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.
Original language | English (US) |
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Article number | 360 |
Journal | BMC bioinformatics |
Volume | 14 |
Issue number | 1 |
DOIs | |
State | Published - Dec 12 2013 |
All Science Journal Classification (ASJC) codes
- Applied Mathematics
- Molecular Biology
- Structural Biology
- Biochemistry
- Computer Science Applications
Keywords
- DNA methylation
- Gene expression
- Gene ontology
- Gene set enrichment analysis