  Cancer is a heterogeneous disease with diverse molecular etiologies and
outcomes. The Cancer Genome Atlas (TCGA) has released a large compendium of
over 10,000 tumors with RNA-seq gene expression measurements. Gene expression
captures the diverse molecular profiles of tumors and can be interrogated to
reveal differential pathway activations. Deep unsupervised models, including
Variational Autoencoders (VAE) can be used to reveal these underlying patterns.
We compare a one-hidden layer VAE to two alternative VAE architectures with
increased depth. We determine the additional capacity marginally improves
performance. We train and compare the three VAE architectures to other
dimensionality 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). We
compare performance in a supervised learning task predicting gene inactivation
pan-cancer and in a latent space analysis of high grade serous ovarian cancer
(HGSC) subtypes. We do not observe substantial differences across algorithms in
the classification task. VAE latent spaces offer biological insights into HGSC
subtype biology.
