Hello, look here! MaAsLin2 is a great tool, but I’ve encountered some issues:
I have several covariates and a continuous variable A, and I want to find the relationship between variable A and microbiome features using a model like Taxa ~ LM (variable A + covariate1 + covariate2 + covariate3…). After obtaining all the results and filtering them based on a maximum significance threshold of q ≤ 0.25, I found 136 features significantly associated with variable A (all_results.tsv). Following some advice, I subset the final MaAsLin2 results table to focus on the main effects of variable A and re-computed the q-values to detect significant microbiome features. I found that only 20 features met the q ≤ 0.25 threshold after this re-computation. Here’s my code:
maas.result = Maaslin2(
input_data = taxonomy,
input_metadata = sample_metadata,
output = ‘output’,
min_abundance = 0,
min_prevalence = 0,
max_significance = 0.25,
normalization = ‘TSS’,
transform = ‘LOG’, #AST
fixed_effects = c(“Breed”,“Strain”,“Age”,“Batch”,“A”),
reference=c(“Breed,Y”),
standardize = T,
plot_heatmap = F,
plot_scatter = F)
Question 1: Which results should I use?
Question 2: Is it necessary to re-compute the q-values?
Question 3: If I have other variables, should I analyze them together in the same model or construct separate models?
fixed_effects = c(“variable A”, “variable B”,“variable C”,“covariate")
or
fixed_effects = c(“variable A”, “covariate“)
fixed_effects = c(“variable B”, “covariate“)
fixed_effects = c(“variable C”, “covariate“)