Is it valid to calculate Good's Coverage on DADA2 denoised feature table?

I have calculated Good’s coverage on feature tables derived from 16S data (rarefied to 33,500 seqs/sample) and ITS data (rarefied to only 970 seqs/sample) and found Good’s coverage to be 1.0 for all samples using qiime diversity alpha --p-metric goods_coverage. This struck me as odd given the low rarefied depth for the ITS samples (though these fungal communities are probably less diverse than the bacterial communities). On further investigation, I found (here) some references to certain measures of alpha diversity (namely richness measures) being invalid in the absence of singletons, and also that DADA2 does not “call singletons”. The calculation of Good’s coverage explicitly relies on number of singletons, so it seems fairly obvious that it would not be valid to calculate this on feature tables derived using DADA2. Is that correct? If so, how can I estimate whether I’ve achieved sufficient coverage?

On a related note: Is it equally invalid to calculate these alpha diversity metrics using mc2 OTU tables from QIIME 1, which have singletons filtered out?

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You are correct — Good’s coverage is not valid following dada2 denoising, since singletons are filtered out by default.

Check out this tutorial.

Correct. In QIIME 1 we generally recommend abundance filtering OTUs, as low-abundance (e.g., singleton) OTUs are very likely errors. So using a non-filtered OTU table is going to vastly overestimate alpha diversity and render a metric like Good’s coverage useless.

I hope that helps!

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Thanks @Nicholas_Bokulich ! That clears up my doubts.

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