Structured Sparse Non-negative Matrix Factorization with L20-Norm for scRNA-seq Data Analysis

Non-negative matrix factorization (NMF) is a powerful tool for dimensionality reduction and clustering. Unfortunately, the interpretation of the clustering results from NMF is difficult, especially for the high-dimensional biological data without effective feature selection. In this paper, we first introduce a row-sparse NMF with -norm constraint (NMF_), where the basis matrix is constrained by the -norm, such that has a row-sparsity pattern with feature selection. It is a challenge to solve the model, because the -norm is non-convex and non-smooth. Fortunately, we prove that the -norm satisfies the Kurdyka-\L{ojasiewicz} property. Based on the finding, we present a proximal alternating linearized minimization algorithm and its monotone accelerated version to solve the NMF_ model. In addition, we also present a orthogonal NMF with -norm constraint (ONMF_) to enhance the clustering performance by using a non-negative orthogonal constraint. We propose an efficient algorithm to solve ONMF_ by transforming it into a series of constrained and penalized matrix factorization problems. The results on numerical and scRNA-seq datasets demonstrate the efficiency of our methods in comparison with existing methods.
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