Hierarchical Generative Biclustering for MicroRNA Expression Analysis
Clustering methods are a useful and common first step in gene expression studies, but the results may be hard to interpret. We bring in explicitly an indicator of which genes tie each cluster, changing the setup to biclustering. Furthermore, we make the indicators hierarchical, resulting in a hierarchy of progressively more specific biclusters. A non-parametric Bayesian formulation makes the model rigorous yet flexible and computations feasible. The model can additionally be used in information retrieval for relating relevant samples. We show that the model outperforms four other biclustering procedures on a large miRNA data set. We also demonstrate the model's added interpretability and information retrieval capability in a case study. Software is publicly available at http://research.ics.tkk.fi/mi/software/treebic/.
gene expression machine learning stochastic processes trees