Biclustering of microarray data based on singular value decomposition

Authors:
Wen-Hui Yang;Dao-Qing Dai;Hong Yan
Affiliations:
Center for Computer Vision and Department of Mathematics, Sun Yat-Sen, University, Guangzhou, China;Center for Computer Vision and Department of Mathematics, Sun Yat-Sen, University, Guangzhou, China and Department of Electric Engineering, City University of Hong Kong, Kowloon, Hong Kong;Department of Electric Engineering, City University of Hong Kong, Kowloon, Hong Kong and School of Electrical and Information Engineering, University of Sydney, NSW, Australia
Venue:
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Year:
2007

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Abstract

Biclustering is an important approach in microarray data analysis. Using biclustering algorithms, one can identify sets of genes sharing compatible expression patterns across subsets of samples. These patterns may provide clues about the main biological processes associated to different physiological states. In this study, we present a new biclustering algorithm to identify local structures from gene expression data set. Our method uses singular value decomposition (SVD) as its framework. Based on the singular value decomposition, identifying bicluster problem from gene expression matrix is transformed into two global clustering problems. After biclustering, our algorithm forms blocks of up-regulated or down-regulated in gene expression matrix, so as to infer that which genes are co-regulated and which genes possibly are functionally related. The experimental results on three benchmark datasets (Human Tissues, Lymphoma, Leukemia) demonstrate good visualization and interpretation ability.