Proportional sample exposures will be used as input to perform clustering.

cluster_exposure(
result,
nclust,
proportional = TRUE,
method = "kmeans",
dis.method = "euclidean",
hc.method = "ward.D",
clara.samples = 5,
iter.max = 10,
tol = 1e-15
)

## Arguments

result |
A `musica_result` object generated by
a mutational discovery or prediction tool. |

nclust |
Pre-defined number of clusters. |

proportional |
Logical, indicating if proportional exposure (default) will be used for clustering. |

method |
Clustering algorithms. Options are "kmeans" (K-means), "hkmeans" (hybrid of hierarchical
K-means), "hclust" (hierarchical clustering), "pam" (PAM), and "clara" (Clara). |

dis.method |
Methods to calculate dissimilarity matrix. Options are "euclidean" (default), "manhattan",
"jaccard", "cosine", and "canberra". |

hc.method |
Methods to perform hierarchical clustering. Options are "ward.D" (default), "ward.D2",
"single", "complete", "average", "mcquitty", "median", and "centroid". |

clara.samples |
Number of samples to be drawn from dataset. Only used when "clara" is selected.
Default is 5. |

iter.max |
Maximum number of iterations for k-means clustering. |

tol |
Tolerance level for kmeans clustering level iterations |

## Value

A one-column data frame with sample IDs as row names and cluster number for each sample.

## See also

## Examples

#> Metric: 'euclidean'; comparing: 7 vectors.

#> Warning: FANNY algorithm has not converged in 'maxit' = 10 iterations