A conceptual illustration of variations autoencoder architectures.

A: An overview of the system architecture. A pathway-derived prior is generated on the transcript or gene level, as described in the following sections. The three alternative input types (transcript-level, gene-level, and community-level) correspond to three different model variants. B: As a latent variable framework, the model assumes that latent variables (Z) are determinants of the measured data (X). To learn Z, p(Z|X) is approximated by q(Z|X), and modeled as the encoder portion of the network. The latent values represent probability distributions, which are implemented in the bottle-neck layer as values for mu (μ) and sigma (σ). Finally, the decoder is conceptually equivalent to p(X|Z).