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 1.10.0 Index]