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Cancer is a heterogeneous disease with diverse molecular etiologies andoutcomes. The Cancer Genome Atlas (TCGA) has released a large compendium ofover 10,000 tumors with RNA-seq gene expression measurements. Gene expressioncaptures the diverse molecular profiles of tumors and can be interrogated toreveal differential pathway activations. Deep unsupervised models, includingVariational Autoencoders (VAE) can be used to reveal these underlying patterns.We compare a one-hidden layer VAE to two alternative VAE architectures withincreased depth. We determine the additional capacity marginally improvesperformance. We train and compare the three VAE architectures to otherdimensionality reduction techniques including principal components analysis(PCA), independent components analysis (ICA), non-negative matrix factorization(NMF), and analysis of gene expression by denoising autoencoders (ADAGE). Wecompare performance in a supervised learning task predicting gene inactivationpan-cancer and in a latent space analysis of high grade serous ovarian cancer(HGSC) subtypes. We do not observe substantial differences across algorithms inthe classification task. VAE latent spaces offer biological insights into HGSCsubtype biology.

Evaluating deep variational autoencoders trained on pan-cancer gene expression