Question: How to incorporate metadata in 16S amplicon study and identify confounding variables
gravatar for Lina F
4.7 years ago by
Lina F160
Boston, MA
Lina F160 wrote:

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!

amplicon 16s confounders • 1.4k views
ADD COMMENTlink modified 4.7 years ago by Josh Herr5.7k • written 4.7 years ago by Lina F160

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.

ADD REPLYlink written 4.7 years ago by Josh Herr5.7k
gravatar for Josh Herr
4.7 years ago by
Josh Herr5.7k
University of Nebraska
Josh Herr5.7k wrote:

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:  Some of these tutorials will be helpful for you.  Best of luck to you Lina F!

ADD COMMENTlink modified 4.7 years ago • written 4.7 years ago by Josh Herr5.7k

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.

ADD REPLYlink written 4.7 years ago by Lina F160
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