Biclustering Using Message Passing
Biclustering is the analog of clustering on a bipartite graph. Existent methods infer biclusters through local search strategies that find one cluster at a time; a common technique is to update the row memberships based on the current column memberships, and vice versa. We propose a biclustering algorithm that maximizes a global objective function using message passing. Our objective function closely approximates a general likelihood function, separating a cluster size penalty term into row-and column-count penalties. Because we use a global optimization framework, our approach excels at resolving the overlaps between biclusters, which are important features of biclusters in practice. Moreover, Expectation-Maximization can be used to learn the model parameters if they are unknown. In simulations, we find that our method outperforms two of the best existing biclustering algorithms, ISA and LAS, when the planted clusters overlap. Applied to three gene expression datasets, our method finds coregulated gene clusters that have high quality in terms of cluster size and density.
This paper proposes a biclustering algorithm that has some advantages over related works. It is able to deal with overlapping clusters and the clustering problem is addressed by the maximization of a well-defined global objective function. I find the idea interesting and the paper technically sound and well written. Despite the description of related works is kind of short, it is enough for positioning the paper in the right context and to understand the novelty of the proposals. While the theoretical formulation presented in Section 2 seems to be correct, it would be very beneficial to have at the end of this section the pseudo-code of the proposed BCMP method. This would help to wrap up the results presented in this section and to make clearer the steps of the proposed algorithm.In the experimental section, the proposed method is compared with to related methods on simulated data with different degrees of noise and overlapping between clusters. Despite these results illustrate the suitability of the proposed algorithm; it would be nice to see its performance on real datasets. In the introduction of the paper, authors claimed that this kind of algorithms can be used in different kind of applications like clustering of microarray data, tumor prediction and document classification. However, no real data is used to test the performance (and compare with existing methods) of the proposed clustering algorithm. A final minor comment is regarding the abstract. In the current version is too long, at some point it looks like an introductory section. I think it can be reduced to half of the size.