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A new geometric biclustering algorithm based on the Hough transform for analysis of large-scale microarray data |
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| Authors: | Zhao Hongya Liew Alan Wee-Chung Xie Xudong Yan Hong |
| Institution: | a Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong b School of Information and Communication Technology, Griffith University, Brisbane, Australia c Department of Automation, Tsinghua University, Beijing, China d School of Electronic and Information Engineering, University of Sydney, NSW 2006, Sydney, Australia |
| 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. |
| Keywords: | Microarray data analysis Gene expression profiles Biclustering The Hough transform |
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