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Gene set bagging for estimating the probability a statistically significant result will replicate

Author(s): Jaffe, Andrew E; Storey, John D; Ji, Hongkai; Leek, Jeffrey T

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Abstract: Significance analysis plays a major role in identifying and ranking genes, transcription factor binding sites, DNA methylation regions, and other high-throughput features associated with illness. We propose a new approach, called gene set bagging, for measuring the probability that a gene set replicates in future studies. Gene set bagging involves resampling the original high-throughput data, performing gene-set analysis on the resampled data, and confirming that biological categories replicate in the bagged samples. Using both simulated and publicly-available genomics data, we demonstrate that significant categories in a gene set enrichment analysis may be unstable when subjected to resampling. We show our method estimates the replication probability (R), the probability that a gene set will replicate as a significant result in future studies, and show in simulations that this method reflects replication better than each set’s p-value. Our results suggest that gene lists based on p-values are not necessarily stable, and therefore additional steps like gene set bagging may improve biological inference on gene sets.
Publication Date: 12-Dec-2013
Citation: Jaffe, Andrew E, Storey, John D, Ji, Hongkai, Leek, Jeffrey T. (2013). Gene set bagging for estimating the probability a statistically significant result will replicate. BMC Bioinformatics, 14 (1), 360 - 360. doi:10.1186/1471-2105-14-360
DOI: doi:10.1186/1471-2105-14-360
ISSN: 1471-2105
Pages: 360 - 360
Type of Material: Journal Article
Journal/Proceeding Title: BMC Bioinformatics
Version: Final published version. This is an open access article.

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