Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Highly Accessed Methodology article

A statistical approach to selecting and confirming validation targets in -omics experiments

Jeffrey T Leek1*, Margaret A Taub1 and Jason L Rasgon2

Author Affiliations

1 Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205-2179, USA

2 Department of Entomology, The Center for Infectious Disease Dynamics, and the Huck Institutes of Life Sciences, University Park, PA 16802, USA

For all author emails, please log on.

BMC Bioinformatics 2012, 13:150  doi:10.1186/1471-2105-13-150

Published: 27 June 2012

Abstract

Background

Genomic technologies are, by their very nature, designed for hypothesis generation. In some cases, the hypotheses that are generated require that genome scientists confirm findings about specific genes or proteins. But one major advantage of high-throughput technology is that global genetic, genomic, transcriptomic, and proteomic behaviors can be observed. Manual confirmation of every statistically significant genomic result is prohibitively expensive. This has led researchers in genomics to adopt the strategy of confirming only a handful of the most statistically significant results, a small subset chosen for biological interest, or a small random subset. But there is no standard approach for selecting and quantitatively evaluating validation targets.

Results

Here we present a new statistical method and approach for statistically validating lists of significant results based on confirming only a small random sample. We apply our statistical method to show that the usual practice of confirming only the most statistically significant results does not statistically validate result lists. We analyze an extensively validated RNA-sequencing experiment to show that confirming a random subset can statistically validate entire lists of significant results. Finally, we analyze multiple publicly available microarray experiments to show that statistically validating random samples can both (i) provide evidence to confirm long gene lists and (ii) save thousands of dollars and hundreds of hours of labor over manual validation of each significant result.

Conclusions

For high-throughput -omics studies, statistical validation is a cost-effective and statistically valid approach to confirming lists of significant results.