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Most existing approaches to clustering gene expression time course data treatthe different time points as independent dimensions and are invariant topermutations, such as reversal, of the experimental time course. Approachesutilizing HMMs have been shown to be helpful in this regard, but are hamperedby having to choose model architectures with appropriate complexities. Here wepropose for a clustering application an HMM with a countably infinite statespace; inference in this model is possible by recasting it in the hierarchicalDirichlet process (HDP) framework (Teh et al. 2006), and hence we call it theHDP-HMM. We show that the infinite model outperforms model selection methodsover finite models, and traditional time-independent methods, as measured by avariety of external and internal indices for clustering on two large publiclyavailable data sets. Moreover, we show that the infinite models utilize morehidden states and employ richer architectures (e.g. state-to-state transitions)without the damaging effects of overfitting.

Gene Expression Time Course Clustering with Countably Infinite Hidden Markov Models