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A simple method for screening variables before clustering microarray data

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  • Krzanowski, Wojtek J.
  • Hand, David J.

Abstract

A simple and computationally fast procedure is proposed for screening a large number of variables prior to cluster analysis. Each variable is considered in turn, the sample is divided into the two groups that maximise the ratio of between-group to within-group sum of squares for that variable, and the achieved value of this ratio is tested to see if it is significantly greater than what would be expected when partitioning a sample from a single homogeneous population. Those variables that achieve significance are then used in the cluster analysis. It is suggested that significance levels be assessed using a Monte Carlo computational procedure; by assuming within-group normality an analytical approximation is derived, but caution in its use is advocated. Computational details are provided for both the partitioning and the testing. The procedure is applied to several microarray data sets, showing that it can often achieve good results both quickly and simply.

Suggested Citation

  • Krzanowski, Wojtek J. & Hand, David J., 2009. "A simple method for screening variables before clustering microarray data," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2747-2753, May.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:7:p:2747-2753
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    References listed on IDEAS

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    1. Mahlet G. Tadesse & Joseph G. Ibrahim & George L. Mutter, 2003. "Identification of Differentially Expressed Genes in High-Density Oligonucleotide Arrays Accounting for the Quantification Limits of the Technology," Biometrics, The International Biometric Society, vol. 59(3), pages 542-554, September.
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    Cited by:

    1. Brusco, Michael J. & Steinley, Douglas, 2011. "Exact and approximate algorithms for variable selection in linear discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 123-131, January.
    2. Gao, Jinxin & Hitchcock, David B., 2010. "James-Stein shrinkage to improve k-means cluster analysis," Computational Statistics & Data Analysis, Elsevier, vol. 54(9), pages 2113-2127, September.
    3. Pacheco, Joaquín & Casado, Silvia & Porras, Santiago, 2013. "Exact methods for variable selection in principal component analysis: Guide functions and pre-selection," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 95-111.
    4. Brusco, Michael J., 2014. "A comparison of simulated annealing algorithms for variable selection in principal component analysis and discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 38-53.

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