Hi everyone,

I would like some help with the design of my analysis.

I have samples from 2 groups in 2 timepoints. Let’s say Treated1, Treated2, Control1, Control2.

My metadata table includes: Subject ID, Timepoint (1 or 2), Treatment (Treated or Control) and some other variables such as Age, Sex etc.

I want to check for differentially abundant taxa between timepoints (treated1 vs treated 2, control1 vs control2) as well as between treatment groups per timepoint (treated1 vs control1, treated2 vs control2).

I have run some experiments but I have gotten somewhat confused. For example:

Exp. 1: Isolated the treated samples only and want to compare timepoint 1 with timepoint 2.

fit_data1 = Maaslin2(

[…],

fixed_effects = c(“Timepoint”),

random_effects = c(“Subject”)

)

Exp. 2: Same procedure, but I wanted to account for confounders.

fit_data2 = Maaslin2(

[…],

fixed_effects = c(“Timepoint”, “Sex”, “Age”),

reference = c(“Sex,F”),

random_effects = c(“Subject”)

)

Exp. 3: Instead of isolating the treated samples, I tried to use all samples and create a new column for the interaction of Treatment and Timepoint. I’m doing this in order to perform all comparisons at the same time.

fit3 = Maaslin2(

[…],

fixed_effects = c(“Timepoint”, “Treatment”, “treatment_time”),

reference = c(“treament_time,Treated1”),

random_effects = c(“Subject”)

)

I have understood that the first 2 approaches are correct, but I am not sure about the 3rd one. If I proceed with the 1st one, I will have to repeat the same procedure, each time isolating the 2 groups that I want to compare. Is this correct, and is this equivalent with the 3rd one? In the 3rd one, do I have to include Timepoint and Treatment as fixed effects, or just the new variable?

Lastly, I am getting several warnings, especially in the 3rd experiment, including:

- WARNING::Fitting problem for feature 35 returning NA
- In .checkRankX(TMBStruc, control$rank_check) :
- fixed effects in conditional model are rank deficient
- In fitTMB(TMBStruc) :
- Model convergence problem; non-positive-definite Hessian matrix. See vignette(‘troubleshooting’)
- In fitTMB(TMBStruc) :
- Model convergence problem; false convergence (8). See vignette(‘troubleshooting’)
- In fitTMB(TMBStruc) :
- Model convergence problem; extreme or very small eigenvalues detected. See vignette(‘troubleshooting’)

Is this worrying?

Thank you,

Christina