Bray Curtis Sample Matrix

Build a Bray–Curtis pairwise dissimilarity matrix for samples with aligned taxa.

Description

Bray–Curtis dissimilarity compares aligned abundance profiles.

Bray–Curtis dissimilarity compares aligned abundance profiles. It measures compositional difference on a bounded scale for nonnegative abundances.^[1]

Build a Bray–Curtis pairwise dissimilarity matrix for samples with aligned taxa.

When to use Bray Curtis Sample Matrix

Use this for the following task: Build a Bray–Curtis pairwise dissimilarity matrix for samples with aligned taxa.

Prepare the caller-filtered sequences, counts, coordinates or annotations requested below. Keep identifiers and paired lists aligned on the same reference.

How to use Bray Curtis Sample Matrix and understand its results

Prepare the caller-filtered sequences, counts, coordinates or annotations requested below. Keep identifiers and paired lists aligned on the same reference.
Enter sampleCounts using the field table, then run the tool.
Read dissimilarityMatrix. Interpret flags, units, ordering and placeholder values using the rules below.
Review any reported issue or failed check, correct the supplied values when appropriate, and verify the result in its intended context.

Input field	Meaning	Value type and bounds	Requirement
sampleCounts	Sample counts	list of list of number; minimum entries 1; maximum entries 128; each entry: list of number; minimum entries 1; maximum entries 128; each entry: number; minimum 0	Required

Supply a rectangular sample-by-taxon matrix with positive abundance in every sample. Compute each pair, use scaling if sums overflow, mirror the matrix and keep diagonal zero.^[2]

d(a,b) = Σ|ak−bk|/Σ(ak+bk)

This summarizes the declared operation. Units, indexing, inclusivity and exceptional cases are explained in the surrounding text.

Output field	Meaning	Value type
dissimilarityMatrix	Dissimilarity matrix	list of list of number

json

{
  "input": {
    "sampleCounts": [
      [
        1,
        0
      ],
      [
        0,
        1
      ],
      [
        1,
        1
      ]
    ]
  },
  "output": {
    "dissimilarityMatrix": [
      [
        0,
        1,
        0.3333333333333333
      ],
      [
        1,
        0,
        0.3333333333333333
      ],
      [
        0.3333333333333333,
        0.3333333333333333,
        0
      ]
    ]
  }
}

Limitations and assumptions

Taxon columns must align across samples. This does not normalize sampling effort or estimate statistical uncertainty.

The tool operates on supplied local data; alignment, filtering and annotation must already be appropriate for the stated calculation. Malformed requests produce input errors. A successfully evaluated request can still contain false flags, empty result lists or a result outside the supplied threshold; those outcomes have the meanings stated above.

Alternative or Complementary methods

Choose Abundance Column Percent for this operation: Normalize a taxa-by-sample abundance matrix to percentages within each sample column. For a complementary task, use GC Coverage Dropout for this operation: Find GC bins with coverage deficits relative to a caller-supplied expected depth.

Further background: Background specification: Bray Curtis Sample Matrix.

References

Background specification: Bray Curtis Sample Matrix — SciPy
Bray Curtis Sample Matrix — tool.io

Similar or alternative tools

Abundance Column Percent
Normalize a taxa-by-sample abundance matrix to percentages within each sample column.
GC Coverage Dropout
Find GC bins with coverage deficits relative to a caller-supplied expected depth.