Hi @llenzi. Thanks for your response. As I mentioned, I am new to this and so, if rarefying is a big no-no, I would really appreciate learning why that's the case.
My reasoning for normalizing the data is related to the interpretations that I am hoping to make from my data. I have 3 experimental treatment and one control treatment in quadriplicates in my experiment. My hope is to compare them to see how the experiment has impacted the populations within the microbiome (if there has been a significant increase or decrease in abundance). Secondarily, I also wished to do pairwise testing with the control to see how much the abundance of populations has changed compared to control.
So, I figured using the rarified table would enable me to do this analysis since it would essentially put all the samples I have on a "level playing field". I understand I cannot compare the W values across different ANCOMs but I maybe able to do some sort of comparative inference since I started off with rarefied data. I acknowledge that there would be a loss in data and also loss of rare ASVs but that's not of interest to me, at least just yet.
So far, the results I got from the rarefied table ANCOM are similar to what I observe experimentally and with the relative abundance heatmap and so, I didn't think it was odd. Furthermore, this paper says normalization should work for ANCOM and would not be a big problem but I would love to hear your thoughts on the same.
With regard to my original question, I have switched to using volcano plots, which seem to be working better. As you mentioned, I filtered out the data (to minimum 10 reads) and still experienced this issue. I think the excessive zeros came from some glitch when converting the abundance data to relative abundance for the PCoA plots.... I am not sure why the tutorial I used suggested this method and I found a workaround with the original QIIME2 tutorial. Sorry guys, as I mentioned, I am incredibly new to this but enjoying it so much!