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Table 2 Summary of the main observations

From: A comparison of methods for differential expression analysis of RNA-seq data

DESeq - Conservative with default settings. Becomes more conservative when outliers are introduced.
- Generally low TPR.
- Poor FDR control with 2 samples/condition, good FDR control for larger sample sizes, also with outliers.
- Medium computational time requirement, increases slightly with sample size.
edgeR - Slightly liberal for small sample sizes with default settings. Becomes more liberal when outliers are introduced.
- Generally high TPR.
- Poor FDR control in many cases, worse with outliers.
- Medium computational time requirement, largely independent of sample size.
NBPSeq - Liberal for all sample sizes. Becomes more liberal when outliers are introduced.
- Medium TPR.
- Poor FDR control, worse with outliers. Often truly non-DE genes are among those with smallest p-values.
- Medium computational time requirement, increases slightly with sample size.
TSPM - Overall highly sample-size dependent performance.
- Liberal for small sample sizes, largely unaffected by outliers.
- Very poor FDR control for small sample sizes, improves rapidly with increasing sample size. Largely unaffected by outliers.
- When all genes are overdispersed, many truly non-DE genes are among the ones with smallest p-values. Remedied when the counts for some genes are Poisson distributed.
- Medium computational time requirement, largely independent of sample size.
voom / vst - Good type I error control, becomes more conservative when outliers are introduced.
- Low power for small sample sizes. Medium TPR for larger sample sizes.
- Good FDR control except for simulation study B 0 4000 . Largely unaffected by introduction of outliers.
- Computationally fast.
baySeq - Highly variable results when all DE genes are regulated in the same direction. Less variability when the DE genes are regulated in different directions.
- Low TPR. Largely unaffected by outliers.
- Poor FDR control with 2 samples/condition, good for larger sample sizes in the absence of outliers. Poor FDR control in the presence of outliers.
- Computationally slow, but allows parallelization.
EBSeq - TPR relatively independent of sample size and presence of outliers.
- Poor FDR control in most situations, relatively unaffected by outliers.
- Medium computational time requirement, increases slightly with sample size.
NOISeq - Not clear how to set the threshold for q NOISeq to correspond to a given FDR threshold.
- Performs well, in terms of false discovery curves, when the dispersion is different between the conditions (see supplementary material).
- Computational time requirement highly dependent on sample size.
SAMseq - Low power for small sample sizes. High TPR for large enough sample sizes.
- Performs well also for simulation study B 0 4000 .
- Largely unaffected by introduction of outliers.
- Computational time requirement highly dependent on sample size.
ShrinkSeq - Often poor FDR control, but allows the user to use also a fold change threshold in the inference procedure.
- High TPR.
- Computationally slow, but allows parallelization.
  1. The table summarizes the present study by means of the main observations and characteristic features for each of the evaluted methods. We have grouped voom+limma and vst+limma together since they performed overall very similarly.