index.html).

Generalized UniFrac Distances and Distance-based Multivariate Analysis of Variance

Overview

The package implements Generalized UniFrac distances for comparing microbial communities and three extensions to permutational multivariate analysis of variance (PERMANOVA) using distance matrix. The three extensions are

PERMANOVA using the Freedman-Lane permutation scheme;
PERMANOVA omnibus test using multiple matrices;
An analytical approach to approximating PERMANOVA p-value.

Installation

install.packages(c("ape", "vegan", "Matrix", "matrixStats"))
install.packages("devtools")
devtools::install_github("jchen1981/GUniFrac")

An Example

We illustrate the usage of package using simulated data.

require(GUniFrac)

data(throat.otu.tab)
data(throat.tree)
data(throat.meta)


# Rarefaction
otu.tab.rff <- Rarefy(throat.otu.tab)$otu.tab.rff

# Calculate the UniFrac distance
unifracs <- GUniFrac(otu.tab.rff, throat.tree, alpha=c(0, 0.5, 1))$unifracs

dw <- unifracs[, , "d_1"]          # Weighted UniFrac
du <- unifracs[, , "d_UW"]         # Unweighted UniFrac     
dv <- unifracs[, , "d_VAW"]        # Variance adjusted weighted UniFrac
d0 <- unifracs[, , "d_0"]          # GUniFrac with alpha 0  
d5 <- unifracs[, , "d_0.5"]        # GUniFrac with alpha 0.5 

# Test the smoking effect based on unweighted UniFrac distance, adjusting sex.

# PERMANOVA using Freedman-Lane permutation scheme ('Sex' should be put before 'SmokingStatus')
adonis3(as.dist(unifracs[, , 'd_UW']) ~ Sex + SmokingStatus, data = throat.meta)

# Or we can permute within each sex
adonis3(as.dist(unifracs[, , 'd_UW']) ~ SmokingStatus, data = throat.meta, 
strata = throat.meta$Sex)

# Compare the p-values to the analytical approach
dmanova(as.dist(unifracs[, , 'd_UW']) ~ Sex + SmokingStatus, data = throat.meta)

# PERMANOVA omnibus test combining weighted and unweighted distance matrices
PermanovaG2(unifracs[, , c("d_1", "d_UW")] ~ Sex + SmokingStatus, data = throat.meta)