plotSimilarityMatrix {klic} | R Documentation |
Plot similarity matrix with pheatmap
Description
Plot similarity matrix with pheatmap
Usage
plotSimilarityMatrix(
X,
y = NULL,
clusLabels = NULL,
colX = NULL,
colY = NULL,
myLegend = NULL,
fileName = "posteriorSimilarityMatrix",
savePNG = FALSE,
semiSupervised = FALSE,
showObsNames = FALSE,
clr = FALSE,
clc = FALSE,
plotWidth = 500,
plotHeight = 450
)
Arguments
X |
Similarity matrix. |
y |
Vector |
clusLabels |
Cluster labels |
colX |
Colours for the matrix |
colY |
Colours for the response |
myLegend |
Vector of strings with the names of the variables |
fileName |
If |
savePNG |
Boolean: if TRUE, the plot is saved as a png file. Default is FALSE. |
semiSupervised |
Boolean flag: if TRUE, the response is plotted next to the matrix. |
showObsNames |
Boolean. If TRUE, observation names are shown in the plot. Default is FALSE. |
clr |
Boolean. If TRUE, rows are ordered by hierarchical clustering. Default is FALSE. |
clc |
Boolean. If TRUE, columns are ordered by hierarchical clustering. Default is FALSE. |
plotWidth |
Plot width. Default is 500. |
plotHeight |
Plot height. Default is 450. |
Value
No return value. This function plots the similarity matrix either to screen or to a png file.
Author(s)
Alessandra Cabassi alessandra.cabassi@mrc-bsu.cam.ac.uk
Examples
# Load one dataset with 100 observations, 2 variables, 4 clusters
data <- as.matrix(read.csv(system.file("extdata", "dataset1.csv",
package = "klic"), row.names = 1))
# Load cluster labels
cluster_labels <- as.matrix(read.csv(system.file("extdata",
"cluster_labels.csv", package = "klic"), row.names = 1))
# Compute consensus clustering with K=4 clusters
cm <- coca::consensusCluster(data, 4)
# Plot consensus (similarity) matrix
plotSimilarityMatrix(cm)
# Plot consensus (similarity) matrix with response
names(cluster_labels) <- as.character(1:100)
rownames(cm) <- names(cluster_labels)
plotSimilarityMatrix(cm, y = cluster_labels)