When comparing methods we found that mixed model methods did not have better performance, but that, as previously reported, count distribution methods increased power. Overall we recommend the negative binomial GLM model, using the APL, as the method with the best overall performance.

When run on CPUs quasar is quite a bit faster (up to ~40x) than exisiting methods, while producing concordant output when the statistical model aligns.

2. We also show that negative binomial models can fail to appropriately control the Type 1 error, which we fix in quasar by implementing the Cox-Reid adjusted profile likelihood (APL), a core part of edgeR and DESeq2.