PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Hierarchical generative biclustering for microRNA expression analysis
Jose Caldas and Samuel Kaski
In: Research in Computational Molecular Biology, Proceedings of 14th Annual International Conference RECOMB 2010 (2010) Springer , Berlin , pp. 65-79.

Abstract

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 and yet flexible, and computations feasible. The model can additionally be used for 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://www.cis.hut.fi/projects/mi/software/treebic/

EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:7591
Deposited By:Samuel Kaski
Deposited On:17 March 2011