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Welcome to the radEmu wiki! This is a living document where we will be collecting great questions from users.

Frequently Asked Questions

Interpretation

Q: If I ran radEmu on a dataset of shotgun sequencing "counts" with 1 covariate (control vs treatment), and for a specific bin/otu I get an estimate value of say 6, how can I interpret that?

A: If

• counts = "depth of coverage", and
• you didn't alter the argument constraint_fn to emuFit(), and
• the predictor that you put in your model is called group (formula = ~ group) and has values Treatment and Control,
• you have no other predictors in your model,

then if the estimated coefficient on groupTreatment is 6, you would interpret that as follows:

"We estimate that the average cell concentration of [Your Bin] is e^6 \approx 403 times higher in samples in the Treatment group compared to those in the Control group, compared to the average fold-change across all bins."

Abundance filtering

Q: Would be appropriate to perform any sort of abundance filtering prior to running radEmu?

A: Practical answer -- we recommend against abundance filtering... but we definitely recommend filtering out chimeras and contaminants according to best practices.

One of the reasons why we recommend against abundance filtering is that while restricting to a prespecified subcomposition (e.g., "I only want to look at fold changes in Bifidobacteria") is absolutely fine, restricting to a data-driven subcomposition (via abundance filtering) is a fairly complex conditioning process that radEmu's p-values don't account for. Hence our recommendation against this practice 😊

Fun fact -- at the time of writing, we don't know of any differential abundance methods that account for the impact of abundance filtering on the distribution of test statistics either... but we know of many methods that perform abundance filtering as part of DA, even though they don't account for its impact! 🐈‍⬛ Just one more reason to consider radEmu...