Apologies if addressing the issue of sample weights in the limma-voom pipeline is becoming onerous, but I have another question about the difference between voom and voomWithQualityWeights (or voomLmFit with sample.weights = F and sample.weights = T) and when to use one approach or the other.

I primarily work with large human bulk RNA-Seq datasets (typically in the 100s of samples) and I've found that sample weights often increase the number of DEGs identified, even in situations where there are no apparent outlier samples and the groups of interest separate nicely in PCA.

However, I've found that sometimes sample weights decrease the number of DEGs found (compared to normal voom). How might this be interpreted? Does this suggest that a high proportion of samples in the experiment have been down-weighted, leading to reduced power? In such a situation is it still right to use sample weights?