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Comparison of Clinical Subgroup aCGH Profiles through Pseudolikelihood Ratio Tests

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Listed:
  • Engler David

    (Brigham Young University)

  • Shen Yiping

    (Massachusetts General Hospital)

  • Gusella James

    (Massachusetts General Hospital)

  • Betensky Rebecca A.

    (Harvard School of Public Health)

Abstract

Array-based Comparative Genomic Hybridization (aCGH) is a microarray-based technology that assists in identification of DNA sequence copy number changes across the genome. Examination of differences in instability phenotype, or pattern of copy number alterations, between cancer subtypes can aid in classification of cancers and lead to better understanding of the underlying cytogenic mechanism. Instability phenotypes are composed of a variety of copy number alteration features including height or magnitude of copy number alteration level, frequency of transition between copy number states such as gain and loss, and total number of altered clones or probes. That is, instability phenotype is multivariate in nature. Current methods of instability phenotype assessment, however, are limited to univariate measures and are therefore limited in both sensitivity and interpretability. In this paper, a novel method of instability assessment is presented that is based on the Engler et al. (2006) pseudolikelhood approach for aCGH data analysis. Through use of a pseudolikelihood ratio test (PLRT), more sensitive assessment of instability phenotype differences between cancer subtypes is possible. Evaluation of the PLRT method is conducted through analysis of a meningioma data set and through simulation studies. Results are shown to be more accurate and more easily interpretable than current measures of instability assessment.

Suggested Citation

  • Engler David & Shen Yiping & Gusella James & Betensky Rebecca A., 2011. "Comparison of Clinical Subgroup aCGH Profiles through Pseudolikelihood Ratio Tests," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-23, July.
    • Handle: RePEc:bpj:sagmbi:v:10:y:2011:i:1:n:31
    DOI: 10.2202/1544-6115.1407
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    References listed on IDEAS

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    1. Baladandayuthapani, Veerabhadran & Ji, Yuan & Talluri, Rajesh & Nieto-Barajas, Luis E. & Morris, Jeffrey S., 2010. "Bayesian Random Segmentation Models to Identify Shared Copy Number Aberrations for Array CGH Data," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1358-1375.
    2. Stacia M. DeSantis & E. Andrés Houseman & Brent A. Coull & David N. Louis & Gayatry Mohapatra & Rebecca A. Betensky, 2009. "A Latent Class Model with Hidden Markov Dependence for Array CGH Data," Biometrics, The International Biometric Society, vol. 65(4), pages 1296-1305, December.
    3. Fridlyand, Jane & Snijders, Antoine M. & Pinkel, Dan & Albertson, Donna G. & Jain, A.N.Ajay N., 2004. "Hidden Markov models approach to the analysis of array CGH data," Journal of Multivariate Analysis, Elsevier, vol. 90(1), pages 132-153, July.
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