Hi, I’m new to MaAsLin2 and am looking for some advice on setting up the formula for assessing multiple variables and also ensuring I’m reading my results correctly.
I’ve working with normalized (relative abundance) microbiome data and a series of environmental variables related to water chemistry. I’m looking to see if any taxa are significantly correlated with any of net change (end-start of experiment concentration) of the chemistry variables. There are two treatments (Control and Sponge) and 4 independent samples of each treatment taken from the same time point (so not longitudinal).
I’ve started by assessing each variable in an independent MaAsLIn test because though they’re taken from the same water sample I don’t know that they can be considered dependent, but if you have other thoughts I would love to hear those. So the chemistry variable net change is a fixed effect (some are continuous and others only have 2 discrete values so MaAsLin considers them categorical despite the fact that they are numerical) and I’ve also made treatment a fixed effect with ‘Control’ as the reference value. Does this seem reasonable?
Anthranilate_fit_data = Maaslin2(input_data = input_data, input_metadata = df_input_metadata, min_prevalence = 0, normalization = "NONE", transform = "None", analysis_method = "LM", output = "anthranilate_netchange_Output", fixed_effects = c("anthranilatenetchange", "Treatment"), reference = c("Treatment, Control"), standardize = FALSE, correction = "BH", max_significance = 0.25, #0.25 is via developer default heatmap = TRUE)
I’m then obtaining output (abbreviated version) that looks like this:
feature metadata value coef stderr N N.not.0 pval qval ASV20 Treatment SpongeExometabolome 19.7456116 2.255907585 8 5 0.000322598 0.034840569 ASV42 anthranilatenetchange anthranilatenetchange 233502.8118 31318.6195 8 4 0.000684843 0.036981521
Am I correct in interpreting this as ASV20 is significantly positively correlated to treatment, which in this case means its more abundant in the sponge treatment. Meanwhile, ASV42 is significantly correlated with Anthranilate net change and in this case positively as well, based on the coef?
Lastly, another question regarding interpretation of these results. When I use this same code, but leave out treatment as a fixed effect, there are always many more significant ASVs, but my thinking was that I need to account for group effects (i.e., treatment). So in the above results ASV42 is correlated with Anthranilate net change AFTER accounting for the effect of treatment? Is that accurate?