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Comparison and visualisation of agreement for paired lists of rankings

Author

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  • 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
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    References listed on IDEAS

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    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.
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