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Dimension Reduction for Classification with Gene Expression Microarray Data

Author

Listed:
  • Dai Jian J

    (University of California, Davis)

  • Lieu Linh

    (University of California, Los Angeles)

  • Rocke David

    (University of California, Davis)

Abstract

An important application of gene expression microarray data is classification of biological samples or prediction of clinical and other outcomes. One necessary part of multivariate statistical analysis in such applications is dimension reduction. This paper provides a comparison study of three dimension reduction techniques, namely partial least squares (PLS), sliced inverse regression (SIR) and principal component analysis (PCA), and evaluates the relative performance of classification procedures incorporating those methods. A five-step assessment procedure is designed for the purpose. Predictive accuracy and computational efficiency of the methods are examined. Two gene expression data sets for tumor classification are used in the study.

Suggested Citation

  • Dai Jian J & Lieu Linh & Rocke David, 2006. "Dimension Reduction for Classification with Gene Expression Microarray Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 5(1), pages 1-21, February.
    • Handle: RePEc:bpj:sagmbi:v:5:y:2006:i:1:n:6
    DOI: 10.2202/1544-6115.1147
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    Citations

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    Cited by:

    1. Abhishek Bhola & Shailendra Singh, 2019. "Visualisation and Modelling of High-Dimensional Cancerous Gene Expression Dataset," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 1-22, March.
    2. Hayashi Takeshi, 2012. "Variational Bayes Procedure for Effective Classification of Tumor Type with Microarray Gene Expression Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(5), pages 1-21, October.
    3. Prendergast, Luke A. & Smith, Jodie A., 2022. "Influence functions for linear discriminant analysis: Sensitivity analysis and efficient influence diagnostics," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
    4. Boulesteix Anne-Laure, 2006. "Reader's Reaction to "Dimension Reduction for Classification with Gene Expression Microarray Data" by Dai et al (2006)," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 5(1), pages 1-7, June.
    5. Flavia Esposito, 2021. "A Review on Initialization Methods for Nonnegative Matrix Factorization: Towards Omics Data Experiments," Mathematics, MDPI, vol. 9(9), pages 1-17, April.
    6. Sek Won Kong & Christin D Collins & Yuko Shimizu-Motohashi & Ingrid A Holm & Malcolm G Campbell & In-Hee Lee & Stephanie J Brewster & Ellen Hanson & Heather K Harris & Kathryn R Lowe & Adrianna Saada , 2012. "Characteristics and Predictive Value of Blood Transcriptome Signature in Males with Autism Spectrum Disorders," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-13, December.
    7. Asuman Turkmen & Nedret Billor, 2013. "Partial least squares classification for high dimensional data using the PCOUT algorithm," Computational Statistics, Springer, vol. 28(2), pages 771-788, April.
    8. Ramos, Sandra & Amaral Turkman, Antónia & Antunes, Marília, 2010. "Bayesian classification for bivariate normal gene expression," Computational Statistics & Data Analysis, Elsevier, vol. 54(8), pages 2012-2020, August.
    9. Nguyen Tuan S & Rojo Javier, 2009. "Dimension Reduction of Microarray Data in the Presence of a Censored Survival Response: A Simulation Study," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-38, January.
    10. Abhijeet R Patil & Sangjin Kim, 2020. "Combination of Ensembles of Regularized Regression Models with Resampling-Based Lasso Feature Selection in High Dimensional Data," Mathematics, MDPI, vol. 8(1), pages 1-23, January.
    11. Song Huang & Tiejun Tong & Hongyu Zhao, 2010. "Bias-Corrected Diagonal Discriminant Rules for High-Dimensional Classification," Biometrics, The International Biometric Society, vol. 66(4), pages 1096-1106, December.

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