plotMOC {coca} R Documentation

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 alessandra.cabassi@mrc-bsu.cam.ac.uk

References

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

Examples

# Load data
data <- list()
package = "coca"), row.names = 1))
package = "coca"), row.names = 1))
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