Statistical Applications in Genetics and Molecular Biology
Editor-in-Chief: Stumpf, Michael P.H.
6 Issues per year
IMPACT FACTOR 2016: 0.646
5-year IMPACT FACTOR: 1.191
CiteScore 2016: 0.94
SCImago Journal Rank (SJR) 2016: 0.625
Source Normalized Impact per Paper (SNIP) 2016: 0.596
Mathematical Citation Quotient (MCQ) 2016: 0.06
Accurate Ranking of Differentially Expressed Genes by a Distribution-Free Shrinkage Approach
High-dimensional case-control analysis is encountered in many different settings in genomics. In order to rank genes accordingly, many different scores have been proposed, ranging from ad hoc modifications of the ordinary t statistic to complicated hierarchical Bayesian models.Here, we introduce the shrinkage t statistic that is based on a novel and model-free shrinkage estimate of the variance vector across genes. This is derived in a quasi-empirical Bayes setting. The new rank score is fully automatic and requires no specification of parameters or distributions. It is computationally inexpensive and can be written analytically in closed form.Using a series of synthetic and three real expression data we studied the quality of gene rankings produced by the shrinkage t statistic. The new score consistently leads to highly accurate rankings for the complete range of investigated data sets and all considered scenarios for across-gene variance structures.
Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.