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Pre-validation and inference in microarrays

Author

Listed:
  • Tibshirani Robert J.

    (Stanford University)

  • Efron Brad

    (Stanford University)

Abstract

In microarray studies, an important problem is to compare a predictor of disease outcome derived from gene expression levels to standard clinical predictors. Comparing them on the same dataset that was used to derive the microarray predictor can lead to results strongly biased in favor of the microarray predictor. We propose a new technique called ``pre-validation'' for making a fairer comparison between the two sets of predictors. We study the method analytically and explore its application in a recent study on breast cancer.

Suggested Citation

  • Tibshirani Robert J. & Efron Brad, 2002. "Pre-validation and inference in microarrays," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 1(1), pages 1-20, August.
    • Handle: RePEc:bpj:sagmbi:v:1:y:2002:i:1:n:1
    DOI: 10.2202/1544-6115.1000
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    Cited by:

    1. Dennis Kostka & Rainer Spang, 2008. "Microarray Based Diagnosis Profits from Better Documentation of Gene Expression Signatures," PLOS Computational Biology, Public Library of Science, vol. 4(2), pages 1-6, February.
    2. Arivazhagan Arimappamagan & Kumaravel Somasundaram & Kandavel Thennarasu & Sreekanthreddy Peddagangannagari & Harish Srinivasan & Bangalore C Shailaja & Cini Samuel & Irene Rosita Pia Patric & Sudhans, 2013. "A Fourteen Gene GBM Prognostic Signature Identifies Association of Immune Response Pathway and Mesenchymal Subtype with High Risk Group," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-14, April.
    3. Lama, Nicola & Boracchi, Patrizia & Biganzoli, Elia, 2009. "Exploration of distributional models for a novel intensity-dependent normalization procedure in censored gene expression data," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1906-1922, March.
    4. Ross L. Prentice & Mary Pettinger & Garnet L. Anderson, 2005. "Statistical Issues Arising in the Women's Health Initiative," Biometrics, The International Biometric Society, vol. 61(4), pages 899-911, December.
    5. Lida Qiu & Deyong Kang & Chuan Wang & Wenhui Guo & Fangmeng Fu & Qingxiang Wu & Gangqin Xi & Jiajia He & Liqin Zheng & Qingyuan Zhang & Xiaoxia Liao & Lianhuang Li & Jianxin Chen & Haohua Tu, 2022. "Intratumor graph neural network recovers hidden prognostic value of multi-biomarker spatial heterogeneity," Nature Communications, Nature, vol. 13(1), pages 1-12, December.

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