Tumor clustering using nonnegative matrix factorization with gene selection.
Tumor clustering is becoming a powerful method in cancer class discovery. Nonnegative matrix factorization (NMF) has shown advantages over other conventional clustering techniques. Nonetheless, there is still considerable room for improving the performance of NMF. To this end, in this paper, gene selection and explicitly enforcing sparseness are introduced into the factorization process. Particularly, independent component analysis is employed to select a subset of genes so that the effect of irrelevant or noisy genes can be reduced. The NMF and its extensions, sparse NMF and NMF with sparseness constraint, are then used for tumor clustering on the selected genes. A series of elaborate experiments are performed by varying the number of clusters and the number of selected genes to evaluate the cooperation between different gene selection settings and NMF-based clustering. Finally, the experiments on three representative gene expression datasets demonstrated that the proposed scheme can achieve better clustering results.
Zheng
Chun-Hou
CH
Intelligent Computing Laboratory, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China. zhengch99@126.com
Huang
De-Shuang
DS
Zhang
Lei
L
Kong
Xiang-Zhen
XZ
D000465:Algorithms; D016000:Cluster Analysis; D030541:Databases, Genetic; D015870:Gene Expression; D006801:Humans; D008957:Models, Genetic; D009369:Neoplasms / Q000235:genetics* / Q000378:metabolism; D025341:Principal Component Analysis