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dc.contributor.authorStupnikov, A
dc.contributor.authorMcInerney, CE
dc.contributor.authorSavage, KI
dc.contributor.authorMcIntosh, SA
dc.contributor.authorEmmert-Streib, F
dc.contributor.authorKennedy, R
dc.contributor.authorSalto-Tellez, M
dc.contributor.authorPrise, KM
dc.contributor.authorMcArt, DG
dc.date.accessioned2021-07-26T08:20:23Z
dc.date.available2021-07-26T08:20:23Z
dc.identifier.citationComputational and structural biotechnology journal, 2021, 19 pp. 3470 - 3481
dc.identifier.issn2001-0370
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/4690
dc.identifier.eissn2001-0370
dc.identifier.eissn2001-0370en_US
dc.identifier.doi10.1016/j.csbj.2021.05.040
dc.identifier.doi10.1016/j.csbj.2021.05.040en_US
dc.description.abstractRNA-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.
dc.formatElectronic-eCollection
dc.format.extent3470 - 3481
dc.languageeng
dc.language.isoeng
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleRobustness of differential gene expression analysis of RNA-seq.
dc.typeJournal Article
dcterms.dateAccepted2021-05-25
rioxxterms.versionVoR
rioxxterms.versionofrecord10.1016/j.csbj.2021.05.040
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0
dc.relation.isPartOfComputational and structural biotechnology journal
pubs.notesNot known
pubs.organisational-group/ICR
pubs.organisational-group/ICR/ImmNet
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology/Integrated Pathology
pubs.organisational-group/ICR
pubs.organisational-group/ICR/ImmNet
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology/Integrated Pathology
pubs.publication-statusPublished
pubs.volume19en_US
pubs.embargo.termsNot known
icr.researchteamIntegrated Pathology
icr.researchteamIntegrated Pathologyen_US
dc.contributor.icrauthorSalto-Tellez, Manuelen


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