-dimensional omics data. XOmiVAE is capable of revealing the contribution of each gene and latent dimension for each classification prediction, and the correlation between each gene and each latent dimension. It is also demonstrated that XOmiVAE can explain not only the supervised classification but the unsupervised clustering results from the deep learning network. To the best of our knowledge, XOmiVAE is one of the first activation level-based interpretable deep learning models explaining novel clusters generated by VAE. The explainable results generated by XOmiVAE were validated by both the performance of downstream tasks and the biomedical knowledge. In our experiments, XOmiVAE explanations of deep learning based cancer classification and clustering aligned with current domain knowledge including biological annotation and academic literature, which shows great potential for novel biomedical knowledge discovery from deep learning models.