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BMC Bioinformatics. 2013 Dec 12;14:360. doi: 10.1186/1471-2105-14-360.

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

Author information

1
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore MD 21205, USA. jleek@jhsph.edu.

Abstract

BACKGROUND:

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.

RESULTS:

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.

CONCLUSIONS:

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.

PMID:
24330332
PMCID:
PMC3890500
DOI:
10.1186/1471-2105-14-360
[Indexed for MEDLINE]
Free PMC Article

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