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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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Probabilistic outlier identification for RNA sequencing generalized linear models.

Stefano Mangiola1, Evan A Thomas1, Martin Modrák2

  • 1The Walter and Eliza Hall Institute, Parkville, Victoria, 3052, Australia.

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|March 12, 2021
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Summary
This summary is machine-generated.

Outliers in RNA sequencing data can skew differential expression analysis. We introduce ppcseq, a Bayesian tool to detect outlier transcripts, improving RNA sequencing data quality control and analysis accuracy.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Modeling

Background:

  • Relative transcript abundance is crucial for gene function studies.
  • Negative binomial models are common for RNA sequencing differential analysis but lack robustness to outliers.
  • Existing outlier detection methods for RNA sequencing data are insufficient, relying on visual inspection.

Purpose of the Study:

  • To develop a rigorous, probabilistic method for detecting outlier data points in RNA sequencing datasets.
  • To introduce a quality control tool, ppcseq, for identifying transcripts that deviate from the negative binomial distribution.
  • To assess the impact of outliers on differential expression analysis results.

Main Methods:

  • Utilized Bayesian computation for large-scale comparison of observed RNA sequencing data against theoretical distributions.
  • Developed and applied the ppcseq tool for outlier transcript identification.
  • Analyzed multiple public RNA sequencing datasets using popular differential expression analysis tools.

Main Results:

  • Identified that 3-10% of differentially abundant transcripts across various algorithms and datasets were affected by outliers.
  • Demonstrated that outliers can inflate statistical significance in differential expression analysis.
  • Showcased ppcseq as an effective quality control tool for RNA sequencing data.

Conclusions:

  • Outliers are prevalent in public RNA sequencing datasets and can compromise differential expression analysis.
  • The proposed ppcseq tool provides a robust, probabilistic approach to outlier detection in RNA sequencing.
  • Implementing ppcseq enhances the reliability and accuracy of gene expression studies.