Group-wise comparisons of microbiome composition

PERMANOVA for community-level multivariate comparisons

PERMANOVA quantifies multivariate community-level differences between groups.

Load example data:

# Load libraries
library(microbiome)
library(ggplot2)
library(dplyr)

# Probiotics intervention example data 
data(peerj32) # Source: https://peerj.com/articles/32/
pseq <- peerj32$phyloseq # Rename the example data

# Pick relative abundances (compositional) and sample metadata 
pseq.rel <- microbiome::transform(pseq, "compositional")
otu <- abundances(pseq.rel)
meta <- meta(pseq.rel)

Visualize microbiome variation

Visualize the population density and highlight sample groups (probiotic treatment LGG vs Placebo):

p <- plot_landscape(pseq.rel, method = "NMDS", distance = "bray", col = "group", size = 3)
print(p)

PERMANOVA significance test for group-level differences

Now let us evaluate whether the group (probiotics vs. placebo) has a significant effect on overall gut microbiota composition. Perform PERMANOVA:

# samples x species as input
library(vegan)
permanova <- adonis(t(otu) ~ group,
               data = meta, permutations=99, method = "bray")

# P-value
print(as.data.frame(permanova$aov.tab)["group", "Pr(>F)"])

## [1] 0.28

Checking the homogeneity condition

Check that variance homogeneity assumptions hold (to ensure the reliability of the results):

# Note the assumption of similar multivariate spread among the groups
# ie. analogous to variance homogeneity
# Here the groups have signif. different spreads and
# permanova result may be potentially explained by that.
dist <- vegdist(t(otu))
anova(betadisper(dist, meta$group))

## Analysis of Variance Table
## 
## Response: Distances
##           Df   Sum Sq   Mean Sq F value Pr(>F)
## Groups     1 0.000016 0.0000160  0.0043 0.9483
## Residuals 42 0.157881 0.0037591

Investigate the top factors

Show coefficients for the top taxa separating the groups