suggest_best_k-ConsensusPartition-method {cola} | R Documentation

## Suggest the best number of partitions

### Description

Suggest the best number of partitions

### Usage

    ## S4 method for signature 'ConsensusPartition'
    suggest_best_k(object, jaccard_index_cutoff = 0.95)

### Arguments

`object` | A `ConsensusPartition-class` object.
`jaccard_index_cutoff` | The cutoff for Jaccard index compared to previous k.

### Details

The best k is selected according to following rules:

1. k with rand index larger than `jaccard_index_cutoff` are removed. If all k are removed, the best k is defined as `NA`. 2. If there are some k having `1-PAC` larger than 0.9, the largest k is selected as the best k. 3. If it does not fit rule 2, the k with highest vote of highest 1-PAC, mean_silhouette and concordance scores is selected as the best k.

`suggest_best_k` function only gives suggestion of selecting a reasonable best k. Users still need to look at the plots (e.g. by `select_partition_number` or `consensus_heatmap` functions), or even by checking whether the subgrouping gives a reasonable signatures by `get_signatures`, to pick a reasonable k that best explains their study.

The best k with 1-PAC larger than 0.9 is treated as a stable partition.

### Value

The best k.

### Author(s)

Zuguang Gu <z.gu@dkfz.de>

### Examples

    data(cola_rl)
    obj = cola_rl["sd", "kmeans"]
    suggest_best_k(obj)

[Package cola version 1.0.1 Index]