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

In immunarch: Bioinformatics Analysis of T-Cell and B-Cell Immune Repertoires

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 fviz_cluster.
.text	If TRUE then plot text labels. Passed to fviz_cluster.
.ellipse	If TRUE then plot ellipses around all samples. Passed to "ellipse" from fviz_cluster.
.point.size	Size of points, passed to "pointsize" from fviz_cluster.
.text.size	Size of text labels, passed to labelsize from 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

data(immdata)
ov <- repOverlap(immdata$data)
repOverlapAnalysis(ov, "mds+kmeans") %>% vis()

immunarch documentation built on Dec. 28, 2022, 2:59 a.m.