A new geometric biclustering algorithm based on the Hough transform for analysis of large-scale microarray data

Journal of Theoretical Biology
Hongya ZhaoHong Yan

Abstract

Biclustering is an important tool in microarray analysis when only a subset of genes co-regulates in a subset of conditions. Different from standard clustering analyses, biclustering performs simultaneous classification in both gene and condition directions in a microarray data matrix. However, the biclustering problem is inherently intractable and computationally complex. In this paper, we present a new biclustering algorithm based on the geometrical viewpoint of coherent gene expression profiles. In this method, we perform pattern identification based on the Hough transform in a column-pair space. The algorithm is especially suitable for the biclustering analysis of large-scale microarray data. Our studies show that the approach can discover significant biclusters with respect to the increased noise level and regulatory complexity. Furthermore, we also test the ability of our method to locate biologically verifiable biclusters within an annotated set of genes.

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Citations

Jun 16, 2012·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Qinghui GaoHaiyan Huang
Aug 11, 2012·PloS One·Jiyuan AnColleen C Nelson
Jul 22, 2014·Journal of Theoretical Biology·Yonggang PeiXiting Zhao
Oct 20, 2012·Journal of Theoretical Biology·Doris Z Wang, Hong Yan
Oct 2, 2013·Computer Methods and Programs in Biomedicine·Jose L FloresBorja Calvo
Nov 20, 2015·IEEE Transactions on Cybernetics·Shudong HuangTianrui Li
Apr 17, 2018·Briefings in Bioinformatics·Lan ZhaoMaarten F Bijlsma

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