Hi @pvgeende,
@aljabadi may want to ask you for more details (and data), in case this is related to a bug in the function. My intuition is that your OTU data are highly collinear and so you have ~ 51 variables considered as important (and potentially exactly the same values) on the first component. Have a look back at the data, the variables selected and let us know.
1 - It means that the discrimination only happens on the first component (as you will visualise on the plot), and after that you are only adding noise. The performance results indicate that yes, it is difficult to separate the groups. It might be better not to tune, and instead choose a reasonable number of variables ad-hoc that will allow you for exploration and interpretation using pathway analysis etc. For visualisation, you can still use 2 components but focus your interpretation on the variables selected on component 1.
2 - No, but you can extract the similarity matrix from circosPlot (see post here) and use cytoscape for customised plots.
Kim-Anh