IDEAS home Printed from https://ideas.repec.org/p/bep/uwabio/1055.html
   My bibliography  Save this paper

Calibrating Observed Differential Gene Expression for the Multiplicity of Genes on the Array

Author

Listed:
  • Yingye Zheng

    (Fred Hutchinson Cancer Research Center)

  • Margaret Pepe

    (University of Washington)

Abstract

In a gene expression array study, the expression levels of thousands of genes are monitored simultaneously across various biological conditions on a small set of subjects. One goal of such studies is to explore a large pool of genes in order to select a subset of genes that appear to be differently expressed for further investigation. Of particular interest here is how to select the top k genes once genes are ranked based on their evidence for differential expression in two tissue types. We consider statistical methods that provide a more rigorous and intuitively appealing selection process for k. We propose to choose genes based on adjusted p-values (AP values). The AP values are calculated with a resampling based algorithm assuming that no genes are truly differentially expressed, and take into account the multiplicity and dependence encountered in microarray data. Using both simulated data and real microarray data, we assess and compare the performance of our new method with existing methods. The intuitive basis for the AP values and the fact that our procedure has operating characteristics at least as good as existing procedures make it attractive for practical application.

Suggested Citation

  • Yingye Zheng & Margaret Pepe, 2004. "Calibrating Observed Differential Gene Expression for the Multiplicity of Genes on the Array," UW Biostatistics Working Paper Series 1055, Berkeley Electronic Press.
    • Handle: RePEc:bep:uwabio:1055
    Note: oai:bepress.com:uwbiostat-1055
    as

    Download full text from publisher

    File URL: http://www.bepress.com/cgi/viewcontent.cgi?article=1055&context=uwbiostat
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Margaret Sullivan Pepe & Gary Longton & Garnet L. Anderson & Michel Schummer, 2003. "Selecting Differentially Expressed Genes from Microarray Experiments," Biometrics, The International Biometric Society, vol. 59(1), pages 133-142, March.
    Full references (including those not matched with items on IDEAS)

    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. Li-Xuan Qin & Steven G. Self, 2006. "The Clustering of Regression Models Method with Applications in Gene Expression Data," Biometrics, The International Biometric Society, vol. 62(2), pages 526-533, June.
    2. Gong Chen & Qing Zhou, 2010. "Heterogeneity in DNA Multiple Alignments: Modeling, Inference, and Applications in Motif Finding," Biometrics, The International Biometric Society, vol. 66(3), pages 694-704, September.
    3. Ahmed Hossain & Hafiz T.A. Khan, 2016. "Identification of genomic markers correlated with sensitivity in solid tumors to Dasatinib using sparse principal components," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(14), pages 2538-2549, October.
    4. Man-Jen Hsu & Huey-Miin Hsueh, 2013. "The linear combinations of biomarkers which maximize the partial area under the ROC curves," Computational Statistics, Springer, vol. 28(2), pages 647-666, April.
    5. Osamu Komori, 2011. "A boosting method for maximization of the area under the ROC curve," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 63(5), pages 961-979, October.
    6. Juana-María Vivo & Manuel Franco & Donatella Vicari, 2018. "Rethinking an ROC partial area index for evaluating the classification performance at a high specificity range," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(3), pages 683-704, September.
    7. Hossain Ahmed & Beyene Joseph, 2013. "Estimation of weighted log partial area under the ROC curve and its application to MicroRNA expression data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(6), pages 743-755, December.
    8. Pascale Leroy & Andrea Tham & Hofer Wong & Rachel Tenney & Chun Chen & Rachel Stiner & John R Balmes & Agnès C Paquet & Mehrdad Arjomandi, 2015. "Inflammatory and Repair Pathways Induced in Human Bronchoalveolar Lavage Cells with Ozone Inhalation," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-28, June.
    9. Xiang, Qinfang & Edwards, Jode & Gadbury, Gary L., 2006. "Interval estimation in a finite mixture model: Modeling P-values in multiple testing applications," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 570-586, November.
    10. Shigeyuki Matsui & Shu Zeng & Takeharu Yamanaka & John Shaughnessy, 2008. "Sample Size Calculations Based on Ranking and Selection in Microarray Experiments," Biometrics, The International Biometric Society, vol. 64(1), pages 217-226, March.
    11. Yu, Wenbao & Park, Taesung, 2015. "Two simple algorithms on linear combination of multiple biomarkers to maximize partial area under the ROC curve," Computational Statistics & Data Analysis, Elsevier, vol. 88(C), pages 15-27.
    12. Ahmed Hossain & Joseph Beyene, 2015. "Application of skew-normal distribution for detecting differential expression to microRNA data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(3), pages 477-491, March.
    13. Debashis Ghosh & Arul Chinnaiyan, 2004. "Covariate adjustment in the analysis of microarray data from clinical studies," The University of Michigan Department of Biostatistics Working Paper Series 1030, Berkeley Electronic Press.
    14. Jialiang Li & Jason P. Fine, 2010. "Weighted area under the receiver operating characteristic curve and its application to gene selection," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(4), pages 673-692, August.
    15. Mark A. van de Wiel & Kyung In Kim, 2007. "Estimating the False Discovery Rate Using Nonparametric Deconvolution," Biometrics, The International Biometric Society, vol. 63(3), pages 806-815, September.
    16. Manuel Franco & Juana-María Vivo, 2021. "Evaluating the Performances of Biomarkers over a Restricted Domain of High Sensitivity," Mathematics, MDPI, vol. 9(21), pages 1-20, November.
    17. 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.

    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:bep:uwabio:1055. 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: Christopher F. Baum (email available below). General contact details of provider: http://www.bepress.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.