mergeCommunities: Merge communities from graph-based clustering
In bluster: Clustering Algorithms for Bioconductor

Description Usage Arguments Details Value Author(s) See Also Examples

Adjust the resolution of a graph-based community detection algorithm by greedily merging clusters together. At each step, the pair of clusters that yield the highest modularity are merged.

1	mergeCommunities(graph, clusters, number = NULL, steps = NULL)

`graph`	A graph object from igraph, usually where each node represents an observation.
`clusters`	Factor specifying the cluster identity for each node.
`number`	Integer scalar specifying the number of clusters to obtain. Ignored if `steps` is specified.
`steps`	Integer scalar specifying the number of merge steps.

This function is similar to the cut_at function from the igraph package, but works on clusters that were not generated by a hierarchical algorithm. The aim is to facilitate rapid adjustment of the number of clusters without having to repeat the clustering - or, even worse, repeating the graph construction, e.g., in makeSNNGraph.

A vector or factor of the same length as clusters, containing the desired number of merged clusters.

Aaron Lun

cut_at, for a faster and more natural adjustment when using a hierarchical community detection algorithm.

NNGraphParam, for a one-liner to generate graph-based clusters.

output <- clusterRows(iris[,1:4], NNGraphParam(k=5), full=TRUE)
table(output$clusters)

merged <- mergeCommunities(output$objects$graph, output$clusters, number=3)
table(merged)