How to incorporate metadata in 16S amplicon study and identify confounding variables

Question

[Lina F]

Hi all,

We have a bunch of 16S metagenomics samples for case and control patients. Unfortunately, based on 16S microbial abundance counts, there are no significant differences between the case and control patients. Before we go ahead and scrap the experiment, my supervisor suggested looking into metadata of the study (smokers/non-smokers, BMI, age, sex, etc). He is hoping that we might be able to identify confounders that way; maybe excluding the smokers from the analysis will give us stronger signals, etc.

I am new to this kind of analysis and would appreciate suggestions as to what kind of tools I could look at (ideally with beginner's tutorials :-P)

Thanks in advance!

[Josh Herr]

Just to clarify (for those sorting based on tags), I changed the tags and title to remove metagenomics -- 16S amplicon sequencing is not metagenomics -- you're using a single marker to look at diversity and not looking at genomic DNA from a group of organisms.

Answer 1

[Josh Herr]

Both QIIME and mothur will allow you to add metadata to an existing 16S data analysis:

In QIIME, see Working with BIOM tables in QIIME (particularly the section 'splitting by sample data')

In mothur, see the command make.biom

If you have additional questions, please see the web tutorials of the microbial ecology course I co-teach: edamame-course.org. Some of these tutorials will be helpful for you. Best of luck to you Lina F!

[Lina F]

Thanks for the ideas!

I have actually used Biom tables before, but have only used them to color dots in emperor plots based on metadata criteria (i.e. all smokers are blue and all non-smokers are red).

I would like to go further and determine if certain samples are influencing our results more than others.