Identifying Characteristic Genes Based on Robust Principal Component Analysis

  • Chun-Hou Zheng
  • Jin-Xing Liu
  • Jian-Xun Mi
  • Yong Xu
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 304)


In this paper, based on robust PCA, a novel method of characteristic genes identification is proposed. In our method, the differentially expressed genes and non-differentially expressed genes are treated as perturbation signals S 0 and low-rank matrix A 0, respectively, which can be recovered from the gene expression data using robust PCA. The scheme to identify the characteristic genes is as following. Firstly, the matrix S 0 of perturbation signals is discovered from gene expression data matrix D by using robust PCA. Secondly, the characteristic genes are selected according to matrix S 0. Finally, the characteristic genes are checked by the tool of Gene Ontology. The experimental results show that our method is efficient and effective.


robust PCA gene identification gene expression data 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Wang, L., Li, P.C.H.: Microfluidic DNA Microarray Analysis: A Review. Analytica Chimica Acta 687, 12–27 (2011)CrossRefGoogle Scholar
  2. 2.
    Heller, M.J.: DNA Microarray Technology: Devices, Systems, and Applications. Annual Review of Biomedical Engineering 4, 129–153 (2002)CrossRefGoogle Scholar
  3. 3.
    Nyamundanda, G., Brennan, L., Gormley, I.C.: Probabilistic Principal Component Analysis for Metabolomic Data. BMC Bioinformatics 11, 571 (2010)CrossRefGoogle Scholar
  4. 4.
    Witten, D.M., Tibshirani, R., Hastie, T.: A Penalized Matrix Decomposition, with Applications to Sparse Principal Components and Canonical Correlation Analysis. Biostatistics 10, 515–534 (2009)CrossRefGoogle Scholar
  5. 5.
    Liu, J.X., Zheng, C.H., Xu, Y.: Extracting Plants Core Genes Responding to Abiotic Stresses by Penalized Matrix Decomposition. Comput. Biol. Med. (2012), doi:10.1016 /j.compbiomed.2012.1002.1002Google Scholar
  6. 6.
    Candes, E.J., Li, X., Ma, Y., Wright, J.: Robust Principal Component Analysis? Journal of the ACM 58, 11 (2011)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Lin, Z., Chen, M., Wu, L., Ma, Y.: The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-rank Matrices (2010),
  8. 8.
    Journée, M., Nesterov, Y., Richtarik, P., Sepulchre, R.: Generalized Power Method for Sparse Principal Component Analysis. The Journal of Machine Learning Research 11, 517–553 (2010)Google Scholar
  9. 9.
    Boyle, E.I., Weng, S.A., Gollub, J., Jin, H., Botstein, D., Cherry, J.M., Sherlock, G.: GO:TermFinder - Open Source Software for Accessing Gene Ontology Information and Finding Significantly Enriched Gene Ontology Terms Associated with a list of Genes. Bioinformatics 20, 3710–3715 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Chun-Hou Zheng
    • 1
  • Jin-Xing Liu
    • 2
    • 3
  • Jian-Xun Mi
    • 2
  • Yong Xu
    • 2
  1. 1.College of Electrical Engineering and AutomationAnhui UniversityHefeiChina
  2. 2.Bio-Computing Research Center, Shenzhen Graduate SchoolHarbin Institute of TechnologyShenzhenChina
  3. 3.College of Information and Communication TechnologyQufu Normal UniversityRizhaoChina

Personalised recommendations