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Updated: Jun 1, 2026
10:10
Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
Published on: September 18, 2021
Proportion statistics to detect differentially expressed genes: a comparison with log-ratio statistics.
Tracy L Bergemann1, Jason Wilson
1Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA. tracy.l.bergemann@medtronic.com
BMC Bioinformatics
|June 9, 2011
Summary
This study introduces a novel proportion-based method for measuring gene expression. This approach offers greater numerical stability than the traditional log(2)-ratio, especially for low expression values.
Area of Science:
- Genomics
- Molecular Biology
- Bioinformatics
Background:
- Gene expression is typically quantified as a ratio relative to a reference sample.
- Current methods like log(2)-ratio have limitations, particularly with low expression values.
Purpose of the Study:
- To propose and validate a more numerically stable alternative to the log(2)-ratio for measuring relative gene expression.
Main Methods:
- Developed and evaluated proportion-based statistics as an alternative to ratio-based measures.
- Applied methods to cDNA microarray and RNA-Seq data.
- Conducted simulations to assess robustness.
Main Results:
- Proportion statistics showed similar results to log(2)-ratio in microarray experiments.
- RNA-Seq analysis revealed proportion statistics identified additional differentially expressed genes.
- Simulations indicated proposed methods were robust to distributional assumption deviations.
Conclusions:
- Proportion estimates provide equivalent results to log(2)-ratio in most cases.
- Proposed methods offer superior performance when gene expression values are near zero.
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