A joint-L2,1-norm-constraint-based semi-supervised feature extraction for RNA-Seq data analysis

It is of urgency to effectively identify differentially expressed genes from RNA-Seq data. In this paper, we proposed a novel method, joint-L2,1-norm-constraint-based semi-supervised feature extraction (L21SFE), to analyze RNA-Seq data. Our scheme was shown as follows. Firstly, we constructed a graph Laplacian matrix and refined it by using the labeled samples. Our graph construction method can make full use of a large number of unlabelled samples. Secondly, we found semi-supervised optimal maps by solving a generalized eigenvalue problem. Thirdly, we solved an optimal problem via the joint L2,1-norm constraint to obtain a projection matrix. It can diminish the impact of noises and outliers by using the L2,1-norm constraint and produce more precise results. Finally, we identified differentially expressed genes based on the projection matrix. The results on simulation and real RNA-Seq data sets demonstrated the feasibility and effectiveness of our method.