@MicrobesAWG Does anyone have experience with ANCOMBC 2 and how it decides which features (taxon/ASVs) to keep in the differential abundance output when running pairwise comparisons?
Thanks!
@MicrobesAWG Does anyone have experience with ANCOMBC 2 and how it decides which features (taxon/ASVs) to keep in the differential abundance output when running pairwise comparisons?
Thanks!
@olabiyi you may also want to ask this at the next Microbes AWG mtg? Itâs usually first Wednesday of the month, 10-11a PT
@jaume.puig @daniela.bezdan - thoughts on agenda for Feb 5th mtg, maybe open to @olabiyi asking there?
Others, ideas, thoughts? @nicholas.brereton @katherine.j.baxter @vrvinothan @ben.sikes @joel.babdor @lauraelf @anna.simpson @philipjsweet @Zerrin @sudip.sharma.temple @mkagd001
Hi @olabiyi,
The DA output would be after the pairwise comparison, so one would retain everything for interpretation. You can then focus on specific dynamics based on that interpretation/biology.
For treatment prior to DA analysis, this would depend on the data and question (whatâs being compared and why) but below are some general steps to consider for filtering prior to pairwise comparisons which we use to improve meaningful biological insight for amplicon, metaT, WMS and untargeted metabolomics. Generally, we want to maintain data integrity and change as little as possible, but specifically prior to pw test:
Otherwise, Iâd make sure to keep the highest resolution of ASVs as possible (not collapsing anything based on annotation until interpretation) and not remove anything based on annotation on a first pass. ie. our ancient cyanobacteria or a-proteobacteria friends (mitochondria or chloroplast ASVs, depending on environment), given this can confound and can be done downstream with eyes open. Iâd also check how the correction is performing if you have very unbalanced library sizes.
For a real expert, you could ask @emmanuel.gonzalez
Hope that helps a bit!
Thanks @rtscott2001, Iâll ask the question at the meeting on the 5th of Feb. Hopefully I can find an answer before then.
@nicholas.brereton thanks for the wonderful recommendations. However, I was talking specifically about how ANCOMBC2 does itâs filtering before pairwise comparisons. I am asking because we are updating GeneLabâs amplicon Illumina workflow to run using nextflow and implement differential abundance testing which is currently missing. So ANCOMBC2 does pairwise comparisons between groups if you ask it to but even when you do set the library cutoff and prevalence cutoff parameters to zero (i.e. do not drop any sample or ASV), it still drops some ASVs in the final results generated. So how does it make this decision? My observation is that even if one group in a pairwise comparison (A vs B) has a count of zero (i.e. either A or B has a count of zero) then it drops that ASV entirely from its pairwise comparison.
I am looking to confirm my observation or gain new insights as to how ANCOMBC decides which ASVs to drop,
Thanks.
If I get your question - that example would be a structural zero. They mention the step explicitly in the bioconductor chat, I think.
Hi @nicholas.brereton, that must be It. My observation was right but I just didnât use the right terminology. It basically didnât analyze (i.e. dropped) the âstructural zerosâ as stated in the bioconductor documentation. I just confirmed this by checking the zero_ind dataframe of the output object generated by ANCOMBC2. If even one group has a structural zero for that ASV it wasnât further analyzed.
Thanks for your help.
Great! Just for clarity, these are the MOST significant parts of the data. So, while they should be removed from the test, they should always be reintegrated in the DA result. Ie. They should just automatically assigned as significant. If ancombc2 doesnât do this
Itâd be cool to see some genelab comparisons of real data against deseq2 at some point.