On the bias of H-scores for comparing biclusters, and how to correct it

Jacopo Di Iorio; Francesca Chiaromonte; Marzia A. Cremona

doi:10.1093/bioinformatics/btaa060

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arXiv · 1907.11142

On the bias of H-scores for comparing biclusters, and how to correct it

Jacopo Di Iorio·Francesca Chiaromonte·Marzia A. Cremona

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Abstract

In the last two decades several biclustering methods have been developed as new unsupervised learning techniques to simultaneously cluster rows and columns of a data matrix. These algorithms play a central role in contemporary machine learning and in many applications, e.g. to computational biology and bioinformatics. The H-score is the evaluation score underlying the seminal biclustering algorithm by Cheng and Church, as well as many other subsequent biclustering methods. In this paper, we characterize a potentially troublesome bias in this score, that can distort biclustering results. We prove, both analytically and by simulation, that the average H-score increases with the number of rows/columns in a bicluster. This makes the H-score, and hence all algorithms based on it, biased towards small clusters. Based on our analytical proof, we are able to provide a straightforward way to correct this bias, allowing users to accurately compare biclusters.

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Jacopo Di Iorio, Francesca Chiaromonte, Marzia A. Cremona. 2019-07-24. On the bias of H-scores for comparing biclusters, and how to correct it. https://doi.org/10.1093/bioinformatics%2Fbtaa060

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