immunarch (version 0.10.3)

vis.immunr_kmeans: 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 immunr_kmeans
vis(
  .data,
  .point = TRUE,
  .text = TRUE,
  .ellipse = TRUE,
  .point.size = 2,
  .text.size = 10,
  .plot = c("clust", "best"),
  ...
)

Value

Ggplot2 objects inside the pathwork container.

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.

See Also

vis, repOverlapAnalysis, geneUsageAnalysis

Examples

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