I have a followup question. I was looking at some work I have done using NMDS for a statistics class, and we standardized the data using Wisconsin double standardization. So my question is, do you happen to know if this is necessary, given that my data is already rarefied?
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To answer your question, I personally don’t have any experience with the Wisconsin double standardization method, but from a quick glimpse of it I’m not sure if its very suitable for dataset with lots of zero since the standardization is dividing each element by its abundance max then by total sites (samples). I don’t know if this would give you zeroes or NaN, which may affect your result. Regardless, it does seem to deal with stabilizing variance which, depending on the data can happen both at the level of sampling effort (rarefaction) or even after rarefaction based on the nature of the data itself. So it may benefit in both scenarios. You may want to consult a statistician on this, sorry I couldn’t be more help. Or perhaps someone with more experience with this can qiime in!
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