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#' Plot similarity matrix with pheatmap
#'
#' @param X Similarity matrix.
#' @param y Vector
#' @param clusLabels Cluster labels
#' @param colX Colours for the matrix
#' @param colY Colours for the response
#' @param myLegend Vector of strings with the names of the variables
#' @param savePNG Boolean: if TRUE, the plot is saved as a png file. Default is
#' FALSE.
#' @param fileName If \code{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.
#' @param semiSupervised Boolean flag: if TRUE, the response is plotted next to
#' the matrix.
#' @param showObsNames Boolean. If TRUE, observation names are shown in the
#' plot. Default is FALSE.
#' @param clr Boolean. If TRUE, rows are ordered by hierarchical clustering.
#' Default is FALSE.
#' @param clc Boolean. If TRUE, columns are ordered by hierarchical clustering.
#' Default is FALSE.
#' @param plotWidth Plot width. Default is 500.
#' @param plotHeight Plot height. Default is 450.
#' @return No return value. This function plots the similarity matrix either
#' to screen or to a png file.
#' @author Alessandra Cabassi \email{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)
#' @export
plotSimilarityMatrix = function(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) {
if (!is.null(y)) {
# Check if the rownames correspond to the ones in the similarity matrix
check <- sum(1 - rownames(X) %in% row.names(y))
if (check == 1)
stop("X and y must have the same row names.")
}
if (!is.null(clusLabels)) {
if (!is.integer(clusLabels))
stop("Cluster labels must be integers.")
n_clusters <- length(table(clusLabels))
riordina <- NULL
for (i in 1:n_clusters) {
riordina <- c(riordina, which(clusLabels == i))
}
X <- X[riordina, riordina]
y <- y[riordina, ]
y <- as.data.frame(y)
}
if (savePNG)
grDevices::png(paste(fileName, ".png", sep = ""),
width = plotWidth,
height = plotHeight)
if (!is.null(y)) {
pheatmap::pheatmap(
X,
legend = TRUE,
color = c("white", (
RColorBrewer::brewer.pal(n = 6,
name = "PuBu")
)),
cluster_rows = clr,
cluster_cols = clc,
annotation_col = y,
show_rownames = showObsNames,
show_colnames = showObsNames,
drop_levels = FALSE,
na_col = "seashell2"
)
} else {
pheatmap::pheatmap(
X,
legend = TRUE,
color = c("white", (
RColorBrewer::brewer.pal(n = 6,
name = "PuBu")
)),
cluster_rows = clr,
cluster_cols = clc,
show_rownames = showObsNames,
show_colnames = showObsNames,
drop_levels = FALSE,
na_col = "seashell2"
)
}
if (savePNG)
grDevices::dev.off()
}
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