PermanovaG2 | 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.
PermanovaG2(formula, data = NULL, ...)
formula |
a formula, left side of the formula ( |
data |
a data frame containing the covariates |
... |
parameters passing to |
Return a list containing:
p.tab |
a data frame, columns: p-values for individual distance matrices and the omnibus test, rows: covariates. (Note: they are sequential p-values, put the variable of interest in the end) |
aov.tab.list |
a list of |
Jun Chen <chen.jun2@mayo.edu>
Chen, J., Bittinger, K., Charlson, E.S., Hoffmann, C., Lewis, J., Wu, G.D., Collman, R.G., Bushman, F.D. and Li, H. (2012). Associating microbiome composition with environmental covariates using generalized UniFrac distances. 28(16): 2106–2113.
Rarefy
, GUniFrac
, adonis3
## Not run:
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
PermanovaG2(unifracs[, , c("d_1", "d_UW")] ~ groups)
## End(Not run)
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