Look for the altered feature associations responsible for the significant Non-parametric microbial interdependence test?

[11131 (Hongbin Liu)]

Hi,
We know that NMIT evaluates how interdependencies of features (e.g., microbial taxa, sequence variants, or OTUs) within a community might differ over time between sample groups. In the demo data, the delivery mode has a significant effect on the interdependencies of features. Is there any method to know which feature associations are altered by the delivery mode?
Thanks!
Best!
Hongbin Liu

[Nicholas_Bokulich]

Hi @11131,
You could check out what @Yilong_Zhang did in the original NMIT paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5696116/

But more generally other methods for differential abundance testing (e.g., ancom, aldex2) can be used to test for associations between sample metadata (like deliver mode or diet) and microbial composition, and you can relate those results to the NMIT test result.

Good luck!

[11131 (Hongbin Liu)]

Hi Bokulich,
Thank you for your prompt reply! Indeed, the anocom and aldex2 are optional tools for differential abundance analysis between different groups. But in my research, the data are longitudinal and dynamic after the diet intervention, so I think a method that specific for the longitudinal data analysis will be better because either one time point after the dietary intervention will not fully capture the microbial response. Do you have any advances for this longitudinal analysis to screen the microbes that response to the dietary intervention and responsible for the microbial ecological alteration? Any help would be much appreciated!
Thanks!

[Nicholas_Bokulich]

Hi @11131,

@11131

Do you have any advances for this longitudinal analysis to screen the microbes that response to the dietary intervention and responsible for the microbial ecological alteration?

Only the methods in q2-longitudinal, e.g., as we’ve already discussed here:

Easy way to perform the linear-mixed-effects analysis on the important features from the Feature volatility analysis?

Hi, I just finished the Feature volatility analysis, and I’m trying to perform the Linear mixed effect models analysis on the important features from the Feature volatility analysis. However, the important features form the filtered-table could be as many as more than 10, making it fussy and repetitive when I use the –p-metric parameter in the qiime longitudinal linear-mixed-effects because only one feature could be analyzed at a time. I know a loop could solve this problem. But it’s still stil…

Beyond that I recommend checking the literature if these methods do not match your goals, or you could open a “general discussion” topic on this forum to see if other forum users have some advice to address your specific research question.

Good luck!

[11131 (Hongbin Liu)]

Hi Bokulich,
My apologies for the late reply.
Thank you for your help and patience! I’ll try to analyze the data with q2-longitudinal first and also search the related papers to see whether there is a method could achieve my goal.
Thanks again!
Hongbin Liu