Archive
Quantifying evidence for genetic association
Y. Yang and D. R. Bickel, “Minimum description length and empirical Bayes methods of identifying SNPs associated with disease,” Technical Report, Ottawa Institute of Systems Biology, COBRA Preprint Series, Article 74, available at biostats.bepress.com/cobra/ps/art74 (2010).
This manuscript adapts two new evidential, information-theoretic methods to the problem of detecting SNPs associated with disease on the basis of genome-wide association data. Both an application to coronary artery disease and an extensive set of simulation studies indicate that these parametric methods tend to be more reliable than a popular semi-parametric approach to estimating local false discovery rates. In addition, the paper reports that one of the two novel methods performs better than the other.
The abstract and the discussion section of the preprint provide more detailed summaries.
Medium-scale simultaneous inference
D. R. Bickel, “Minimum description length methods of medium-scale simultaneous inference,” Technical Report, Ottawa Institute of Systems Biology, available at tinyurl.com/36dm6lj (2010). Full preprint
Abstract— Nonparametric statistical methods developed for analyzing data for high numbers of genes, SNPs, or other biological features tend to have low efficiency for data with the smaller numbers of features such as proteins, metabolites, or, when expression is measured with conventional instruments, genes. For this medium-scale inference problem, the minimum description length (MDL) framework quantifies the amount of information in the data supporting a null or alternative hypothesis for each feature in terms of parametric model selection. Two new MDL techniques are proposed. First, using test statistics that are highly informative about the parameter of interest, the data are reduced to a single statistic per feature. This simplifying step is already implicit in conventional hypothesis testing and has been found effective in empirical Bayes applications to genomics data. Second, the codelength difference between the alternative and null hypotheses of any given feature can take advantage of information in the measurements from all other features by using those measurements to find the overall code of minimum length summed over those features. The techniques are applied to protein abundance data, demonstrating that a computationally efficient approximation that is close for a sufficiently large number of features works well even when the number of features is as low as 20.
Keywords: information criteria; minimum description length; model selection; reduced likelihood
Shrinkage estimation of expression fold change
Z. Montazeri*, C. M. Yanofsky*, and D. R. Bickel, “Shrinkage estimation of effect sizes as an alternative to hypothesis testing followed by estimation in high-dimensional biology: Applications to differential gene expression,” Statistical Applications in Genetics and Molecular Biology 9 (1) 23 (2010). Article | Software
* the first two authors contributed equally
Significance v. fold change
C. M. Yanofsky and D. R. Bickel, “Validation of differential gene expression algorithms: Application comparing fold change estimation to hypothesis testing,” BMC Bioinformatics 11, 63 (2010). Article
Empirical null conditioning
D. R. Bickel, “Estimating the null distribution for conditional inference and genome-scale screening,” Technical Report, Ottawa Institute of Systems Biology, arXiv.org:0910.0745 (2009). Full preprint
Shrinkage estimation vs. testing
Z. Montazeri, C. M. Yanofsky, and D. R. Bickel [the first two authors contributed equally], “Shrinkage estimation of gene expression fold change as an alternative to testing hypotheses of equivalent expression,” Technical Report, Ottawa Institute of Systems Biology, COBRA Preprint Series, Article 60, available at tinyurl.com/mwhnj2 (2009). Full preprint
Fold change estimation versus hypothesis testing
D. R. Bickel and C. M. Yanofsky, “Validation of differential gene expression algorithms: Application comparing fold change estimation to hypothesis testing,” Preprint, Ottawa Institute of Systems Biology, COBRA Preprint Series, Article 50, available at tinyurl.com/bw9jod (2009).
David Bickel, statistician 
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