R: Clustering of objects or distance matrices

immunr_hclust {immunarch} | R Documentation

## Clustering of objects or distance matrices

### Description

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.

### Usage

    immunr_hclust(.data, .k = 2, .k.max = nrow(.data) - 1, .method = "complete", .dist = TRUE)

    immunr_kmeans(.data, .k = 2, .k.max = as.integer(sqrt(nrow(.data))) + 1,
    .method = c("silhouette", "gap_stat"))

    immunr_dbscan(.data, .eps, .dist = TRUE)

### Arguments

`.data` | Matrix or data frame with features, distance matrix or output from repOverlapAnalysis or geneUsageAnalysis functions.
`.k` | The number of clusters to create, passed as `k` to hcut or as `centers` to kmeans.
`.k.max` | Limits the maximum number of clusters. It is passed as `k.max` to fviz_nbclust for `immunr_hclust` and `immunr_kmeans`.
`.method` | Passed to hcut or as fviz_nbclust. In case of hcut the agglomeration method is going to be used (argument `hc_method`). In case of fviz_nbclust it is the method to be used for estimating the optimal number of clusters (argument `method`).
`.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 `eps` to dbscan.

### Value

`immunr_hclust` - list with two elements. First element is an output from hcut. Second element is an output from fviz_nbclust

`immunr_kmeans` - list with three elements. First element is an output from kmeans. Second element is an output from fviz_nbclust. Third element is the input dataset `.data`.

`immunr_dbscan` - list with two elements. First element is an output from dbscan. Second element is the input dataset `.data`.

### Examples

    data(immdata)
    gu <- geneUsage(immdata$data, .norm = TRUE)
    immunr_hclust(t(as.matrix(gu[, -1])), .dist = FALSE)

    gu[is.na(gu)] <- 0
    immunr_kmeans(t(as.matrix(gu[, -1])))

[Package immunarch version 0.6.7 Index]