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Proportion statistics to detect differentially expressed genes: a comparison with log-ratio statistics.
BMC Bioinformatics. 2011 Jun 7;12(1):228
Authors: Bergemann TL, Wilson J
ABSTRACT: BACKGROUND: 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 log-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. RESULTS: 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. CONCLUSIONS: To measure relative expression between two samples, the proportion estimates that we propose yield equivalent results to the log-ratio under most circumstances and better results than the log-ratio when expression values are close to zero.
PMID: 21649912 [PubMed - as supplied by publisher]
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Proportion statistics to detect differentially expressed genes: a comparison with log-ratio statistics.
BMC Bioinformatics. 2011 Jun 7;12(1):228
Authors: Bergemann TL, Wilson J
ABSTRACT: BACKGROUND: 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 log-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. RESULTS: 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. CONCLUSIONS: To measure relative expression between two samples, the proportion estimates that we propose yield equivalent results to the log-ratio under most circumstances and better results than the log-ratio when expression values are close to zero.
PMID: 21649912 [PubMed - as supplied by publisher]
More...