splitTestClusters {MetaNeighbor}R Documentation

Split test clusters according to AUROC similarity to train clusters.

Description

This function computes hierarchical clustering to group similar test clusters, using similarity to train clusters as features, then uses a standard tree cutting algorithm to obtain groups of similar clusters. Note that the cluster hierarchy does *not* correspond to the row ordering of plotHeatmapPretrained function, which uses a different heuristic.

Usage

splitTestClusters(mn_scores, k)

Arguments

mn_scores

An AUROC matrix as generated by MetaNeighborUS, usually with the "trained_model" option.

k

The number of desired cluster sets.

Value

A list of cluster sets, each cluster set is a character vector containg cluster labels.

See Also

plotHeatmapPretrained


[Package MetaNeighbor version 1.13.0 Index]