plotMOC {coca}

Plot Matrix-Of-Clusters

Description

This function creates a matrix of clusters, starting from a list of heterogeneous datasets.

Usage

plotMOC(
  moc,
  datasetIndicator,
  datasetNames = NULL,
  annotations = NULL,
  clr = FALSE,
  clc = FALSE,
  savePNG = FALSE,
  fileName = "moc.png",
  showObsNames = FALSE,
  showClusterNames = FALSE,
  annotation_colors = NA
)

Arguments

moc

Matrix-Of-Clusters of size N x sumK.

datasetIndicator

Vector containing integers indicating which rows correspond to some clustering of the same dataset.

datasetNames

Vector containing the names of the datasets to which each column of labels corresponds. If NULL, datasetNames will be the same as datasetIndicator. Default is NULL.

annotations

Dataframe containing annotations. Number of rows must be N. If the annotations are integers, use as.factor() for a better visual result.

clr

Cluster rows. Default is FALSE.

clc

Cluster columns. Default is FALSE.

savePNG

Boolean. If TRUE, plot is saved as a png file.

fileName

If savePNG is TRUE, this is the string containing the name of the moc figure. Can be used to specify the folder path too. Default is "moc". The ".png" extension is automatically added to this string.

showObsNames

Boolean. If TRUE, the plot will also include the column names (i.e. name of each observation). Default is FALSE, since there are usually too many columns.

showClusterNames

Boolean. If TRUE, plot cluster names next to corresponding row. Default is FALSE.

annotation_colors

Optional. See annotation_colors in pheatmap::pheatmap.

Author(s)

Alessandra Cabassi [e-mail]

References

The Cancer Genome Atlas, 2012. Comprehensive molecular portraits of human breast tumours. Nature, 487(7407), pp.61–70.

Examples

# Load data
data <- list()
data[[1]] <- as.matrix(read.csv(system.file("extdata", "dataset1.csv",
package = "coca"), row.names = 1))
data[[2]] <- as.matrix(read.csv(system.file("extdata", "dataset2.csv",
package = "coca"), row.names = 1))
data[[3]] <- as.matrix(read.csv(system.file("extdata", "dataset3.csv",
package = "coca"), row.names = 1))

# Create vector of dataset names, in the same order as they appear above
datasetNames <- c("Dataset1", "Dataset2", "Dataset3")

# Build matrix of clusters
outputBuildMOC <- buildMOC(data, M = 3, K = 6, distances = "cor")

# Extract matrix of clusters and dataset indicator vector
moc <- outputBuildMOC$moc
datasetIndicator <- outputBuildMOC$datasetIndicator

# Prepare annotations
true_labels <- as.matrix(read.csv(
system.file("extdata", "cluster_labels.csv", package = "coca"),
row.names = 1))
annotations <- data.frame(true_labels = as.factor(true_labels))

# Plot matrix of clusters
plotMOC(moc,
        datasetIndicator,
        datasetNames = datasetNames,
        annotations = annotations)