I have been using ALDEx2 extensively to find differential features (taxa) between case and control saliva microbiome samples. Using a q-value cutoff of 0.05 I have found some puzzling results where the distributions and median values of features are similar but are still called differential abundant. An example of this can be seen from this boxplot:
ALDEX results:
effect: 0.2778,
wi.eBH=0.0168
Similar plots are also found for other features that were called.
I'm trying to figure out why these features are being called differential abundant when it looks like they have very similar relative abundances (as shown by the boxplot). Is this due to my experimental design having a large class imbalance 1:5 (case:control) or perhaps I need to also implement an effect size cutoff as well (which is hard to estimate what a "good" cutoff would be).
After further thought I realised that it may be better to visualise the results as CLR values (rather than RA)... this seems to have fixed up the plots a bit and perhaps solved the issue.
Hello,
I have been using ALDEx2 extensively to find differential features (taxa) between case and control saliva microbiome samples. Using a q-value cutoff of 0.05 I have found some puzzling results where the distributions and median values of features are similar but are still called differential abundant. An example of this can be seen from this boxplot:
ALDEX results:
effect: 0.2778, wi.eBH=0.0168
Similar plots are also found for other features that were called. I'm trying to figure out why these features are being called differential abundant when it looks like they have very similar relative abundances (as shown by the boxplot). Is this due to my experimental design having a large class imbalance 1:5 (case:control) or perhaps I need to also implement an effect size cutoff as well (which is hard to estimate what a "good" cutoff would be).