MetagenomeSeq for amplicon differential abundance analysis

Question

[Bioinfonext]

Hi

I have imported phyloseq object into metagenomeSeq, I do have three treatment group:T1, T2 and T3. I want to see how these treatment group affect the microbial diversity in plant roots.

I want to do pairwise comparison, T1_VS_T2 , T2_VS_T3 and T1_VS_T3. I have tried below code to extract T1_vS_T2, IS IT CORRECT?

here I use the code but need some correction:

> root_data <- phyloseq_to_metagenomeSeq(root_M_filter)

**DO I need to used normalize count for further processing? what is setting? 

> normalizedMatrix <- MRcounts(root_data, norm = TRUE)


> settings = zigControl(maxit = 1, verbose = FALSE)



> mod = model.matrix(~root_data$Treatment)

> colnames(mod) = levels(root_data$Treatment)

> res = fitZig(obj = root_data, mod = mod, control = settings)
> res
An object of class "fitZigResults"
Slot "fit":
An object of class "MArrayLM"
$coefficients
                                         T1           T2          T3
71f84e7910006f22684121564206e8ca -2.8129112 -0.321366092  0.06113294
54e204fb99c80764e964456dadd6a0e5  0.5643061  0.019018800  1.01351768
55cd1fc570879d645bbf7a3642e9b0a8 -3.1159264  0.072348076  0.02205983
65f5c31e12c277aec319e2096463f9d2 -0.1355134 -0.004567206  0.53119153
7bed62f0fef250fd831dcf13bf43f4fc  1.9469670 -0.596315540 -0.35124338
                                 scalingFactor
71f84e7910006f22684121564206e8ca    1.62101725
54e204fb99c80764e964456dadd6a0e5   -0.24292482
55cd1fc570879d645bbf7a3642e9b0a8    1.50462644
65f5c31e12c277aec319e2096463f9d2    0.05833637
7bed62f0fef250fd831dcf13bf43f4fc   -0.56247683

> finalMod = slot(res, "fit")$design
> contrast.matrix = makeContrasts(T1 - T2, levels = finalMod)
> fit2 = contrasts.fit(zigFit, contrast.matrix)
> fit2 = eBayes(fit2)
> topTable(fit2)
                                     logFC   AveExpr         t      P.Value
3147790f0d5a78316fb9dd64f53b9473 11.262064 14.091887 16.331949 3.482815e-15
d2ec9f3b77975c0f457e4b7413b217ff  9.466087 12.212476 10.606122 6.159311e-11
7b908379d1fd2e75d84fd9aaecfd3f77  6.907182  9.784971  9.163780 1.267984e-09
d5fdc1df6dfd31acce9a34b9d8a76590  5.404459  8.373729  7.035926 1.802316e-07
19ddf8e59673c884734cf6093c672da0  5.381863  8.449413  7.011910 1.912363e-07

Kind Regards