Adjust for confounding variables with ANCOM-BC

Hello,

I’m trying to use ANCOM-BC to deal with some microbiome data with known confounding variables and I am trying to figure out if detection of differential abundance can account for these/how to tell the algorithm about them.

I have 20 samples, half from treated animals and half from untreated.
Additionally, the animals come from one of 4 different locations.

Sample    Treat   Loc
1           T      A
2           F      A
3           T      B
4           F      B
5           T      C
(and so on)

To run the program, you use a command like this:

output <- ancombc(phyloseq = phylum_data, formula = "Loc + Treat", p_adj_method = "holm")
res <- output$res

And then the various metrics (logFoldChange, p value, adjusted p value, etc) are all stored as columns in res
www.bioconductor.org/packages/release/bioc/vignettes/ANCOMBC/inst/doc/ANCOMBC.html#running-ancombc-function

But it’s unclear to me if these values are just telling me the about the results in response to the variable in question OR if they’re accounting for confounding variables. I’m used to DESeq2, so perhaps I’m just thinking about this wrong. Any help is appreciated.