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Archive for the ‘gene expression’ Category

Software for local false discovery rate estimation

15 August 2011 Leave a comment

LFDR-MLE is a suite of R functions for the estimation of local false discovery rates by maximum likelihood under a two-group parametric mixture model of test statistics.

Observed confidence levels for microarrays, etc.

22 June 2011 Leave a comment

D. R. Bickel, “Estimating the null distribution to adjust observed confidence levels for genome-scale screening,” Biometrics 67, 363-370 (2011). Abstract and article | French abstract | Supplementary material | Simple explanation

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This paper describes the first application of observed confidence levels to data of high-dimensional biology. The proposed method for multiple comparisons can take advantage of the estimated null distribution without any prior distribution. The new method is applied to microarray data to illustrate its advantages.

Confidence intervals for semi-parametric empirical Bayes

7 January 2011 2 comments

D. R. Bickel, “Large-scale interval and point estimates from an empirical Bayes extension of confidence posteriors,” Technical Report, Ottawa Institute of Systems Biology, arXiv:1012.6033 (2010). Full preprint

To address multiple comparison problems in high-dimensional biology, this paper introduces shrunken point estimates for feature prioritization and shrunken confidence intervals to indicate the uncertainty of the point estimates. The new point and interval estimates are applied to gene expression data and are found to be conservative by simulation, as expected from limiting cases. Unlike the parametric empirical Bayes estimates, the new estimates are compatible with the semi-parametric approach to local false discovery rate estimation that has been extensively developed and applied over the last decade. This is carried out by replacing strong parametric assumptions with the confidence posterior theory of papers in the presses of Biometrics and Communications in Statistics — Theory and Methods.

Inference to the best explanation

13 September 2010 Leave a comment

D. R. Bickel, “The strength of statistical evidence for composite hypotheses: Inference to the best explanation,” Technical Report, Ottawa Institute of Systems Biology, COBRA Preprint Series, Article 71, available at biostats.bepress.com/cobra/ps/art71 (2010).

Shrinkage estimation of expression fold change

9 June 2010 Leave a comment

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

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Significance v. fold change

28 January 2010 Leave a comment

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

11 October 2009 Leave a comment

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