NMF based gene selection algorithm for improving performance of the spectral cancer clustering

Analyzing cancers using microarray gene expression datasets is currently an active research in medical community. There are many tasks related to this research, e.g., clustering and classifcation, data compression, and samples characterization. In this paper, we discuss the task of cancer clustering. The spectral clustering is one of the most commonly used methods in cancer clustering. As the gene expression datasets usually are highly imbalanced, i.e., containing only a few tissue samples (hundreds at most) but each is expressed by thousands of genes, filtering out some irrelevant and potentially misleading gene expressions is a necessary step to improve the performance of the method. In this paper, we propose an unsupervised gene selection algorithm based on the nonnegative matrix factorization (NMF). Our algorithm is designed by making use of the clustering capability of the NMF to select the most informative genes. Clustering performance of the spectral method is then evaluated by comparing the results using the original datasets with the results using the pruned datasets. Our results suggest that the proposed algorithm can be used to improve clustering performance of the spectral method.