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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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Replacing normalizations with interval assumptions enhances differential expression and differential abundance

Kyle C McGovern1, Justin D Silverman2,3,4,5

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New interval assumptions improve differential expression and abundance analyses by accounting for scale uncertainty, reducing false positives and enhancing reproducibility. These methods offer a more robust alternative to traditional normalizations.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Biology

Background:

  • Differential expression and abundance analyses commonly use normalization methods.
  • Normalization relies on strict assumptions about biological system scale (e.g., microbial load).
  • Violations of these assumptions introduce bias, increasing false positive and negative rates.

Purpose of the Study:

  • Introduce interval assumptions as a generalization of normalizations.
  • Enable accounting for potential errors in biological scale assumptions.
  • Provide a customizable and biologically plausible alternative to normalization.

Main Methods:

  • Develop a hypothesis testing framework integrating interval assumptions.
  • Modify existing tools like ALDEx2 to use interval assumptions.
  • Generalize Quantitative Microbiome Profiling (QMP) with interval assumptions.

Main Results:

  • Interval assumptions significantly decrease false positive rates (e.g., from 45% to 5%).
  • Statistical power is retained or increased compared to normalization.
  • Interval assumptions demonstrate robustness and improved performance even under misspecification.

Conclusions:

  • Interval assumptions enhance the rigor and reproducibility of omics data analysis.
  • They offer a more robust, interpretable, and user-friendly alternative to normalization.
  • Support the shift from normalization to methods addressing scale uncertainty.