How to use (3 steps)
- Choose an example, or paste an OTU/ASV table (or import a CSV/TSV file).
- Select a metric (Jaccard / Bray–Curtis) and preprocessing (relative abundance, etc.).
- Review the distance matrix, heatmap, and PCoA (export CSV/PNG if needed).
Beta diversity is useful for exploration and visualization, but statistical conclusions require additional analyses (tests or modeling).
Inputs
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Metric & preprocessing
If sample depths differ, try “Relative abundance”.
Sample groups (optional: color points in PCoA)
Two columns: sample, group (header is optional).
Results
This tool is for exploration and learning. It does not claim statistical significance.
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Share & export
The share URL restores settings only (input data is not included). To save inputs too, use JSON export.
Distance matrix
Heatmap
PCoA (2D)
Jaccard vs Bray–Curtis
- Jaccard compares presence/absence only.
- Bray–Curtis also uses abundances (counts or relative abundance).
Bray–Curtis can be affected by library size (sample depth). If needed, compare both counts and relative abundance.
Equations (reference)
- Jaccard distance:
d = 1 - |A∩B| / |A∪B| - Bray–Curtis dissimilarity:
d = Σ|xᵢ - yᵢ| / Σ(xᵢ + yᵢ)
Here, A and B are sets of observed features, and xᵢ/yᵢ are feature values (relative abundance or counts). Axis directions are arbitrary, so flipping the PCoA plot does not change the meaning.
FAQ
What is beta diversity?
A way to describe how different samples are from each other as a distance (dissimilarity). Smaller means more similar; larger means more different.
What is the difference between Jaccard and Bray–Curtis?
Jaccard compares presence/absence only. Bray–Curtis also uses abundances (counts or relative abundance).
What is PCoA?
A method that places samples in 2D based on the distance matrix (principal coordinates analysis). Axis direction is arbitrary, so flipping does not change the meaning.
Can I claim statistical significance from this result alone?
No. Beta diversity is useful for exploration and visualization, but statistical testing requires additional analyses.
What should I do first on this page?
Start with the minimum required inputs or the first action shown near the primary button. Keep optional settings at defaults for a baseline run, then change one setting at a time so you can explain what caused each output change.
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