title: A measure of the signal-to-noise ratio of microarray samples and studies using gene correlations.
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.
authors.affiliation: Institut de Recherches Interdisciplinaires et de Développements en Intelligence Artificielle, Université Libre de Bruxelles, Brussels, Belgium. davenet@ulb.ac.be
authors.forename: David
authors.initials: D
authors.lastname: Venet
authors.forename: Vincent
authors.initials: V
authors.lastname: Detours
authors.forename: Hugues
authors.initials: H
authors.lastname: Bersini
mesh_terms: D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D059629:Signal-To-Noise Ratio; D012984:Software