I have samples of two types (A/B), before and after a treatment (pre/post).
(There is no reason to hypothesize A would have changed more than B, or in a different direction; both were subject to the same treatment and likely had different starting communities)
I ran pairwise-differences on my PCoA axes to see if there were any predictable directional differences in pre/post. Axis 3 was significant.
I generated a biplot and there are clues as to which taxa may be driving this, but as I found in the forum, the most important taxa are determined by their vector magnitude on PC1.
I ran ANCOM-BC with pre/post and A/B as interactive and cumulative independent variables and have a list of significant taxa. I can run pairwise differences on the abundances or relative abundances of these taxa and generate boxplots. (It would probably be better to run ANCOM-BC2 in R where it is possible to run a paired analysis and may try this but it is intimidating for a beginner)
I was hoping there may be a more straightforward way to find which taxa are driving the difference between pre/post, and possibly contributing to interactions between pre/post and A/B.
I was thinking it may be a good idea to run Spearman on Axis 3 vs taxonomic (relative) abundances. Is there a good way to do that in QIIME?
Is there a way to generate a biplot (and list of important taxa) using Axis 3 as the axis of importance?
Do you have any other suggestions for how to find the significantly important taxa and visualize their impact? The sample classifier heatmaps are interesting and may be of use, but I would imagine RandomForest methods aren't as robust as ANCOM-BC and pairwise differences.
I also tried longitudinal feature-volatility, and only one taxon was in the important features result, with importance = 1. When I set feature-count to 10, a different resulting single taxon with importance = 1. Not sure if I am doing something wrong here:
qiime longitudinal feature-volatility
Any suggestions are greatly appreciated! Thank you, Nate