Chapter 4: Alpha and Beta Diversity Statistics & Ordination
Chapter 4: Alpha and Beta Diversity Statistics & Ordination
Ecological diversity statistics summarize microbial community dynamics within individual samples (Alpha diversity) and measure differences in community composition across samples or experimental cohorts (Beta diversity).
Core Diversity Metrics
- Alpha Diversity:
- Observed Features / Chao1: Pure species richness (count of unique ASVs).
- Shannon Index: Accounts for both richness and evenness (\(H = -\sum p_i \ln p_i\)).
- Faith's Phylogenetic Diversity (PD): Sum of branch lengths on a phylogenetic tree connecting observed species.
- Beta Diversity:
- Bray-Curtis Dissimilarity: Non-phylogenetic abundance-weighted dissimilarity.
- Weighted vs. Unweighted UniFrac: Phylogenetic distance accounting for evolutionary relatedness. Unweighted captures presence/absence; Weighted incorporates relative abundance.
1. Computing Core Metrics in QIIME 2
# Run complete diversity suite rarefied at 10,000 reads/sample
qiime diversity core-metrics-phylogenetic \\
--i-phylogeny rooted-tree.qza \\
--i-table table.qza \\
--p-sampling-depth 10000 \\
--m-metadata-file sample-metadata.tsv \\
--output-dir core-metrics-results2. Statistical Hypothesis Testing with PERMANOVA (adonis2)
Visualizing separation on a PCoA plot is insufficient; you must statistically test whether group differences are significant using PERMANOVA (Permutational Multivariate Analysis of Variance) in R.
library(vegan)
library(phyloseq)
# Extract distance matrix and metadata
dist_matrix <- phyloseq::distance(physeq_obj, method = "bray")
metadata <- as(sample_data(physeq_obj), "data.frame")
# Test treatment effect while controlling for host sex and age
fit <- adonis2(dist_matrix ~ Treatment + Sex + Age, data = metadata, permutations = 999)
print(fit)