ANCOM more/less abundant taxa

Hello QIIME2 community,

I am exploring how to use QIIME2 2018.4 release, and I struggle with interpreting ANCOM output.

To my understanding, the output first table shows the features that reject the null hypothesis, which means that they are differentially abundant. But it does not tell which group has higher/lower abundance. The second table shows the percentile of the features in different groups. Are these numbers the number of reads of those features? Then, does the difference in library sizes between the samples/groups (e.g. 3x - 10x difference) affect the interpretation of the percentile as more/less abundant because they are not normalized?

The volcano plot x-axis shows the clr mean_difference. I searched the forum and here, it is suggested that it is essentially log fold change and indicates increase if it is positive, and decrease if negative.

But in this case (attached picture), the second table shows a higher abundance of the feature in control but the volcano plot gives a negative clr mean difference. I tried switching the order of the sample in the metadata file and it still gives the same negative value.

My question is how to tell which group has higher/lower abundance besides from using the percentile table in the ANCOM output?

I am sorry if the question is too trivial.

Thank you very much


Hi @1112,
Sorry for the delayed response.


Yes. See this explanation of the percentile abundance results.

These frequencies are not normalized in the percentile abundances table; however, they are normalized by ANCOM prior to calculating significance so should not influence the results/interpretation.

higher in control, lower in treatment, so negative clr makes sense. As indicated in that forum post, which group is assigned as “control” can be arbitrary (maybe alphabetical order?) so I would not worry about this.

use the percentile abundance table as a guide. In this example, that feature is very clearly more abundant in the control group.

I hope that helps!


Thank you very much!


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