Robustness of differential gene expression analysis of RNA-seq.
Robustness of differential gene expression analysis of RNA-seq.
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RNA-seq差异基因表达分析的稳健性
DOI：
10.1016/j.csbj.2021.05.040
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发表时间：
2021
影响因子：
6
通讯作者：
McArt DG
中科院分区：
文献类型：
--
作者：
Stupnikov A;McInerney CE;Savage KI;McIntosh SA;Emmert-Streib F;Kennedy R;Salto-Tellez M;Prise KM;McArt DG
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.
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DOI：
10.1093/bioinformatics/btu638
发表时间：
2015-01-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Anders S;Pyl PT;Huber W
通讯作者：
Huber W
影响因子：
48
作者：
Langmead, Ben;Salzberg, Steven L.
通讯作者：
Salzberg, Steven L.
影响因子：
3
作者：
Li P;Piao Y;Shon HS;Ryu KH
通讯作者：
Ryu KH
影响因子：
14.9
作者：
Flicek P;Amode MR;Barrell D;Beal K;Billis K;Brent S;Carvalho-Silva D;Clapham P;Coates G;Fitzgerald S;Gil L;Girón CG;Gordon L;Hourlier T;Hunt S;Johnson N;Juettemann T;Kähäri AK;Keenan S;Kulesha E;Martin FJ;Maurel T;McLaren WM;Murphy DN;Nag R;Overduin B;Pignatelli M;Pritchard B;Pritchard E;Riat HS;Ruffier M;Sheppard D;Taylor K;Thormann A;Trevanion SJ;Vullo A;Wilder SP;Wilson M;Zadissa A;Aken BL;Birney E;Cunningham F;Harrow J;Herrero J;Hubbard TJ;Kinsella R;Muffato M;Parker A;Spudich G;Yates A;Zerbino DR;Searle SM
通讯作者：
Searle SM
影响因子：
3.7
作者：
Mohorianu I;Bretman A;Smith DT;Fowler EK;Dalmay T;Chapman T
通讯作者：
Chapman T