WGCNA (version 1.43)

branchSplitFromStabilityLabels: Branch split (dissimilarity) statistic derived from labels determined from a stability study

Description

This function evaluates how different two branches are based on a series of cluster labels that are usually obtained in a stability study but can in principle be arbitrary. The idea is to quantify how well membership on the two tested branches can be predicted from clusters in the given stability labels.

Usage

branchSplitFromStabilityLabels(
   branch1, branch2, 
   stabilityLabels, 
   ignoreLabels = 0,
   ...)

Arguments

branch1

A vector of indices giving members of branch 1.

branch2

A vector of indices giving members of branch 1.

stabilityLabels

A matrix of cluster labels. Each column corresponds to one clustering and each row to one object (whose indices branch1 and branch2 refer to).

ignoreLabels

Label or labels that do not constitute proper clusters in stabilityLabels, for example because they label unassigned objects.

...

Ignored.

Value

• Branch dissimilarity (a single number between 0 and 1).

Details

The idea is to measure how well clusters in stabilityLabels can distinguish the two given branches. For example, if a cluster C intersects with branch1 but not branch2, it can distinguish branches 1 and 2 perfectly. On the other hand, if there is a cluster C that contains both branch 1 and branch 2, the two branches are indistinguishable (based on the test clustering).

Formally, for each cluster C in each clustering in stabilityLabels, its contribution to the branch similarity is min(r1, r2), where r1 = |intersect(C, branch1)|/|branch1| and r2 = |intersect(C, branch2)|/|branch2|. The statistics for clusters in each clustering are added; the sums are then averaged across the clusterings. Since the result is a similarity statistic, the final dissimilarity is defined as 1-similarity. The dissimilarity ranges between 0 (branch1 and branch2 are indistinguishable) and 1 (branch1 and branch2 are perfectly distinguishable).

This is a very simple statistic that does not attempt to correct for the similarity that would be expected by chance.

See Also

This function is utilized in blockwiseModules and blockwiseConsensusModules.