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. |