Date: 3/1/2020
Reference: Ha MJ, Kim J, Galloway-Pena J, Do KA, Peterson CB. (2020) Compositional zero-inflated network estimation for microbiome data. BMC Bioinformatics. 21:1-20
This vignette describes how to use R/COZINE to estimate microbial network when the abundances in each sample are constrained to have a fixed sum and there is incomplete overlaping microbial populations across subjects, the data are both compositional and zero-inflated.
We illustrate the usage of R/COZINE pakcage using 86 oral samples from newly diagnosed adult acute myeloid leukemia (AML) patients undergoing induction chemotherapy (IC) at MD Anderson Cancer Center in Houston TX from September 2013 to august 2015 measured at baseline before IC. Bacterial genomic DNA was extracted and the 16S rRNA V4 region was sequenced. The OTU counts within geneara can be loaded.
data("iOraldat")
dim(iOraldat)
## [1] 86 63
We fit COZINE, parallelizing over nodes using “mclapply”.
hf = cozine(dat = iOraldat)
The output is the object from fitHurdle function from the HurdleNormal package. A network with the lowest BIC value is selected and a post-hoc symmetrization is performed to obtain the adjacency matrix.
A = ORadj(hf$adjMat[[which.min(hf$BIC_etc$BIC)]]) # Adjacency Matrix
To check assortativity mixing over phylogenetic tree, we load the classification data.
data(class)
Then we compute assortativity coefficients at each of the hierarchies of the phylogenetic tree.
fit.assort = assortativity.phylo(A,class=class,graph.unit="Genus",num.perm=100000)
fit.assort$out
## [,1] [,2] [,3]
## [1,] "Phylum" "0.264279967819791" "5e-05"
## [2,] "Class" "0.221379082810953" "0"
## [3,] "Order" "0.148902067639045" "2e-05"
## [4,] "Family" "0.102591960266956" "0"
The coefficients and the p-values are in the second and third column of the out object/ The null.values object includes a list of the null assortativity coefficients obtained by permuting the ancestor labels of a phylogenetic classification.