Community Resolution Selector

Select a tested community resolution closest to the target cluster count.

Description

Community-resolution tuning can compare previously tested settings against a target cluster count.

Community-resolution tuning can compare previously tested settings against a target cluster count. The nearest observed count is a discrete selection, not a new clustering fit.^[1]

Select a tested community resolution closest to the target cluster count.

When to use Community Resolution Selector

Use this tool to select a tested community resolution closest to the target cluster count.

Prepare the upstream counts, expression values, sequences or record lists requested by this tool. Keep corresponding features in the same order and confirm count units, transformations and caller-selected criteria.

How to use Community Resolution Selector and understand its results

Prepare the upstream counts, expression values, sequences or record lists requested by this tool. Keep corresponding features in the same order and confirm count units, transformations and caller-selected criteria.
Enter resolutions, clusterCounts, targetClusters using the field table and calculation rules, then run the tool.
Read selectedResolution, selectedClusterCount, clusterCountError. The explanation below defines the units, signs, ordering and any flags or placeholder values.
If the tool reports an input issue, correct the values and run it again. Apply the stated assumptions when interpreting a successfully returned result.

Input field	Meaning	Value type; see rules below	Requirement
resolutions	Resolutions	list of number	Required
clusterCounts	Cluster counts	list of number	Required
targetClusters	Target clusters	number	Required

Enter aligned nonempty resolution/count lists, positive safe-integer observed counts and a finite target. Return the resolution with smallest absolute count error, its count and that error. Ties retain the first supplied setting; resolutions are not sorted.

select first minimum of |observedClusterCount−targetClusters|

Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.

Output field	Meaning	Value type
selectedResolution	Selected resolution	number
selectedClusterCount	Selected cluster count	number
clusterCountError	Cluster count error	number

json

{
  "input": {
    "resolutions": [
      0.2,
      0.5,
      1
    ],
    "clusterCounts": [
      2,
      4,
      8
    ],
    "targetClusters": 5
  },
  "output": {
    "selectedResolution": 0.5,
    "selectedClusterCount": 4,
    "clusterCountError": 1
  }
}

Limitations and assumptions

The target and resolutions need not be positive integers in the runtime. No community detection, interpolation, stability analysis or biological cluster assessment is performed.

Numeric inputs must be finite. Follow the calculation-specific rules for overflow, underflow and exceptional cases. Lists are limited to 100,000 entries; the rules above may require smaller limits. Missing required fields, extra fields, wrong value types and violations of the calculation rules are input errors.

Alternative or Complementary methods

Use Silhouette Score: Compute a silhouette width from within- and between-cluster distances. For a complementary task, use HVG Dispersion Selector: Select highly variable genes by variance-to-mean dispersion.

References

Community Resolution Selector — tool.io

Similar or alternative tools

Silhouette Score
Compute a silhouette width from within- and between-cluster distances.
HVG Dispersion Selector
Select highly variable genes by variance-to-mean dispersion.