A Measure of the Signal-to-Noise Ratio of Microarray Samples and Studies Using Gene Correlations

17Citations
Citations of this article
41Readers
Mendeley users who have this article in their library.

Abstract

Background: The quality of gene expression data can vary dramatically from platform to platform, study to study, and sample to sample. As reliable statistical analysis rests on reliable data, determining such quality is of the utmost importance. Quality measures to spot problematic samples exist, but they are platform-specific, and cannot be used to compare studies. Results: As a proxy for quality, we propose a signal-to-noise ratio for microarray data, the "Signal-to-Noise Applied to Gene Expression Experiments", or SNAGEE. SNAGEE is based on the consistency of gene-gene correlations. We applied SNAGEE to a compendium of 80 large datasets on 37 platforms, for a total of 24,380 samples, and assessed the signal-to-noise ratio of studies and samples. This allowed us to discover serious issues with three studies. We show that signal-to-noise ratios of both studies and samples are linked to the statistical significance of the biological results. Conclusions: We showed that SNAGEE is an effective way to measure data quality for most types of gene expression studies, and that it often outperforms existing techniques. Furthermore, SNAGEE is platform-independent and does not require raw data files. The SNAGEE R package is available in BioConductor. © 2012 Venet et al.

Cite

CITATION STYLE

APA

Venet, D., Detours, V., & Bersini, H. (2012). A Measure of the Signal-to-Noise Ratio of Microarray Samples and Studies Using Gene Correlations. PLoS ONE, 7(12). https://doi.org/10.1371/journal.pone.0051013

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free