title: Sparse p-norm Nonnegative Matrix Factorization for clustering gene expression data.
abstract: Nonnegative Matrix Factorization (NMF) is a powerful tool for gene expression data analysis as it reduces thousands of genes to a few compact metagenes, especially in clustering gene expression samples for cancer class discovery. Enhancing sparseness of the factorisation can find only a few dominantly coexpressed metagenes and improve the clustering effectiveness. Sparse p-norm (p > 1) Nonnegative Matrix Factorization (Sp-NMF) is a more sparse representation method using high order norm to normalise the decomposed components. In this paper, we investigate the benefit of high order normalisation for clustering cancer-related gene expression samples. Experimental results demonstrate that Sp-NMF leads to robust and effective clustering in both automatically determining the cluster number, and achieving high accuracy.
authors.affiliation: Life Science Division, Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, China. victorwxliu@yahoo.com.cn
authors.forename: Weixiang
authors.initials: W
authors.lastname: Liu
authors.forename: Kehong
authors.initials: K
authors.lastname: Yuan
mesh_terms: D000465:Algorithms; D000818:Animals; D014408:Biomarkers, Tumor / Q000378:metabolism*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D005810:Multigene Family / Q000502:physiology*; D009363:Neoplasm Proteins / Q000378:metabolism*; D009369:Neoplasms / Q000175:diagnosis* / Q000378:metabolism*; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins; D020543:Proteome