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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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Robust identification of differentially expressed genes from RNA-seq data.

Md Shahjaman1, Md Manir Hossain Mollah2, Md Rezanur Rahman3

  • 1Department of Statistics, Begum Rokeya University, Rangpur 5400, Bangladesh.

Genomics
|November 23, 2019
PubMed
Summary

A new robust method improves the identification of differentially expressed genes (DEGs) from RNA-sequencing data, especially in the presence of outliers. This approach enhances accuracy for both small and large sample sizes, outperforming existing methods.

Keywords:
DEGsLog-cpmRNA-sequence dataβ-Weight function and robustness

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Identifying differentially expressed genes (DEGs) is crucial for understanding molecular mechanisms of phenotypic variation.
  • Next-generation sequencing (RNA-seq) is a preferred method for DEG analysis due to cost-effectiveness and limitations of microarrays.
  • Existing DEG methods often struggle with accuracy and false discoveries, particularly with skewed data and outliers.

Purpose of the Study:

  • To develop a robust method for identifying DEGs from RNA-seq data that overcomes limitations of existing approaches, especially in the presence of outliers.
  • To improve the accuracy and reliability of DEG analysis in both small and large sample datasets.

Main Methods:

  • Robustification of the voom transformation within the limma pipeline using the minimum β-divergence method.
  • Comparison of the proposed method against seven popular DEG selection methods (DEseq, DEseq2, SAMseq, Bayseq, limma (voom), edgeR, edgeR_robust).
  • Performance evaluation using both simulated and real RNA-seq count datasets.

Main Results:

  • The proposed robustified voom method demonstrates improved performance in identifying DEGs compared to existing methods, particularly when outliers are present.
  • The method maintains comparable performance to existing approaches in the absence of outliers.
  • Improved accuracy and reduced false discoveries were observed across both small and large sample cases.

Conclusions:

  • The proposed robustified voom method offers a superior alternative for DEG selection from RNA-seq data, especially in datasets with outliers.
  • This method enhances the reliability of DEG identification in genomic studies.
  • Adoption of this method is recommended for more accurate and robust DEG analysis.