  Learning latent expression themes that best express complex patterns in a
sample is a central problem in data mining and scientific research. For
example, in computational biology we seek a set of salient gene expression
themes that explain a biological process, extracting them from a large pool of
gene expression profiles. In this paper, we introduce probabilistic models to
learn such latent themes in an unsupervised fashion. Our models capture
contagion, i.e., dependence among multiple occurrences of the same feature,
using a hierarchical Bayesian scheme. Contagion is a convenient analytical
formalism to characterize semantic themes underlying observed feature patterns,
such as biological context. We present model variants tailored to different
properties of biological data, and we outline a general variational inference
scheme for approximate posterior inference. We validate our methods on both
simulated data and realistic high-throughput gene expression profiles via SAGE.
Our results show improved predictions of gene functions over existing methods
based on stronger independence assumptions, and demonstrate feasibility of a
promising hierarchical Bayesian formalism for soft clustering and latent
aspects analysis.
