plotSimilarityMatrix: Plot similarity matrix with pheatmap

In klic: Kernel Learning Integrative Clustering

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 is TRUE, this is the string containing the name of the output file. Can be used to specify the folder path too. Default is "posteriorSimilarityMatrix". The extension ".png" is automatically added to this string.
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 [e-mail]

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)

Example output

klic documentation built on July 8, 2020, 6:23 p.m.