Cluster the data with one of the following methods:
- immunr_hclust
clusters the data using the hierarchical clustering from hcut;
- immunr_kmeans
clusters the data using the K-means algorithm from kmeans;
- immunr_dbscan
clusters the data using the DBSCAN algorithm from dbscan.
immunr_hclust(.data, .k = 2, .k.max = nrow(.data) - 1, .method = "complete", .dist = T) immunr_kmeans(.data, .k = 2, .k.max = as.integer(sqrt(nrow(.data))) + 1, .method = c("silhouette", "gap_stat")) immunr_dbscan(.data, .eps, .dist = T)
.data | Matrix or data frame with features, distance matrix or output from repOverlapAnalysis or geneUsageAnalysis functions. |
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.k | The number of clusters to create, passed as |
.k.max | Limits the maximum number of clusters. It is passed as |
.method | Passed to hcut or as fviz_nbclust. In case of hcut the agglomeration method is going to be used (argument In case of fviz_nbclust it is the method to be used for estimating the optimal number of clusters (argument |
.dist | If TRUE then ".data" is expected to be a distance matrix. If FALSE then the euclidean distance is computed for the input objects. |
.eps | Local radius for expanding clusters, minimal distance between points to expand clusters. Passed as |