Proportion statistics to detect differentially expressed genes: a comparison with log-ratio statistics

• Main Authors: Bergemann Tracy L, Wilson Jason
• Format: Article
• Language: English
• Published: BMC 2011-06-01
• Series: BMC Bioinformatics
• Online Access: http://www.biomedcentral.com/1471-2105/12/228

<p>Abstract</p> <p>Background</p> <p>In genetic transcription research, gene expression is typically reported in a test sample relative to a reference sample. Laboratory assays that measure gene expression levels, from Q-RT-PCR to microarrays to RNA-Seq experiments, will compare two samples to the same genetic sequence of interest. Standard practice is to use the log₂-ratio as the measure of relative expression. There are drawbacks to using this measurement, including unstable ratios when the denominator is small. This paper suggests an alternative estimate based on a proportion that is just as simple to calculate, just as intuitive, with the added benefit of greater numerical stability.</p> <p>Results</p> <p>Analysis of two groups of mice measured with 16 cDNA microarrays found similar results between the previously used methods and our proposed methods. In a study of liver and kidney samples measured with RNA-Seq, we found that proportion statistics could detect additional differentially expressed genes usually classified as missing by ratio statistics. Additionally, simulations demonstrated that one of our proposed proportion-based test statistics was robust to deviations from distributional assumptions where all other methods examined were not.</p> <p>Conclusions</p> <p>To measure relative expression between two samples, the proportion estimates that we propose yield equivalent results to the log₂-ratio under most circumstances and better results than the log₂-ratio when expression values are close to zero.</p>