Colonization from inoculum

Hi All,
Could you provide some idea on how to check the percentage of ASVs from the inoculum that is colonized in the sample. For the work, we transfer human fecal microbiota (inoculum) into mice. I am looking at the mice microbiome and want to identify how many and what percentage of the inoculum is present in the mice sample.
I would really appreciate some insight on this.


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Hi @annalex, @cherman2 is building some tools to do exactly what you're asking here. She is traveling at the moment, but should be able to get back to you within the next few days.

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Hi @annalex,
This sounds like a perfect use-case for q2-fmt sample-peds method! sample-peds is an implementation of Proportional Engraftment of Donor Strains.

What you will need for this method ( and all methods in q2-fmt if you are interested in using them) is metadata for each sample with a numeric time-point, subject column, and a DonorID column. The donor id column should be the sample name of the donor that was given to the recipient.
Hope that makes sense! Let me know if you need any clarification.

Also since this is an FMT study, I would recommend looking at the other methods in q2-fmt as well, such as the engraftment and feature-peds! I would love to chat more about those if it is helpful for you!

Install instruction are located in this github: GitHub - qiime2/q2-fmt: Plugin for analysing the efficacy of fecal microbiota transplants
This is currently in alpha-testing so if you find any bugs or issues with the methods let me know!


Hi @cherman2
Thanks for building q2-fmt tools. It will be useful for our study. Like @annalex, we also want to compare which ASV from human stool colonized mice. We will be comparing human stool gavage (that was given to mice) and fecal samples from mouse (that received the gavage).

Could you please elaborate about numeric time-point and subject?
What does subject refer to? Human subject?



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Hi @kindergarten

This sounds like something feature-peds could help with! It looks at what percentage of your recipients engrafted an ASV. For example, if ASV 1 was seen in 3 out of 4 of your mice that received ASV 1 from their donors, the feature-peds would be 75%. This can help you see which asv are engrafting best. Just a warning though: Make sure to look at the tool tip in the heatmap, once you run feature-peds. Sometimes you can end up with a really high percentage but the fraction is 1/1 and thats not as impressive as a feature that's engrafting 75% of the time but is 75 recipients that engrafted/100 possible recipients.

So with a large portions of the studies I have seen, they are sampling the FMT recipients for a specific length of tim, before and after the fmt. They typically need a subject for the recipient and a timepoint for their samples. It sounds like that might not be the case for your study!

This is making me realize that q2-fmt sample/ feature peds should be more flexible with that, I will make an issue to address this, so that subject and time-point are not required for sample-peds and feature-peds.

In the meantime, it might just be quicker for you to create a "fake time-point" column (where all the recipients have a timepoint of 1) and a "fake subject" column (where each sample has its own unique subject)

Another thing! You might want to do some prevalence and abundance filtering before sample and feature peds. This will make sure that low abundance/prevalence features aren't skewing your data. Filtering data — QIIME 2 2023.5.1 documentation

Lastly, I would like to add that sample and feature peds are a very naive way of looking at "engraftment" because they just look at presence/absence of specific asv. While this is super exciting and definitely important to understanding FMT engraftment. I would also look into q2-fmt engraftment as this can capture shifts in alpha and beta diversity that also can be helpful for understanding the full impact of the FMT.

Does that make sense? Let me know if there are any other questions! Feedback from users on the q2-fmt tool is so valuable! Thank you for looking into q2-fmt.