plotSim: Plot (dis)similarity matrix

In adjclust: Adjacency-Constrained Clustering of a Block-Diagonal Similarity Matrix

Plot (dis)similarity matrix

Description

Heatmap of the (dis)similarity matrix

Usage

plotSim(
  mat,
  type = c("similarity", "dissimilarity"),
  clustering = NULL,
  dendro = NULL,
  k = NULL,
  log = TRUE,
  legendName = "intensity",
  main = NULL,
  priorCount = 0.5,
  stats = c("R.squared", "D.prime"),
  h = NULL,
  axis = FALSE,
  naxis = min(10, nrow(mat)),
  axistext = NULL,
  xlab = "objects",
  cluster_col = "darkred",
  mode = c("standard", "corrected", "total-disp", "within-disp", "average-disp")
)

Arguments

mat	matrix to plot. It can be of class 'matrix', 'dgCMatrix', 'dsCMatrix', 'dist', 'HTCexp', 'snpMatrix'.
type	input matrix type. Can be either "similarity" or "dissimilarity" (kernels are supposed to be of type "similarity").
clustering	vector of clusters to display on the matrix (if not NULL). If clustering is provided, it must be a numeric vector of length identical to the matrix size with clusters identified as consecutive integers 1, 2, 3, ...
dendro	dendrogram provided as an hclust object, or as another type of object that can be converted to hclust with as.hclust.
k	number of clusters to display. Used only when dendro is not NULL and clustering is NULL. The clustering is then deduced from the dendrogram by a standard cut.
log	logical. Should the breaks be based on log-scaled values of the matrix entries. Default to TRUE.
legendName	character. Title of the legend. Default to "intensity".
main	character. Title of the plot. Default to NULL (no title).
priorCount	numeric. Average count to be added to each entry of the matrix to avoid taking log of zero. Used only if log == TRUE and default to 0.5.
stats	input SNP correlation type. Used when mat is of type SnpMatrix.
h	positive integer. Threshold distance for SNP correlation computation. Used when mat is of type SnpMatrix.
axis	logical. Should x-axis be displayed on the plot? Default to FALSE.
naxis	integer. If axis == TRUE, number of ticks to display on the x-axis.
axistext	character vector. If axis == TRUE, labels to display of the x-axis (its length has to be equal to naxis).
xlab	character. If axis == TRUE, x-axis title.
cluster_col	colour for the cluster line if clustering is not NULL.
mode	type of dendrogram to plot (see plot.chac). Default to "standard".

Details

This function produces a heatmap for the used (dis)similarity matrix that can be used as a diagnostic plot to check the consistency between the obtained clustering and the original (dis)similarity

See Also

select, adjClust

Examples

## Not run: 
clustering <- rep(1:3, each = 50)
dist_data <- as.matrix(dist(iris[, 1:4]))
dendro_iris <- adjClust(dist_data, type = "dissimilarity")
plotSim(dist_data, type = "dissimilarity", dendro = dendro_iris, axis = TRUE)
plotSim(dist_data, type = "dissimilarity", dendro = dendro_iris,
        clustering = clustering)
plotSim(dist_data, type = "dissimilarity", dendro = dendro_iris, axis = TRUE,
        k = 3)
plotSim(dist_data, type = "dissimilarity", legendName = "IF", axis = TRUE, 
        clustering = clustering)
p <- plotSim(dist(iris[, 1:4]), type = "dissimilarity", log = FALSE, 
             clustering = clustering, cluster_col = "blue")
# custom palette
p + scale_fill_gradient(low = "yellow", high = "red")
# dsCMatrix
m <- Matrix(c(0, 0, 2, 0, 3, 0, 2, 0, 0), ncol = 3)
res <- adjClust(m)
plotSim(m, axis = TRUE)
plotSim(m, dendro = res)
# dgCMatrix
m <- as(m, "generalMatrix")
plotSim(m)
m <- as.dist(m)
if (require("HiTC", quietly = TRUE)) {
  load(system.file("extdata", "hic_imr90_40_XX.rda", package = "adjclust"))
  res <- hicClust(hic_imr90_40_XX, log = TRUE)
  plotSim(hic_imr90_40_XX, axis = TRUE)
}
if (requireNamespace("snpStats", quietly = TRUE)) {
  data(testdata, package = "snpStats")
  plotSim(Autosomes[1:200, 1:5], h = 3, stats = "R.squared", axis = TRUE,
          axistext = c("A", "B", "C", "D", "E"))
}

## End(Not run)

adjclust documentation built on Oct. 8, 2024, 9:07 a.m.