Hi all, I tested the differentialabundance pipeline with standard settings on one of our datasets. The results look very good! However, while checking the results I found a rare number of genes to be significantly different, but seem only found expressed in ONE individual sample (of 5x2 samples covering two conditions). I think there may be outliers among the samples influencing this outcome, but generally: is this expected behaviour? Do you have any recommendations on how to best address this, e.g., by setting filtering settings ("--filtering_min_abundance")? I´m grateful for any advise. Best wishes, Christian.

Yeah, this is just a consequence how how the modelling works- one crazy sample can increase the mean on side of the comparison. The filtering options are your friend, e.g. say you have a 5 vs 5 comparison, it might be sensible to ask that at least 4 / 10 samples have expression above a threshold.

Ok, thanks for clarification. This is really helpful to me :). You mean something like setting "--filtering_min_abundance 10 --filtering_min_samples 4"? I also saw there is a setting "--filtering_min_proportion_not_na", but it does not seem to have any effect with the default value of 0.5.

Yes, something along those lines