IDEAS home Printed from https://ideas.repec.org/a/bpj/sagmbi/v11y2012i4n5.html

A Novel and Fast Normalization Method for High-Density Arrays

Author

Listed:
  • van Iterson Maarten

    (Center for Human and Clinical Genetics, Leiden University Medical Center)

  • Duijkers Floor A.M.

    (Department of Pediatric Oncology/Hematology, Erasmus University Medical Center-Sophia Children's Hospital)

  • Meijerink Jules P.P.

    (Department of Pediatric Oncology/Hematology, Erasmus University Medical Center-Sophia Children's Hospital)

  • Admiraal Pieter

    (Department of Pediatric Oncology/Hematology, Erasmus University Medical Center-Sophia Children's Hospital)

  • van Ommen Gert-Jan B.

    (Center for Human and Clinical Genetics, Leiden University Medical)

  • Boer Judith M.
  • van Noesel Max M.
  • Menezes Renee X.

Abstract

Background: Among the most commonly applied microarray normalization methods are intensity-dependent normalization methods such as lowess or loess algorithms. Their computational complexity makes them slow and thus less suitable for normalization of large datasets. Current implementations try to circumvent this problem by using a random subset of the data for normalization, but the impact of this modification has not been previously assessed. We developed a novel intensity-dependent normalization method for microarrays that is fast, simple and can include weighing of observations.Results: Our normalization method is based on the P-spline scatterplot smoother using all data points for normalization. We show that using a random subset of the data for normalization should be avoided as unstable results can be produced. However, in certain cases normalization based on an invariant subset is desirable, for example, when groups of samples before and after intervention are compared. We show in the context of DNA methylation arrays that a constant weighted P-spline normalization yields a more reliable normalization curve than the one obtained by normalization on the invariant subset only.Conclusions: Our novel intensity-dependent normalization method is simpler and faster than current loess algorithms, and can be applied to one- and two-colour array data, similar to normalization based on loess.Availability: An implementation of the method is currently available as an R package called TurboNorm from www.bioconductor.org .

Suggested Citation

  • van Iterson Maarten & Duijkers Floor A.M. & Meijerink Jules P.P. & Admiraal Pieter & van Ommen Gert-Jan B. & Boer Judith M. & van Noesel Max M. & Menezes Renee X., 2012. "A Novel and Fast Normalization Method for High-Density Arrays," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(4), pages 1-31, July.
    • Handle: RePEc:bpj:sagmbi:v:11:y:2012:i:4:n:5
    DOI: 10.1515/1544-6115.1753
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/1544-6115.1753
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/1544-6115.1753?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Nathaniel D. Heintzman & Gary C. Hon & R. David Hawkins & Pouya Kheradpour & Alexander Stark & Lindsey F. Harp & Zhen Ye & Leonard K. Lee & Rhona K. Stuart & Christina W. Ching & Keith A. Ching & Jess, 2009. "Histone modifications at human enhancers reflect global cell-type-specific gene expression," Nature, Nature, vol. 459(7243), pages 108-112, May.
    2. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167, June.
    3. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506, June.
    4. Inyoung Kim & Noah D. Cohen & Raymond J. Carroll, 2003. "Semiparametric Regression Splines in Matched Case-Control Studies," Biometrics, The International Biometric Society, vol. 59(4), pages 1158-1169, December.
    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. Wu, Ximing & Sickles, Robin, 2018. "Semiparametric estimation under shape constraints," Econometrics and Statistics, Elsevier, vol. 6(C), pages 74-89.
    2. Julie Vercelloni & M Julian Caley & Mohsen Kayal & Samantha Low-Choy & Kerrie Mengersen, 2014. "Understanding Uncertainties in Non-Linear Population Trajectories: A Bayesian Semi-Parametric Hierarchical Approach to Large-Scale Surveys of Coral Cover," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-9, November.
    3. Otto-Sobotka, Fabian & Salvati, Nicola & Ranalli, Maria Giovanna & Kneib, Thomas, 2019. "Adaptive semiparametric M-quantile regression," Econometrics and Statistics, Elsevier, vol. 11(C), pages 116-129.
    4. Becker, William, 2020. "Metafunctions for benchmarking in sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    5. Fan Fan & Yong Liu & A. Ford Ramsey & David J. Leatham, 2026. "Market Insurance and Risk Pooling in U.S. Crop Insurance," Agricultural Economics, International Association of Agricultural Economists, vol. 57(2), March.
    6. Simon N. Wood, 2020. "Inference and computation with generalized additive models and their extensions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 307-339, June.
    7. Sun, Shilin & Li, Qi & Hu, Wenyang & Liang, Zhongchao & Wang, Tianyang & Chu, Fulei, 2023. "Wind turbine blade breakage detection based on environment-adapted contrastive learning," Renewable Energy, Elsevier, vol. 219(P2).
    8. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.
    9. Hyunju Son & Youyi Fong, 2021. "Fast grid search and bootstrap‐based inference for continuous two‐phase polynomial regression models," Environmetrics, John Wiley & Sons, Ltd., vol. 32(3), May.
    10. Seebens, Holger, 2009. "Child Welfare and Old-Age Security in Female Headed Households in Tanzania," IZA Discussion Papers 3929, IZA Network @ LISER.
    11. Arūnas P. Verbyla & Joanne Faveri & John D. Wilkie & Tom Lewis, 2018. "Tensor Cubic Smoothing Splines in Designed Experiments Requiring Residual Modelling," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(4), pages 478-508, December.
    12. Michael Wegener & Göran Kauermann, 2017. "Forecasting in nonlinear univariate time series using penalized splines," Statistical Papers, Springer, vol. 58(3), pages 557-576, September.
    13. Bernhard Baumgartner & Daniel Guhl & Thomas Kneib & Winfried J. Steiner, 2018. "Flexible estimation of time-varying effects for frequently purchased retail goods: a modeling approach based on household panel data," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(4), pages 837-873, October.
    14. repec:plo:pone00:0212565 is not listed on IDEAS
    15. Zi Ye & Giles Hooker & Stephen P. Ellner, 2021. "Generalized Single Index Models and Jensen Effects on Reproduction and Survival," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(3), pages 492-512, September.
    16. Daniel Edinam Wormenor & Sampson Twumasi-Ankrah & Accam Burnett Tetteh, 2025. "Comparison of Semiparametric Models in the Presence of Noise and Outliers," Journal of Applied Mathematics, John Wiley & Sons, vol. 2025(1).
    17. Ferraccioli, Federico & Sangalli, Laura M. & Finos, Livio, 2022. "Some first inferential tools for spatial regression with differential regularization," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    18. Yichen Gao & Yu Zhang & Ximing Wu, 2015. "Penalized exponential series estimation of copula densities with an application to intergenerational dependence of body mass index," Empirical Economics, Springer, vol. 48(1), pages 61-81, February.
    19. Jaime Pinilla & Miguel Negrín, 2021. "Non-Parametric Generalized Additive Models as a Tool for Evaluating Policy Interventions," Mathematics, MDPI, vol. 9(4), pages 1-12, February.
    20. Marra Giampiero & Radice Rosalba, 2017. "A joint regression modeling framework for analyzing bivariate binary data in R," Dependence Modeling, De Gruyter, vol. 5(1), pages 268-294, December.
    21. Vahid Goodarzi Vanani & Davood Shahsavani & Mohammad Kazemi, 2025. "A robust partial linear model combining modified Huber loss function and variable selection," Statistical Papers, Springer, vol. 66(6), pages 1-28, October.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:bpj:sagmbi:v:11:y:2012:i:4:n:5. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyterbrill.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.