@article {38194,
	title = {A decision-theory approach to interpretable set analysis for high-dimensional data},
	journal = {BiometricsBiometrics},
	volume = {69},
	year = {2013},
	note = {http://www.ncbi.nlm.nih.gov/pubmed/23909925?dopt=Abstract},
	type = {10.1111/biom.12060},
	abstract = {A key problem in high-dimensional significance analysis is to find pre-defined sets that show enrichment for a statistical signal of interest; the classic example is the enrichment of gene sets for differentially expressed genes. Here, we propose a new decision-theory approach to the analysis of gene sets which focuses on estimating the fraction of non-null variables in a set. We introduce the idea of "atoms," non-overlapping sets based on the original pre-defined set annotations. Our approach focuses on finding the union of atoms that minimizes a weighted average of the number of false discoveries and missed discoveries. We introduce a new false discovery rate for sets, called the atomic false discovery rate (afdr), and prove that the optimal estimator in our decision-theory framework is to threshold the afdr. These results provide a coherent and interpretable framework for the analysis of sets that addresses the key issues of overlapping annotations and difficulty in interpreting p values in both competitive and self-contained tests. We illustrate our method and compare it to a popular existing method using simulated examples, as well as gene-set and brain ROI data analyses.},
	author = {Boca, Simina M. and H{\'e}ctor Corrada Bravo and Caffo, Brian and Leek, Jeffrey T. and Parmigiani, Giovanni}
}
