Robustness of differential gene expression analysis of RNA-seq

A. Stupnikov, C. E. McInerney, K. I. Savage, S. A. McIntosh, F. Emmert-Streib, R. Kennedy, M. Salto-Tellez, K. M. Prise, D. G. McArt*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

RNA-sequencing (RNA-seq) is a relatively new technology that lacks standardisation. RNA-seq can be used for Differential Gene Expression (DGE) analysis, however, no consensus exists as to which methodology ensures robust and reproducible results. Indeed, it is broadly acknowledged that DGE methods provide disparate results. Despite obstacles, RNA-seq assays are in advanced development for clinical use but further optimisation will be needed. Herein, five DGE models (DESeq2, voom + limma, edgeR, EBSeq, NOISeq) for gene-level detection were investigated for robustness to sequencing alterations using a controlled analysis of fixed count matrices. Two breast cancer datasets were analysed with full and reduced sample sizes. DGE model robustness was compared between filtering regimes and for different expression levels (high, low) using unbiased metrics. Test sensitivity estimated as relative False Discovery Rate (FDR), concordance between model outputs and comparisons of a ’population’ of slopes of relative FDRs across different library sizes, generated using linear regressions, were examined. Patterns of relative DGE model robustness proved dataset-agnostic and reliable for drawing conclusions when sample sizes were sufficiently large. Overall, the non-parametric method NOISeq was the most robust followed by edgeR, voom, EBSeq and DESeq2. Our rigorous appraisal provides information for method selection for molecular diagnostics. Metrics may prove useful towards improving the standardisation of RNA-seq for precision medicine.

Original languageEnglish
Pages (from-to)3470-3481
Number of pages12
JournalComputational and Structural Biotechnology Journal
Volume19
DOIs
Publication statusPublished - 26 May 2021

Bibliographical note

Funding Information:
This work was supported by funding from the Brainwaves Northern Ireland (Registered Charity Number: NIC103464).

Publisher Copyright:
© 2021 The Authors

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

  • Diagnostics
  • Differential gene expression analysis
  • Differential gene expression models
  • Precision medicine
  • RNA-seq
  • Standardisation

ASJC Scopus subject areas

  • Biotechnology
  • Biophysics
  • Structural Biology
  • Biochemistry
  • Genetics
  • Computer Science Applications

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