PermanovaG2: Permutational Multivariate Analysis of Variance Using...

In GUniFrac: Generalized UniFrac Distances, Distance-Based Multivariate Methods and Feature-Based Univariate Methods for Microbiome Data Analysis

Permutational Multivariate Analysis of Variance Using Multiple Distance Matrices(Freedman-Lane Permutation)

Description

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.

Usage

PermanovaG2(formula, data = NULL, ...)

Arguments

formula	a formula, left side of the formula (Y ~ X) is a three dimensional ARRAY containing the supplied distance matrices as produced by GUniFrac function. Or it could be a list of distance matrices.
data	a data frame containing the covariates
...	parameters passing to adonis function

Value

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 adonis AOV tables for individual distance matrices

Author(s)

Jun Chen <[e-mail]>

References

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.

See Also

Rarefy, GUniFrac, adonis3

Examples

## 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)

GUniFrac documentation built on Sept. 14, 2023, 1:07 a.m.