Using q2-boots as upstream input for q2-fmt

Library Plugin Support q2-fmt
1

Dear qiime2 forum,

I am planning to use the q2-fmt plugin on my data, but I would like to combine it with q2-boots so I can perform Rarefaction if possible.

My thought was that I could take the inputs from q2-boots and sub them into q2-fmt, and that I could create an "average feature table" from the multiple feature tables created through rarefaction in q2-boots by using q2-feature-table merge by using the average as the overlap method.

However, as I recognize the tool was not designed for this I am concerned that there are plugin features within it that would not be compatible, or even worse that using the q2-boots outputs instead could jeopardize the outputs of q2-fmt. As such, I would greatly appreciate your input on:

a) the feasibility of this (would this be possible, is this not recommended?) and
b) if there is a better way of combining the two than I have outlined below:

Specifically, I would like to use the following plugin features:

  1. Distance to Baseline / Donor - input seems to be a diversity metric

    • Would it be possible to simply input a diversity metric generated from q2 boots instead?

  2. Proportional Engraftment of Donor Features (PEDF) - input is rarefied feature table

    • Would it be possible to input the merged "average rarefaction feature table" instead?

  3. Permutation Test of PEDF - input is rarefied table but also requires information on sampling depth

    • Would it be possible to input the merged "average rarefaction feature table" instead?
    • Out of curiosity, why is sampling depth information used for in this function?

  4. Feature Engraftment input is taxonomy collapsed, relative frequency feature table

    • What is the advantage of using relative frequency feature table rather than a Rarefaction corrected feature table? For consistency, would it be possible to use the "average rarefaction feature table" instead?

Thank you so much in advance for your help!! :folded_hands:

kindest regards,
Zoë

p.s. upon thinking about this more, I wonder if the best way for PEDF would be to create it like how q2-boots does -- by calculating a value for each of the eg. 100 subsampled tables, and then making an average from that. Would there be any way that this would be possible? Thank you so much!!

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