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An NMF-L2,1-Norm Constraint Method for Characteristic Gene Selection.

ABSTRACT: Recent research has demonstrated that characteristic gene selection based on gene expression data remains faced with considerable challenges. This is primarily because gene expression data are typically high dimensional, negative, non-sparse and noisy. However, existing methods for data analysis are able to cope with only some of these challenges. In this paper, we address all of these challenges with a unified method: nonnegative matrix factorization via the L2,1-norm (NMF-L2,1). While L2,1-norm minimization is applied to both the error function and the regularization term, our method is robust to outliers and noise in the data and generates sparse results. The application of our method to plant and tumor gene expression data demonstrates that NMF-L2,1 can extract more characteristic genes than other existing state-of-the-art methods.

SUBMITTER: Wang D

PROVIDER: S-EPMC4948826 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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An NMF-L2,1-Norm Constraint Method for Characteristic Gene Selection.

Wang Dong D Liu Jin-Xing JX Gao Ying-Lian YL Yu Jiguo J Zheng Chun-Hou CH Xu Yong Y

PloS one 20160718 7

Recent research has demonstrated that characteristic gene selection based on gene expression data remains faced with considerable challenges. This is primarily because gene expression data are typically high dimensional, negative, non-sparse and noisy. However, existing methods for data analysis are able to cope with only some of these challenges. In this paper, we address all of these challenges with a unified method: nonnegative matrix factorization via the L2,1-norm (NMF-L2,1). While L2,1-nor ...[more]

PMID: 27428058

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