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Internal validation inferences of significant genomic features in genome-wide screening

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  • Cheng, Cheng

Abstract

Although validation of classification and prediction models has been a long-standing topic in Statistics and computer learning, the concept of statistical validation in genome-wide screening studies has been vague. Internal validation generally refers to validation procedures solely based on the study dataset. A popular approach to internal validation of identified genomic features has been the split-dataset validation. Contrast to this approach, internal validation in genome-wide association screening studies is precisely defined through the concepts of association profile and profile significance. A general procedure and two specific profile significance measures are developed and are compared with the split-dataset validation approach by a simulation study. The simulation results clearly demonstrate the strength and limitations of the profile significance approach to internal validation, especially its enormous gain in sensitivity (power) and stability over the split-dataset validation. The proposed methodology is illustrated by an example of genome-wide SNP association analysis in genetic epidemiology.

Suggested Citation

  • Cheng, Cheng, 2009. "Internal validation inferences of significant genomic features in genome-wide screening," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 788-800, January.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:3:p:788-800
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    1. Yoav Benjamini & Abba M. Krieger & Daniel Yekutieli, 2006. "Adaptive linear step-up procedures that control the false discovery rate," Biometrika, Biometrika Trust, vol. 93(3), pages 491-507, September.
    2. Art B. Owen, 2005. "Variance of the number of false discoveries," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(3), pages 411-426, June.
    3. Allison, David B. & Gadbury, Gary L. & Heo, Moonseong & Fernandez, Jose R. & Lee, Cheol-Koo & Prolla, Tomas A. & Weindruch, Richard, 2002. "A mixture model approach for the analysis of microarray gene expression data," Computational Statistics & Data Analysis, Elsevier, vol. 39(1), pages 1-20, March.
    4. Cheng Cheng & Pounds Stanley B. & Boyett James M. & Pei Deqing & Kuo Mei-Ling & Roussel Martine F., 2004. "Statistical Significance Threshold Criteria For Analysis of Microarray Gene Expression Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-32, December.
    5. Efron, Bradley, 2004. "Large-Scale Simultaneous Hypothesis Testing: The Choice of a Null Hypothesis," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 96-104, January.
    6. Bair, Eric & Hastie, Trevor & Paul, Debashis & Tibshirani, Robert, 2006. "Prediction by Supervised Principal Components," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 119-137, March.
    7. 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, August.
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    1. Axel Gandy & Georg Hahn, 2016. "A Framework for Monte Carlo based Multiple Testing," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(4), pages 1046-1063, December.

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