Characteristic gene selection via L2,1-norm Sparse Principal Component Analysis

Abstract

Sparse Principal Component Analysis (SPCA) is a method that can get the sparse loadings of the principal components (PCs), and it may formulate PCA as a regression-type optimization problem by using the elastic net. But the selected features are different with each PC and generally independent. A new method named SPCA has been proposed for removing these detect, which replaces the elastic net with L2,1-norm penalty. The results of the method on gene expression data are still unknown. Therefore, we will take a test to prove this point in this paper. Firstly, this method is applied to the simulated data for obtaining an optimal parameter. Secondly, the L2,1SPCA method is applied to the gene expression data, that is the head and neck squamous carcinoma data (HNSC). Thirdly, the characteristic genes are selected according the PCs. The results consist of very lower P-value and very higher hit count, which shows the method of L2,1SPCA can obtain higher recognition accuracy and higher relevancy to the genes. Finally, the experimental results demonstrate that the L2,1SPCA works well and has good performances in the gene expression data.