J
John D. Storey
Researcher at Princeton University
Publications - 110
Citations - 35810
John D. Storey is an academic researcher from Princeton University. The author has contributed to research in topics: False discovery rate & Population. The author has an hindex of 46, co-authored 107 publications receiving 32151 citations. Previous affiliations of John D. Storey include University of Washington & Johns Hopkins University.
Papers
More filters
Journal ArticleDOI
Statistical significance for genomewide studies
John D. Storey,Robert Tibshirani +1 more
TL;DR: This work proposes an approach to measuring statistical significance in genomewide studies based on the concept of the false discovery rate, which offers a sensible balance between the number of true and false positives that is automatically calibrated and easily interpreted.
Journal ArticleDOI
A direct approach to false discovery rates
TL;DR: The calculation of the q‐value is discussed, the pFDR analogue of the p‐value, which eliminates the need to set the error rate beforehand as is traditionally done, and can yield an increase of over eight times in power compared with the Benjamini–Hochberg FDR method.
Journal ArticleDOI
The sva package for removing batch effects and other unwanted variation in high-throughput experiments
TL;DR: The sva package is described, which supports surrogate variable estimation with the sva function, direct adjustment for known batch effects with the ComBat function and adjustment for batch and latent variables in prediction problems with the fsva function.
Journal ArticleDOI
The positive false discovery rate: a Bayesian interpretation and the q-value
TL;DR: The Positive False Discovery Rate (pFDR) as mentioned in this paper is a modified version of the false discovery rate (FDR), which is used for exploratory analyses in which one is interested in finding several significant results among many tests.
Journal ArticleDOI
Empirical Bayes analysis of a microarray experiment
TL;DR: A simple nonparametric empirical Bayes model is introduced, which is used to guide the efficient reduction of the data to a single summary statistic per gene, and also to make simultaneous inferences concerning which genes were affected by the radiation.