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Semiparametric methods for identification of tumor progression genes from microarray data

Listed author(s):
  • Debashis Ghosh

    (University of Michigan)

  • Arul Chinnaiyan

    (University of Michigan Pathology and Urology)

Registered author(s):

    The use of microarray data has become quite commonplace in medical and scientific experiments. We focus here on microarray data generated from cancer studies. It is potentially important for the discovery of biomarkers to identify genes whose expression levels correlate with tumor progression. In this article, we develop statistical procedures for the identification of such genes, which we term tumor progression genes. Two methods are considered in this paper. The first is use of a proportional odds procedure, combined with false discovery rate estimation techniques to adjust for the multiple testing problem. The second method is based on order-restricted estimation procedures. The proposed methods are applied to data from a prostate cancer study. In addition, their finite-sample properties are compared using simulated data.

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    Paper provided by Berkeley Electronic Press in its series The University of Michigan Department of Biostatistics Working Paper Series with number 1039.

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    Date of creation: 11 Jul 2004
    Handle: RePEc:bep:mchbio:1039
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    1. L. Ferrante & S. Bompadre & L. Possati & L. Leone, 2000. "Parameter Estimation in a Gompertzian Stochastic Model for Tumor Growth," Biometrics, The International Biometric Society, vol. 56(4), pages 1076-1081, December.
    2. John D. Storey, 2002. "A direct approach to false discovery rates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 479-498.
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