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Semi-supervised Feature Extraction for RNA-Seq Data Analysis

  • Jin-Xing Liu
  • Yong XuEmail author
  • Ying-Lian Gao
  • Dong Wang
  • Chun-Hou Zheng
  • Jun-Liang Shang
Conference paper
  • 2k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9227)

Abstract

It is of urgency to effectively identify differentially expressed genes from RNA-Seq data. In this paper, we propose a novel method, semi-supervised feature extraction, to analyze RNA-Seq data. Our scheme is shown as follows. Firstly, we construct a graph Laplacian matrix and refine it by using labeled samples. Secondly, we find semi-supervised optimal maps by solving a generalized eigenvalue problem. Thirdly, we solve an optimal problem via joint L2,1-norm constraint to obtain a projection matrix. Finally, we identify differentially expressed genes based on the projection matrix. The results on real RNA-Seq data sets demonstrate the feasibility and effectiveness of our method.

Keywords

Feature extraction L2,1-norm constraint Spectral regression RNA-Seq data analysis 

Notes

Acknowledgements

This work was supported in part by the NSFC under grant Nos. 61370163 and 61272339; China Postdoctoral Science Foundation funded project, No. 2014M560264; Shandong Provincial Natural Science Foundation, under grant Nos. ZR2013FL016 and BS2014DX004; Shenzhen Municipal Science and Technology Innovation Council (Nos. JCYJ20140417172417174, CXZZ20140904154910774 and JCYJ20140904154645958).

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Jin-Xing Liu
    • 1
    • 2
  • Yong Xu
    • 1
    Email author
  • Ying-Lian Gao
    • 3
  • Dong Wang
    • 2
  • Chun-Hou Zheng
    • 4
  • Jun-Liang Shang
    • 2
  1. 1.Bio-Computing Research Center, Shenzhen Graduate SchoolHarbin Institute of TechnologyShenzhenChina
  2. 2.School of Information Science and EngineeringQufu Normal UniversityRizhaoChina
  3. 3.Library of Qufu Normal UniversityQufu Normal UniversityRizhaoChina
  4. 4.School of Mechanical Engineering and AutomationAnhui UniversityHefeiChina

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