Hi!

I am currently working  to analyze alpha & beta diversity with microbiome data of 48 sample.
(Animal stool samples)

I finished working of Beta diversity and got the data of significance. (Weighted UniFrac, Bray-curtis)

However, I received this message and need some help from you.

The message is as below (From reviewer)
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I highly recommend the use of **linear modeling (LM) or generalized linear modeling (GLM) which is** commonly used in microbiome studies **rather than a Wilcoxon rank-sum test**. This will allow you to better control for things that may impact your results such as the age animals. It is also important to control for site as this is known to significantly affect the gut microbiome and could be included as a random variable in a generalized linear mixed model (GLMM). 
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I found that non-binomial regression or possion regression is the most commonly used glm for 
microbial analysis and trying to use the models in comapring Alpha and Beta diversity difference of the 16s rRNA data. 

However, my question is that what package in R or python is recommended when figuring out microbial 
analysis. (Perhaps, many researchers in the metabarcoding field use some common statistical tools)

And, also I want to know that how can I modify (adding a dependent variable or etc. ) the data file 
to use in the GLM model. (What variable should be added to compare in Alpha and Beta diversity analysis, and how can I calculate them?)

my metadata file is as below 

![Meta data file to be used in GLM][1]  


  [1]: /media/images/478208ba-66c0-4776-a09d-8228c249

Any recommendation about using Package source, specific GLM model or the way to modify data - 
would be a big help for me to solve the problem. 

Thank you    
In R, linear models are available with the function lm() and generalized linear models with glm(), both from package stats. For mixed effects, you can use the [lme4 package][1] (functions lmer() and glmer()). There's also the function glmnet() from the [glmnet package][2] if you want LASSO or elastic net regularization.


  [1]: https://cran.r-project.org/package=lme4
  [2]: https://cran.r-project.org/package=glmnet