data("Wood")
# regular heatmap from package stats
heatmap(Wood, main = "Wood (standard heatmap)")
# Default heatmap does Euclidean distance, hierarchical clustering with
# complete-link and optimal leaf ordering. Note that the rows are
# ordered top-down in the seriation order (stats::heatmap orders in reverse)
hmap(Wood, main = "Wood (opt. leaf ordering)")
hmap(Wood, plot_margins = "distances", main = "Wood (opt. leaf ordering)")
hmap(Wood, plot_margins = "none", main = "Wood (opt. leaf ordering)")
# Heatmap with correlation-based distance, green-red color (greenred is
# predefined) and optimal leaf ordering and no row label
dist_cor <- function(x) as.dist(sqrt(1 - cor(t(x))))
hmap(Wood, distfun = dist_cor, col = greenred(100),
main = "Wood (reorded by corr. between obs.)")
# Heatmap with order based on the angle in two-dimensional MDS space.
hmap(Wood, method = "MDS_angle", col = greenred(100), row_labels = FALSE,
main = "Wood (reorderd using ange in MDS space)")
# Heatmap for distances
d <- dist(Wood)
hmap(d, main = "Wood (Euclidean distances)")
# order-based with dissimilarity matrices
hmap(Wood, method = "MDS_angle",
col = greenred(100), col_dist = greens(100, power = 2),
keylab = "norm. Expression", main = "Wood (reorderd with distances)")
# without the distance matrices
hmap(Wood, method = "MDS_angle", plot_margins = "none",
col = greenred(100), main = "Wood (reorderd without distances)")
# Manually create a simple heatmap with pimage.
o <- seriate(Wood, method = "heatmap",
control = list(dist_fun = dist, seriation_method = "OLO_ward"))
o
pimage(Wood, o)
# Note: method heatmap calculates reorderd hclust objects which can be used
# for many heatmap implementations like the standard implementation in
# package stats.
heatmap(Wood, Rowv = as.dendrogram(o[[1]]), Colv = as.dendrogram(o[[2]]))
# ggplot 2 version does not support dendrograms in the margin (for now)
if (require("ggplot2")) {
library("ggplot2")
gghmap(Wood) + labs(title = "Wood", subtitle = "Optimal leaf ordering")
# More parameters (see ? ggpimage): reverse column order and flip axes, make a proportional plot
gghmap(Wood, reverse_columns = TRUE) +
labs(title = "Wood", subtitle = "Optimal leaf ordering")
gghmap(Wood, flip_axes = TRUE) +
labs(title = "Wood", subtitle = "Optimal leaf ordering")
gghmap(Wood, flip_axes = TRUE, prop = TRUE) +
labs(title = "Wood", subtitle = "Optimal leaf ordering")
dist_cor <- function(x) as.dist(sqrt(1 - cor(t(x))))
gghmap(Wood, distfun = dist_cor) +
labs(title = "Wood", subtitle = "Reorded by correlation between observations") +
scale_fill_gradient2(low = "darkgreen", high = "red")
gghmap(d, prop = TRUE) +
labs(title = "Wood", subtitle = "Euclidean distances, reordered")
# Note: the ggplot2-based version currently cannot show distance matrices
# in the same plot.
# Manually seriate and plot as pimage.
o <- seriate(Wood, method = "heatmap", control = list(dist_fun = dist,
seriation_method = "OLO_ward"))
o
ggpimage(Wood, o)
}
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