Hello, I’m trying to get the formula correct for my study design and need some help! I have a dataset with two paired samples for each subject (baseline and after treatment). Some of my subjects were treated with a drug while the others received placebo. I would like to test the effect of drug treatment on the microbiome, controlling for subject. Essentially I want to see if the “deltas” for each individual microbiome within subject between baseline and after treatment are different for subjects that received placebo or the drug.

I have incorporated subject as a random effect and have time point and treatment group as fixed effects. My significance results report significant taxa for both time point and treatment group. It’s a bit difficult for me to understand what MaAsLin is doing under the hood, but the significant taxa reported for treatment group should be “taxa that significantly differ by treatment group, comparing baseline to after treatment, controlling for subject”, correct?

Hi @afvrbanac,

If I am understanding everything correctly - yes your interpretation is correct.

Best,

Kelsey

Hi @Kelsey_Thompson,

I’m a bit confused as when I try to test on the deltas (I subtract baseline values for each subject and then only use “Treatment” as a fixed effect in the model) I get different results. Though perhaps that is due to other reasons.

If you look at the image attached, I’m confused as to how I get coefficients with the same directionality for opposite effects. For example, in the first plot, C. jejuni is higher at baseline in the treatment group and decreases with treatment, while V. atypica has the opposite slope and also has a positive coefficient. Since these “deltas” go in opposite directions, I don’t see how they could have the same directionality of significance with relation to placebo. Do you have any insight as to what could be going on here?

Hi @afvrbanac - based on these plots, you have a **significant** *treatment* by *time* interaction effect (i.e., **change from baseline** is different between treatment and placebo groups in your specific example). In order to find these significant associations using MaAsLin2, you need to therefore supply the interaction term as another fixed effect and extract the associated coefficients to match these plots. I hope this helps!

Best,

Himel

@himel.mallick thanks for the help! It is very much appreciated. Sorry I’m new to this so just want to make sure I get it right. My reference levels are baseline and placebo. I see the example in the tutorial for interactions, but dysbiosis is continuous so I’m not sure how to mirror that with my categorical values.

Would I make a single extra column that is just TRUE or FALSE values with all baseline values set to FALSE (since that is the reference), placebo end-of-treatment set to FALSE (since placebo is the reference), and only treated end-of-treatment set to TRUE? Or is there another way I would set this up?

Hi @afvrbanac - happy to help. Although MaAsLin2 is not optimized for interactions, in this case, you can simply create three binary variables as follows:

- Fixed main effect 1: 1 if EOT and 0 otherwise
- Fixed main effect 2: 1 if Treatment and 0 otherwise
- Fixed interaction effect: 1 if EOT_Treatment and 0 otherwise.

Note that, the interaction term is a binary variable by definition since it’s a product of two binary variables and therefore can only take two values: 0 and 1.

With this setup, you don’t need to specify reference as by default 0 will be the reference group in each case and the interaction term is exactly what you want to estimate (i.e., difference in change from baseline to EOT between treatment and placebo). You will still need the subject as a random effect.

Note that, you need to subset your results table to the variable of interest (interaction term) and re-calculate the q-values.

Good luck with the modeling,

Himel

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