References | Normalization methods | Software Packages/ pipelines | Replicates per condition (n) | Conclusions |
---|---|---|---|---|
Bullard et al. 2010 [17] | POLR2A, Q, TC, UQ | Genominator | 2, 4 | POLR2A and UQ with LRT/Exact test significantly reduced the bias of DE relative to qRT-PCR |
Kvam et al. 2012 [33] | DESeq, TMM, UQ | DESeq, edgeR, baySeq, TSPM | 2, 4, 5 | baySeq with UQ normalization performed best with highest sensitivity and low rates of false positives. But all the methods had an inflated true FDR (> 0.1). |
Rapaport F. et al. 2013 [34] | DESeq, TMM, UQ, RPKM, FPKM, Q, voom, | Cuffdiff, DESeq, edgeR, baySeq, PoissonSeq, voom-limma | 2, 3 | No single method emerged as favorable in all comparisons, but baySeq with UQ method was least correlated with qRT-PCR and Cuffdiff had an inflated number of false positive predictions. |
Li et al. 2015 [35] | DESeq, Med, Q, RPKM, RC, TMM, UQ, ERPKM | DESeq, edgeR, Cufflinks-CuffDiff, RSEM, Sailfis | 2, 4 | RC or RPKM seems to be adequate and the results from Sailfish and RSEM with RC or RPKM are inconsistent, resulting a conclusion of that normalization methods are not necessary for all sequence data. |
Dilliest et al. 2013 [23] | DESeq, Med, Q, RPKM, TC, TMM, UQ | DESeq, edgeR, Cufflinks-CuffDiff | 2, 3 | Exact test from DESeq combined with DESeq/TMM normalization performed best in terms of control of FDR below 0.05 for high-count genes; RPKM, TC and Q should be abandoned in DE gene analysis. |
Soneson et al. 2013 [36] | DESeq, TMM, UQ, RPKM, FPKM, voom, vst | DESeq, edgeR, EBSeq, baySeq, NBPSeq,NOIseq, SAMseq, ShrinkSeq,TSPM, limma | 2, 5, 10, 11 | DESeq had poor FDR control with 2 samples and good FDR control for larger sample sizes and low TPR.edgeR had poor FDR control with high TPR. Voom/vst-limma had good FDR control, but low power for small sample size. |
Seyednasroliah et al. 2013 [37] | DESeq, TMM, UQ, RPKM, FPKM, voom | DESeq, edgeR, baySeq, NOIseq, SAMseq, limma, CuffDiff2, EBSeq | 2:6, 8,10,12, 16, 20, 24, 28 | DESeq and limma were the safe choice and relatively conservative while edgeR and EBSeq were too liberal. DESeq and edgeR were the best tools |
Zhang et al. 2014 [38] | DESeq, TMM, FPKM, | DESeq, edgeR, Cufflinks-CuffDiff | 1:6, 8, 14, 20 | TMM performed best in terms of sensitivity and DESeq was the best for control false positives. Both were not sensitive to the read depth. |
Lin et al. 2016 [39] | DESeq, Med, Q, RPKM, TC, TMM, UQ | DESeq, edgeR and SAS | 2, 3, 5 | DESeq and TMM normalization methods were recommended compared to the other methods. |
Tang et al. 2015 [40] | RLE,TMM, UQ, RPKM, FPKM, Q, voom, | DESeq, DESeq2, edgeR, EBSeq, baySeq, SAMseq, PoissonSeq, voom-limma, TCC | 1, 3, 6, 9 | In multi-group comparison, the proposed pipeline internally using edgeR was recommended for count data with replicates while this pipeline with DESeq2 was recommended for data without replicates |
Germain et al. 2016 [41] | RLE, TMM, voom, TPM | Cufflinks-CuffDiff, DESeq2, edgeR, voom-limma | 3, 5 | With benchmarked differential expression analysis, in general voom and edgeR showed the most stable performance and be superior to other methods in most assay with replicates of 3 and 5. But voom significantly underperformed in transcript-level simulation and edgeR shown suboptimal results in the SEQC dataset |
Maza E 2016 [42] | TMM, RLE, MRN | DESeq2, edgeR | 1 | The three methods gave the same results for a simple two-condition comparison withourt replicates. |
Costa-Silva et al. 2017 [43] | TMM, RLE, UQ, voom | Limma-Voom, NOIseq, DESeq2, SAMSeq, EBSeq, sleuth, baySeq, edgeR | 1:8 | Limma-voom, NOIseq and DESeq2 had more consistent results for DEGs identification |
Spies et al. 2019 [44] | Vst, Med, RLE, TMM | DyNB, EBSeq-HMM, FunPat, ImpulseDE2, Imms, next maSigPro, nsgp, splineTC, timeSeq, edgeR, DESeq2 | 2, 3, 5 | DESeq2 and edgeR with a pairwise comparison outperformed TC tools for short time course (< 8 time points) due to high false positive rate except ImpulseDE2, but they were less efficient on longer time series than splineTC and maSigPro tools. |