@gmod/hclust

Fast hierarchical clustering (UPGMA) compiled to WebAssembly with JavaScript/TypeScript bindings.

Algorithm

Agglomerative clustering with average linkage. Computes Euclidean distances, then merges the closest clusters at each step until one cluster remains, producing a dendrogram. Equivalent to R's hclust(method="average").

Features

• WASM-accelerated distance matrix and clustering
• Float32 precision
• Newick/JSON serialization
• Cancellation via callback
• Web worker compatible

Usage

import { clusterObject, toNewick, fromNewick } from '@gmod/hclust'

const result = await clusterObject({
  data: {
    'Sample A': [1.0, 2.0, 3.0],
    'Sample B': [1.5, 2.5, 3.5],
    'Sample C': [10.0, 11.0, 12.0],
  },
})

const newick = toNewick(result.tree)
const tree = fromNewick(newick)

clusterData is also available if you have separate arrays:

import { clusterData } from '@gmod/hclust'

const result = await clusterData({
  data: [
    [1.0, 2.0, 3.0],
    [1.5, 2.5, 3.5],
    [10.0, 11.0, 12.0],
  ],
  sampleLabels: ['Sample A', 'Sample B', 'Sample C'],
})

Cancellation

Pass checkCancellation: () => void to throw and cancel:

clusterData({
  data,
  checkCancellation: () => {
    if (shouldCancel) throw new Error('cancelled')
  },
})

For web workers with cross-origin isolation, use SharedArrayBuffer + Atomics. Without it, use blob URL + synchronous XHR (web workers only).

References

• UPGMA: Sokal, R.R. & Michener, C.D. (1958).
• Lance-Williams recurrence: Lance, G.N. & Williams, W.T. (1967).
• Newick format: Olsen, G.J. (1990). http://evolution.genetics.washington.edu/phylip/newicktree.html

Note

Generated with the help of Claude Code AI, you might be able to tell from the somewhat robotic documentation