A joint-L_2,1-norm-constraint-based semi-supervised feature extraction for RNA-Seq data analysis

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• 作者：Jin-Xing Liu^a ; ^b ; ^{sdcavell@126.com}Author Vitae ; Dong Wang^a ; ^{dongwshark@126.com}Author Vitae ; Ying-Lian Gao^c ; ^{yinliangao@126.com}Author Vitae ; Chun-Hou Zheng^d ; ^{zhengch99@126.com}Author Vitae ; Jun-Liang Shang^a ; ^{shangjunliang110@163.com}Author Vitae ; Feng Liu^e ; ^{feng.liu@szu.edu.cn}Author Vitae ; Yong Xu^b ; ^{yongxu@ymail.com}Author Vitae
• 关键词：Feature extraction ; L2 ; 1-norm constraint ; Spectral regression ; Semi-supervised method ; RNA-Seq data analysis
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：8 March 2017
• 年：2017
• 卷：228
• 期：Complete
• 页码：263-269
• 全文大小：899 K
• 卷排序：228

文摘

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.