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High-throughput DNA methylation datasets for evaluating false discovery rate methodologies


  • Asomaning, N.
  • Archer, K.J.


When analyzing high-throughput genomic data, the multiple comparison problem is most often addressed through estimation of the false discovery rate (FDR), using methods such as the Benjamini & Hochberg, Benjamini & Yekutieli, the q-value method, or in controlling the family-wise error rate (FWER) using Holm’s step down method. To date, research studies that have compared various FDR/FWER methodologies have made use of limited simulation studies and/or have applied the methods to one or more microarray gene expression dataset(s). However, for microarray datasets the veracity of each null hypothesis tested is unknown so that an objective evaluation of performance cannot be rendered for application data. Due to the role of methylation in X-chromosome inactivation, we postulate that high-throughput methylation datasets may provide an appropriate forum for assessing the performance of commonly used FDR methodologies. These datasets preserve the complex correlation structure between probes, offering an advantage over simulated datasets. Using several methylation datasets, commonly used FDR methods including the q-value, Benjamini & Hochberg, and Benjamini & Yekutieli procedures as well as Holm’s step down method were applied to identify CpG sites that are differentially methylated when comparing healthy males to healthy females. The methods were compared with respect to their ability to identify CpG sites located on sex chromosomes as significant, by reporting the sensitivity, specificity, and observed FDR. These datasets are useful for characterizing the performance of multiple comparison procedures, and may find further utility in other tasks such as comparing variable selection capabilities of classification methods and evaluating the performance of meta-analytic methods for microarray data.

Suggested Citation

  • Asomaning, N. & Archer, K.J., 2012. "High-throughput DNA methylation datasets for evaluating false discovery rate methodologies," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1748-1756.
  • Handle: RePEc:eee:csdana:v:56:y:2012:i:6:p:1748-1756 DOI: 10.1016/j.csda.2011.10.020

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    References listed on IDEAS

    1. Garcia-Magariños Manuel & Antoniadis Anestis & Cao Ricardo & González-Manteiga Wenceslao, 2010. "Lasso Logistic Regression, GSoft and the Cyclic Coordinate Descent Algorithm: Application to Gene Expression Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-30, August.
    2. Smyth Gordon K, 2004. "Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-28, February.
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