IDEAS home Printed from https://ideas.repec.org/a/bpj/sagmbi/v16y2017i1p31-45n4.html
   My bibliography  Save this article

Comparison and visualisation of agreement for paired lists of rankings

Author

Listed:
  • Donald Margaret R.

    (Stats Central, University of New South Wales, Anzac Parade, Kensington, NSW, 2052, Australia)

  • Wilson Susan R.

    (Stats Central, University of New South Wales, Anzac Parade, Kensington, NSW, 2052, Australia)

Abstract

Output from analysis of a high-throughput ‘omics’ experiment very often is a ranked list. One commonly encountered example is a ranked list of differentially expressed genes from a gene expression experiment, with a length of many hundreds of genes. There are numerous situations where interest is in the comparison of outputs following, say, two (or more) different experiments, or of different approaches to the analysis that produce different ranked lists. Rather than considering exact agreement between the rankings, following others, we consider two ranked lists to be in agreement if the rankings differ by some fixed distance. Generally only a relatively small subset of the k top-ranked items will be in agreement. So the aim is to find the point k at which the probability of agreement in rankings changes from being greater than 0.5 to being less than 0.5. We use penalized splines and a Bayesian logit model, to give a nonparametric smooth to the sequence of agreements, as well as pointwise credible intervals for the probability of agreement. Our approach produces a point estimate and a credible interval for k. R code is provided. The method is applied to rankings of genes from breast cancer microarray experiments.

Suggested Citation

  • Donald Margaret R. & Wilson Susan R., 2017. "Comparison and visualisation of agreement for paired lists of rankings," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(1), pages 31-45, March.
    • Handle: RePEc:bpj:sagmbi:v:16:y:2017:i:1:p:31-45:n:4
    DOI: 10.1515/sagmb-2016-0036
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/sagmb-2016-0036
    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/sagmb-2016-0036?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. Crainiceanu, Ciprian M. & Ruppert, David & Wand, Matthew P., 2005. "Bayesian Analysis for Penalized Spline Regression Using WinBUGS," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i14).
    2. Marley, Jennifer K. & Wand, Matthew P., 2010. "Non-Standard Semiparametric Regression via BRugs," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 37(i05).
    3. Peter Hall & Michael G. Schimek, 2012. "Moderate-Deviation-Based Inference for Random Degeneration in Paired Rank Lists," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 661-672, June.
    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. Chiara Bocci & Emilia Rocco, 2014. "Estimates for geographical domains through geoadditive models in presence of incomplete geographical information," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(2), pages 283-305, June.
    2. Alexandre Rodrigues & Peter Diggle & Renato Assuncao, 2010. "Semiparametric approach to point source modelling in epidemiology and criminology," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(3), pages 533-542, May.
    3. Erin E. Gabriel & Michael J. Daniels & M. Elizabeth Halloran, 2016. "Comparing biomarkers as trial level general surrogates," Biometrics, The International Biometric Society, vol. 72(4), pages 1046-1054, December.
    4. Sarah Brown & Pulak Ghosh & Bhuvanesh Pareek & Karl Taylor, 2017. "Financial Hardship and Saving Behaviour: Bayesian Analysis of British Panel Data," Working Papers 2017011, The University of Sheffield, Department of Economics.
    5. Welham, S.J. & Thompson, R., 2009. "A note on bimodality in the log-likelihood function for penalized spline mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 920-931, February.
    6. Lauren Hund & Jarvis T. Chen & Nancy Krieger & Brent A. Coull, 2012. "A Geostatistical Approach to Large-Scale Disease Mapping with Temporal Misalignment," Biometrics, The International Biometric Society, vol. 68(3), pages 849-858, September.
    7. Arboretti, Rosa & Bonnini, Stefano & Corain, Livio & Salmaso, Luigi, 2014. "A permutation approach for ranking of multivariate populations," Journal of Multivariate Analysis, Elsevier, vol. 132(C), pages 39-57.
    8. Weining Shen & Subhashis Ghosal, 2015. "Adaptive Bayesian Procedures Using Random Series Priors," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 1194-1213, December.
    9. Yanwei Zhang & Vanja Dukic, 2013. "Predicting Multivariate Insurance Loss Payments Under the Bayesian Copula Framework," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 80(4), pages 891-919, December.
    10. Brent A Coull, 2011. "A Random Intercepts–Functional Slopes Model for Flexible Assessment of Susceptibility in Longitudinal Designs," Biometrics, The International Biometric Society, vol. 67(2), pages 486-494, June.
    11. Stephen N. Freeman & Nicholas J. B. Isaac & Panagiotis Besbeas & Emily B. Dennis & Byron J. T. Morgan, 2021. "A Generic Method for Estimating and Smoothing Multispecies Biodiversity Indicators Using Intermittent Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(1), pages 71-89, March.
    12. Maura Mezzetti & Daniele Borzelli & Andrea d’Avella, 2022. "A Bayesian approach to model individual differences and to partition individuals: case studies in growth and learning curves," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(5), pages 1245-1271, December.
    13. Antonella Plaia & Simona Buscemi & Johannes Fürnkranz & Eneldo Loza Mencía, 2022. "Comparing Boosting and Bagging for Decision Trees of Rankings," Journal of Classification, Springer;The Classification Society, vol. 39(1), pages 78-99, March.
    14. Aldo Gardini & Carlo Trivisano & Enrico Fabrizi, 2021. "Bayesian Analysis of ANOVA and Mixed Models on the Log-Transformed Response Variable," Psychometrika, Springer;The Psychometric Society, vol. 86(2), pages 619-641, June.
    15. Eve Bohnett & Jessica Schulz & Robert Dobbs & Thomas Hoctor & Dave Hulse & Bilal Ahmad & Wajid Rashid & Hardin Waddle, 2023. "Shorebird Monitoring Using Spatially Explicit Occupancy and Abundance," Land, MDPI, vol. 12(4), pages 1-15, April.
    16. Laurini, Fabrizio & Pauli, Francesco, 2009. "Smoothing sample extremes: The mixed model approach," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3842-3854, September.
    17. Botts, Carsten H. & Daniels, Michael J., 2008. "A flexible approach to Bayesian multiple curve fitting," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5100-5120, August.
    18. Daewon Yang & Taeryon Choi & Eric Lavigne & Yeonseung Chung, 2022. "Non‐parametric Bayesian covariate‐dependent multivariate functional clustering: An application to time‐series data for multiple air pollutants," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1521-1542, November.
    19. M�rcio Poletti Laurini, 2014. "Dynamic functional data analysis with non-parametric state space models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(1), pages 142-163, January.
    20. Wood, Simon N., 2016. "Just Another Gibbs Additive Modeler: Interfacing JAGS and mgcv," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 75(i07).

    More about this item

    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:16:y:2017:i:1:p:31-45:n:4. 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.degruyter.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.