vis.immunr_kmeans: Visualisation of K-means and DBSCAN clustering

In immunarch: Multi-Modal Immune Repertoire Analytics for Immunotherapy and Vaccine Design in R

Visualisation of K-means and DBSCAN clustering

Description

Visualisation of the results of K-means and DBSCAN clustering. For hierarhical clustering visualisations see vis.immunr_hclust.

Usage

## S3 method for class 'immunr_kmeans'
vis(
  .data,
  .point = TRUE,
  .text = TRUE,
  .ellipse = TRUE,
  .point.size = 2,
  .text.size = 10,
  .plot = c("clust", "best"),
  ...
)

Arguments

.data	Clustering results from repOverlapAnalysis or geneUsageAnalysis.
.point	If TRUE then plot sample points. Passed to factoextra::fviz_cluster.
.text	If TRUE then plot text labels. Passed to factoextra::fviz_cluster.
.ellipse	If TRUE then plot ellipses around all samples. Passed to "ellipse" from factoextra::fviz_cluster.
.point.size	Size of points, passed to "pointsize" from factoextra::fviz_cluster.
.text.size	Size of text labels, passed to labelsize from factoextra::fviz_cluster.
.plot	A character vector of length one or two specifying which plots to visualise. If "clust" then plot only the clustering. If "best" then plot the number of optimal clusters. If both then plot both.
...	Not used here.

Value

Ggplot2 objects inside the pathwork container.

See Also

vis, repOverlapAnalysis, geneUsageAnalysis

Examples

## Not run: 
data(immdata)
ov <- repOverlap(immdata$data)
repOverlapAnalysis(ov, "mds+kmeans") %>% vis()

## End(Not run)

immunarch documentation built on Nov. 5, 2025, 7:21 p.m.