branchSplitFromStabilityLabels: Branch split (dissimilarity) statistics derived from labels...

View source: R/branchSplit.R

branchSplitFromStabilityLabelsR Documentation

Branch split (dissimilarity) statistics derived from labels determined from a stability study


These functions evaluate 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.


   branch1, branch2, 
   ignoreLabels = 0,

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

            branch1, branch2,
            stabilityLabels, ignoreLabels = 0, 
            verbose = 1, indent = 0,...)



A vector of indices giving members of branch 1.


A vector of indices giving members of branch 1.


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


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


integer level of verbosity. Zero means silent, higher values make the output progressively more and more verbose.


indentation for diagnostic messages. Zero means no indentation, each unit adds two spaces.




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). The three functions differ in the details of the similarity calculation.

branchSplitFromStabilityLabels.individualFraction: Currently the recommended branch split calculation method, and default for hierarchicalConsensusModules. For each branch and all clusters that overlap with the branch (not necessarily with the other branch), calculate the fraction of the cluster objects (restricted to the two branches) that belongs to the branch. For each branch, sum these fractions over all clusters. If this number is relatively low, around 0.5, it means most elements are in non-discriminative clusters.

branchSplitFromStabilityLabels: This was the original branch split measure and for backward compatibility it still is the default method in blockwiseModules and blockwiseConsensusModules. 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.

branchSplitFromStabilityLabels.prediction: Use only for experiments, not recommended for actual analyses because it is not stable under small changes in the branch membership. For each cluster that overlaps with both branches, count the objects in the branch with which the cluster has a smaller overlap and add it to the score for that branch. The final counts divided by number of genes on branch give a "indistinctness" score; take the larger of the two indistinctness scores and call this the similarity.

Since the result of the last two calculations 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).

These statistics are quite simple and do not correct for similarity that would be expected by chance. On the other hand, all 3 statistics are fairly (though not perfectly) stable under splitting and joining of clusters in stabilityLabels.


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


Peter Langfelder

See Also

These function are utilized in blockwiseModules, blockwiseConsensusModules and hierarchicalConsensusModules.

WGCNA documentation built on Jan. 22, 2023, 1:34 a.m.