I have a question regarding the correct utilization of the relative frequency table in a PCoA biplot based on Jaccard distance matrix.
Usually, when I calculate PCoA biplot, I proced with the calculation of the distance matrix from my feature table, then its PCoA, convert the same feature table utilized earlier to a relative frequency table,
and then produce the biplot.
In the last dataset I analyzed, I noticed that a feature present in every sample was among the most important ones, but since I'm using Jaccard I found this result strange.
I read in this post Questions about PCoA biplots that projection of the feature on the PCoA space is due to the calculation of a covariance matrix between the PCoA matrix and the feature table, so the projection depends also by the feature abundance, and not only by its presence/absence, if I understood correctly.
So to neutralize the problem I utilized a new relative frequency table where every feature, if present, has the same frequency. In this way, the contribution of the omnipresent feature was very close to 0 (10^-18 on PCo1 and 2).
My question is: is it correct to utilize a relative frequency table where every present feature has the same weight when calculating PCoA biplot of a qualitative metric like Jaccard?
Thank you for your attention, I hope I've been clear with my explanation!