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Beta diversity calculator (Jaccard / Bray–Curtis): compare samples

Compute between-sample differences (beta diversity) as a distance matrix using Jaccard (presence/absence) or Bray–Curtis (abundance). Paste an OTU/ASV table or import CSV, then explore results with a heatmap and PCoA (2D).

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How to use (3 steps)

  1. Choose an example, or paste an OTU/ASV table (or import a CSV/TSV file).
  2. Select a metric (Jaccard / Bray–Curtis) and preprocessing (relative abundance, etc.).
  3. 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

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.

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

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.

Comments

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