On the topic of deciding whether to rarefy or use some other method of normalization, one recommendation recently stated in Knight et al. 2017:
Prior to analysis, researchers should assess the difference in average library size between groups. If large variability in library sizes across samples is observed, then rarefying is useful as a method of normalization.
I know there’s a lot more to this than just that but this would be a useful feature to add to the interactive plots of feature-table.qzv
Just being able to visually see the average library sizes would be a good starting point!
Thanks for posting! This feature is already available in
alpha-rarefaction. That visualizer performs alpha rarefaction at multiple depths for each samples, and supports grouping samples by metadata column values. Please give that method a try and let us know if you have additional needs that are not covered by that action, or any questions!
Aha. This is perfect/exactly what I was referring to. Thanks @Nicholas_Bokulich ! Having used core-metrics before I totally didn’t realize this was available as a stand alone function. I wonder if it might be a useful addition to the core-metrics defaults since these figures are always submitted along with papers anyways, at least as supplementary material. Though on the other hand it would require a bunch of new inputs and parameters and take away from the simplicity of core-metrics…better just keep as is