Hi!

I am working on an assay to evaluate the impact of three factors on bacterial diversity: treatment (treatment1, treatment2, or control), dose (control or three different doses), and sample harvest time (t1 or t2). As I am not experienced with multivariate designs, I would appreciate your advice on analyzing diversity to highlight the potential effects of these factors. I am unsure if directly studying the effect of one factor on the entire dataset is appropriate, as the other two factors could mask some effects.

**Regarding beta-diversity analysis**: For instance, if I want to examine the dose effect only for samples harvested at t2, should I subset the data to include only t2 samples and then perform the `qiime diversity beta-group-significance`

analysis? And what if I want to further analyze only samples harvested at t2 and treated with treatment1? Should I subset the data again to include only treatment1 samples?

I am also struggling to understand how the adonis test can be appropriately used here. If I suspect that the harvest time factor introduces more noise than information, what is the best way to proceed? I saw in another post that it is possible to 'remove' the variance of factors I am not interested in, but I couldn't figure out how to write the p-formula exactly.

**For alpha-diversity analysis**: I have the same question: should I subset the data to a certain level before performing the `qiime diversity alpha-group-significance`

analysis, or is there a better approach?

Thank you in advance for your help!

Best,

Ben