The Diversity Analysis module quantifies microbial diversity within and between samples, helping you understand community structure, stability, and differences across experimental groups.
What Is Diversity Analysis?
Microbial diversity analysis answers two fundamental questions:
- How diverse is each individual sample? (Alpha diversity)
- How different are samples from each other? (Beta diversity)
These metrics are central to microbiome research and are widely used in comparative studies such as case vs control, treatment vs baseline, or time-series experiments.
Alpha Diversity
What It Measures
Alpha diversity quantifies diversity within a single sample, capturing both:
- The number of taxa present
- How evenly they are distributed
Common Metrics
- Shannon Index
- Accounts for richness and evenness
- Simpson Index
- Emphasizes dominant taxa
- Observed Richness
- Counts the number of observed taxa
Typical Use Cases
- Comparing diversity between groups (e.g. healthy vs diseased)
- Assessing the impact of treatments or environmental changes
- Identifying low-diversity or outlier samples
Alpha diversity differences are most meaningful when sequencing depth and processing parameters are consistent across samples.
Beta Diversity
What It Measures
Beta diversity quantifies differences between samples based on their community composition.
It answers the question: How similar or different are two microbial communities?
Common Distance Metrics
- Bray–Curtis
- Abundance-based, widely used
- Jaccard
- Presence/absence–based
- UniFrac
- Phylogeny-aware (if a phylogenetic tree is available)
Typical Outputs
- PCoA (Principal Coordinates Analysis) plots
- Distance heatmaps
- Clustering patterns by cohort, phenotype, or batch
Overlaying metadata (group, batch, phenotype) on PCoA plots greatly improves biological interpretability.
Normalization & Preprocessing
Diversity metrics are sensitive to sequencing depth and preprocessing choices.
Common strategies include:
- Rarefaction (when required by method)
- Relative abundance normalization
- Consistent filtering thresholds across samples
Never compare diversity metrics computed using different normalization strategies or reference databases.
Best Practices
To ensure robust and interpretable diversity results:
- Use consistent preprocessing across all samples
- Always examine alpha diversity distributions before group comparison
- Interpret beta diversity alongside metadata and study design
- Be cautious of batch effects and technical artifacts
Diversity analysis is most powerful when combined with taxonomic and functional profiling results.
What’s Next?
After diversity analysis, you may want to explore:
- Differential abundance testing
- Functional profiling
- Integrating diversity metrics into AI-based classification models