IDEAS home Printed from https://ideas.repec.org/a/spr/testjl/v12y2003i1p1-77.html
   My bibliography  Save this article

Resampling-based multiple testing for microarray data analysis

Author

Listed:
  • Youngchao Ge
  • Sandrine Dudoit
  • Terence Speed

Abstract

No abstract is available for this item.

Suggested Citation

  • Youngchao Ge & Sandrine Dudoit & Terence Speed, 2003. "Resampling-based multiple testing for microarray data analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 12(1), pages 1-77, June.
    • Handle: RePEc:spr:testjl:v:12:y:2003:i:1:p:1-77
    DOI: 10.1007/BF02595811
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/BF02595811
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/BF02595811?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Efron B. & Tibshirani R. & Storey J.D. & Tusher V., 2001. "Empirical Bayes Analysis of a Microarray Experiment," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1151-1160, 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, August.
    3. Westfall P. H. & Soper K. A., 2001. "Using Priors to Improve Multiple Animal Carcinogenicity Tests," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 827-834, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Edward L. Korn & Boris Freidlin, 2008. "A Note on Controlling the Number of False Positives," Biometrics, The International Biometric Society, vol. 64(1), pages 227-231, March.
    2. Tsai, Chen-An & Chen, James J., 2007. "Kernel estimation for adjusted p-values in multiple testing," Computational Statistics & Data Analysis, Elsevier, vol. 51(8), pages 3885-3897, May.
    3. Ebrahimi, Nader, 2008. "Simultaneous control of false positives and false negatives in multiple hypotheses testing," Journal of Multivariate Analysis, Elsevier, vol. 99(3), pages 437-450, March.
    4. Guo Wenge & Peddada Shyamal, 2008. "Adaptive Choice of the Number of Bootstrap Samples in Large Scale Multiple Testing," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-21, March.
    5. Dazard, Jean-Eudes & Sunil Rao, J., 2012. "Joint adaptive mean–variance regularization and variance stabilization of high dimensional data," Computational Statistics & Data Analysis, Elsevier, vol. 56(7), pages 2317-2333.
    6. Debashis Ghosh, 2006. "Shrunken p-Values for Assessing Differential Expression with Applications to Genomic Data Analysis," Biometrics, The International Biometric Society, vol. 62(4), pages 1099-1106, December.
    7. Yifan Gu & Yang Qi & Pulin Gong, 2019. "Rich-club connectivity, diverse population coupling, and dynamical activity patterns emerging from local cortical circuits," PLOS Computational Biology, Public Library of Science, vol. 15(4), pages 1-34, April.
    8. Jesse Hemerik & Jelle Goeman, 2018. "Exact testing with random permutations," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(4), pages 811-825, December.
    9. Wu, Jincao & Patwa, Tasneem H. & Lubman, David M. & Ghosh, Debashis, 2009. "Identification of differentially expressed spatial clusters using humoral response microarray data," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3094-3102, June.
    10. Ge, Yongchao & Sealfon, Stuart C. & Tseng, Chi-Hong & Speed, Terence P., 2007. "A Holm-type procedure controlling the false discovery rate," Statistics & Probability Letters, Elsevier, vol. 77(18), pages 1756-1762, December.
    11. Miecznikowski, Jeffrey C. & Gold, David & Shepherd, Lori & Liu, Song, 2011. "Deriving and comparing the distribution for the number of false positives in single step methods to control k-FWER," Statistics & Probability Letters, Elsevier, vol. 81(11), pages 1695-1705, November.
    12. Baolin Wu & Zhong Guan & Hongyu Zhao, 2006. "Parametric and Nonparametric FDR Estimation Revisited," Biometrics, The International Biometric Society, vol. 62(3), pages 735-744, September.
    13. Kai Yu & William Wheeler & Qizhai Li & Andrew W. Bergen & Neil Caporaso & Nilanjan Chatterjee & Jinbo Chen, 2010. "A Partially Linear Tree-based Regression Model for Multivariate Outcomes," Biometrics, The International Biometric Society, vol. 66(1), pages 89-96, March.
    14. Zhenchuan Wang & Qiuying Sha & Shuanglin Zhang, 2016. "Joint Analysis of Multiple Traits Using "Optimal" Maximum Heritability Test," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-12, March.
    15. Lippmann, Quentin, 2022. "Gender and lawmaking in times of quotas," Journal of Public Economics, Elsevier, vol. 207(C).
    16. Pittelkow Yvonne E & Wilson Susan R, 2003. "Visualisation of Gene Expression Data - the GE-biplot, the Chip-plot and the Gene-plot," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 2(1), pages 1-19, September.
    17. Sheng, Xuguang & Yang, Jingyun, 2013. "An adaptive truncated product method for combining dependent p-values," Economics Letters, Elsevier, vol. 119(2), pages 180-182.
    18. Xuesong Yu & Timothy W. Randolph & Hua Tang & Li Hsu, 2010. "Detecting Genomic Aberrations Using Products in a Multiscale Analysis," Biometrics, The International Biometric Society, vol. 66(3), pages 684-693, September.
    19. Bergamelli, Michele & Bianchi, Annamaria & Khalaf, Lynda & Urga, Giovanni, 2019. "Combining p-values to test for multiple structural breaks in cointegrated regressions," Journal of Econometrics, Elsevier, vol. 211(2), pages 461-482.
    20. Bickel David R., 2008. "Correcting the Estimated Level of Differential Expression for Gene Selection Bias: Application to a Microarray Study," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-27, March.
    21. Mark van der Laan & Sandrine Dudoit & Katherine Pollard, 2004. "Multiple Testing. Part II. Step-Down Procedures for Control of the Family-Wise Error Rate," U.C. Berkeley Division of Biostatistics Working Paper Series 1138, Berkeley Electronic Press.
    22. Fu, Hsuan & Luger, Richard, 2022. "Multiple testing of the forward rate unbiasedness hypothesis across currencies," Journal of Empirical Finance, Elsevier, vol. 68(C), pages 232-245.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wen Shi & Xi Chen & Jennifer Shang, 2019. "An Efficient Morris Method-Based Framework for Simulation Factor Screening," INFORMS Journal on Computing, INFORMS, vol. 31(4), pages 745-770, October.
    2. Cipolli III, William & Hanson, Timothy & McLain, Alexander C., 2016. "Bayesian nonparametric multiple testing," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 64-79.
    3. Guo Wenge & Peddada Shyamal, 2008. "Adaptive Choice of the Number of Bootstrap Samples in Large Scale Multiple Testing," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-21, March.
    4. Alessio Farcomeni, 2006. "More Powerful Control of the False Discovery Rate Under Dependence," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 15(1), pages 43-73, May.
    5. Nik Tuzov & Frederi Viens, 2011. "Mutual fund performance: false discoveries, bias, and power," Annals of Finance, Springer, vol. 7(2), pages 137-169, May.
    6. Leek Jeffrey T & Storey John D., 2011. "The Joint Null Criterion for Multiple Hypothesis Tests," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-22, June.
    7. Ishwaran, Hemant & Sunil Rao, J., 2008. "Clustering gene expression profile data by selective shrinkage," Statistics & Probability Letters, Elsevier, vol. 78(12), pages 1490-1497, September.
    8. Kenneth Rice & David Spiegelhalter, 2006. "A Simple Diagnostic Plot Connecting Robust Estimation, Outlier Detection, and False Discovery Rates," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(10), pages 1131-1147.
    9. Hironori Fujisawa & Takayuki Sakaguchi, 2012. "Optimal significance analysis of microarray data in a class of tests whose null statistic can be constructed," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(2), pages 280-300, June.
    10. Andrew Y. Chen, 2022. "Most claimed statistical findings in cross-sectional return predictability are likely true," Papers 2206.15365, arXiv.org, revised Mar 2024.
    11. Friguet, Chloé & Causeur, David, 2011. "Estimation of the proportion of true null hypotheses in high-dimensional data under dependence," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2665-2676, September.
    12. T. Tony Cai & Wenguang Sun & Weinan Wang, 2019. "Covariate‐assisted ranking and screening for large‐scale two‐sample inference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(2), pages 187-234, April.
    13. Laurent Barras & Olivier Scaillet & Russ Wermers, 2010. "False Discoveries in Mutual Fund Performance: Measuring Luck in Estimated Alphas," Journal of Finance, American Finance Association, vol. 65(1), pages 179-216, February.
    14. Patrick Kline & Christopher Walters, 2021. "Reasonable Doubt: Experimental Detection of Job‐Level Employment Discrimination," Econometrica, Econometric Society, vol. 89(2), pages 765-792, March.
    15. Patrick Kline & Evan K Rose & Christopher R Walters, 2022. "Systemic Discrimination Among Large U.S. Employers [“Teachers and Student Achievement in the Chicago Public High Schools,”]," The Quarterly Journal of Economics, Oxford University Press, vol. 137(4), pages 1963-2036.
    16. Bickel David R., 2013. "Simple estimators of false discovery rates given as few as one or two p-values without strong parametric assumptions," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(4), pages 529-543, August.
    17. Joshua Habiger & Edsel Peña, 2011. "Randomised -values and nonparametric procedures in multiple testing," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(3), pages 583-604.
    18. Habiger, Joshua D. & Peña, Edsel A., 2014. "Compound p-value statistics for multiple testing procedures," Journal of Multivariate Analysis, Elsevier, vol. 126(C), pages 153-166.
    19. Ji Tieming & Liu Peng & Nettleton Dan, 2012. "Borrowing Information Across Genes and Experiments for Improved Error Variance Estimation in Microarray Data Analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(3), pages 1-29, May.
    20. Dørum Guro & Snipen Lars & Solheim Margrete & Saebo Solve, 2011. "Smoothing Gene Expression Data with Network Information Improves Consistency of Regulated Genes," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-26, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:testjl:v:12:y:2003:i:1:p:1-77. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.