Open AccessAn NMF-L2,1-Norm Constraint Method for Characteristic Gene Selection
Author(s) -
Dong Wang,
JinXing Liu,
Ying-Lian Gao,
Jiguo Yu,
Chun-Hou Zheng,
Yong Xu
Publication year - 2016
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0158494
Subject(s) - non negative matrix factorization , outlier , gene selection , computer science , norm (philosophy) , matrix decomposition , regularization (linguistics) , pattern recognition (psychology) , matrix norm , artificial intelligence , data mining , computational biology , gene expression , algorithm , mathematics , gene , biology , genetics , physics , microarray analysis techniques , eigenvalues and eigenvectors , quantum mechanics , political science , law
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 L 2,1 -norm (NMF- L 2,1 ). While L 2,1 -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- L 2,1 can extract more characteristic genes than other existing state-of-the-art methods.