Molecular Cancer Class Discovery Using Non-negative Matrix Factorization with Sparseness Constraint

Abstract

In cancer diagnosis and treatment, clustering based on gene expression data has been shown to be a powerful method in cancer class discovery. In this paper, we discuss the use of nonnegative matrix factorization with sparseness constraints (NMFSC), a method which can be used to learn a parts representation of the data, to analysis gene expression data. We illustrate how to choose appropriate sparseness factors in the algorithm and demonstrate the improvement of NMFSC by direct comparison with the nonnegative matrix factorization (NMF). In addition, when using it on the two well-studied datasets, we obtain pretty much the same results with the sparse non-negative matrix factorization (SNMF).