PermanovaG {GUniFrac} | R Documentation |
In practice, we do not know a priori which type of change happens in the microbiome. Each distance measure is most powerful in detecting only a certain scenario. When multiple distance matrices are available, separate tests using each distance matrix will lead to loss of power due to multiple testing correction. Combing the distance matrices in a single test will improve power. PermanovaG combines multiple distance matrices by taking the minimum of the P values for individual distance matrices. Significance is assessed by permutation.
PermanovaG(formula, data = NULL, ...)
formula |
FORMULA Left side of the formula ( |
data |
DATA.FRAME containing the covariates |
... |
Parameter passing to |
Return a LIST containing:
p.tab |
DATA.FRAME (columns - p.values for individual distance matrices and the omnibus test (Note: sequential P values, put the variable of interest in the end), rows - covariates) |
aov.tab.list |
LIST of |
Jun Chen <chen.jun2@mayo.edu>
Jun Chen et al. (2012). Associating microbiome composition with environmental covariates using generalized UniFrac distances. 28(16): 2106–2113.
data(throat.otu.tab) data(throat.tree) data(throat.meta) groups <- throat.meta$SmokingStatus # Rarefaction otu.tab.rff <- Rarefy(throat.otu.tab)$otu.tab.rff # Calculate the UniFracs unifracs <- GUniFrac(otu.tab.rff, throat.tree, alpha=c(0, 0.5, 1))$unifracs # Combine unweighted and weighted UniFrac for testing PermanovaG(unifracs[, , c("d_1", "d_UW")] ~ groups) # Combine d(0), d(0.5), d(1) for testing