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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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Differential gene expression analysis tools exhibit substandard performance for long non-coding RNA-sequencing data.

Alemu Takele Assefa1, Katrijn De Paepe2, Celine Everaert3

  • 1Department of Data Analysis and Mathematical Modeling, Ghent University, Ghent, Belgium. AlemuTakele.Assefa@UGent.be.

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|July 26, 2018
PubMed
Summary
This summary is machine-generated.

Differential expression analysis for long non-coding RNAs (lncRNAs) and low-abundance mRNAs is challenging due to high variability. This study evaluated 25 pipelines, finding that most performed poorly on lncRNAs, highlighting the need for robust methods.

Keywords:
Differential gene expressionRNA-seqlncRNAmRNA

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Long non-coding RNAs (lncRNAs) exhibit low expression and high variability, posing challenges for differential expression (DE) analysis.
  • Accurate DE analysis is crucial for understanding gene function and disease mechanisms.

Purpose of the Study:

  • To comprehensively evaluate the performance of 25 pipelines for DE analysis in RNA-seq data.
  • To specifically assess the performance for lncRNAs and low-abundance messenger RNAs (mRNAs).

Main Methods:

  • Utilized non-parametric procedures for simulating gene expression data with realistic variability.
  • Evaluated 25 DE analysis pipelines using 15 performance metrics.
  • Analyzed six diverse RNA-seq datasets, tracking mRNA and lncRNA results separately.

Main Results:

  • All evaluated pipelines showed inferior performance for lncRNAs compared to mRNAs.
  • Substandard performance for lncRNAs extended to low-abundance mRNAs.
  • Pipeline performance was influenced by data variability, sample size, and the proportion of DE genes.

Conclusions:

  • Linear modeling (limma) and SAMSeq demonstrated good false discovery rate control and sensitivity.
  • Over 80 samples are needed for 50% sensitivity in realistic settings like cancer research.
  • Many methods showed excessive false discoveries, impacting reliability and reproducibility; a web application is available for tool selection guidance.