title: Identifying subspace gene clusters from microarray data using low-rank representation.
abstract: Identifying subspace gene clusters from the gene expression data is useful for discovering novel functional gene interactions. In this paper, we propose to use low-rank representation (LRR) to identify the subspace gene clusters from microarray data. LRR seeks the lowest-rank representation among all the candidates that can represent the genes as linear combinations of the bases in the dataset. The clusters can be extracted based on the block diagonal representation matrix obtained using LRR, and they can well capture the intrinsic patterns of genes with similar functions. Meanwhile, the parameter of LRR can balance the effect of noise so that the method is capable of extracting useful information from the data with high level of background noise. Compared with traditional methods, our approach can identify genes with similar functions yet without similar expression profiles. Also, it could assign one gene into different clusters. Moreover, our method is robust to the noise and can identify more biologically relevant gene clusters. When applied to three public datasets, the results show that the LRR based method is superior to existing methods for identifying subspace gene clusters.
authors.affiliation: School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, Jiangsu, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Cui
authors.forename: Chun-Hou
authors.initials: CH
authors.lastname: Zheng
authors.forename: Jian
authors.initials: J
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D020869:Gene Expression Profiling; D006801:Humans; D046228:Microarray Analysis / Q000379:methods*; D008957:Models, Genetic; D005810:Multigene Family / Q000235:genetics*; D012372:ROC Curve; D015003:Yeasts