Plot Heat Map Reordered Using Seriation

Provides heatmaps reordered using several different seriation methods. This includes dendrogram based reordering with optimal leaf order and matrix seriation-based heat maps.

Usage

hmap(
  x,
  distfun = stats::dist,
  method = "OLO_complete",
  control = NULL,
  scale = c("none", "row", "column"),
  plot_margins = "auto",
  col = NULL,
  col_dist = grays(power = 2),
  row_labels = NULL,
  col_labels = NULL,
  ...
)

gghmap(
  x,
  distfun = stats::dist,
  method = "OLO_complete",
  control = NULL,
  scale = c("none", "row", "column"),
  prop = FALSE,
  ...
)

Arguments

x: a matrix or a dissimilarity matrix of class dist. If a dissimilarity matrix is used, then the distfun is ignored.
distfun: function used to compute the distance (dissimilarity) between both rows and columns. For gghmap(), this parameter is passed on in control.
method: a character strings indicating the used seriation algorithm (see seriate.dist()). If the method results in a dendrogram then stats::heatmap() is used to show the dendrograms, otherwise reordered distance matrices are shown instead.
control: a list of control options passed on to the seriation algorithm specified in method.
scale: character indicating if the values should be centered and scaled in either the row direction or the column direction, or none. Default is none.
plot_margins: character indicating what to show in the margins. Options are: "auto", "dendrogram", "distances", or "none".
col: a list of colors used.
col_dist: colors used for displaying distances.
row_labels, col_labels: a logical indicating if row and column labels in x should be displayed. If NULL then labels are displayed if the x contains the appropriate dimname and the number of labels is 25 or less. A character vector of the appropriate length with labels can also be supplied.
...: further arguments passed on to stats::heatmap().
prop: logical; change the aspect ratio so cells in the image have a equal width and height.

Value

An invisible list with elements:

rowInd, colInd: index permutation vectors.
reorder_method: name of the method used to reorder the matrix.

The list may contain additional elements (dendrograms, colors, etc).

Details

For dendrogram based heat maps, the arguments are passed on to stats::heatmap() in stats. The following arguments for heatmap() cannot be used: margins, Rowv, Colv, hclustfun, reorderfun.

For seriation-based heat maps further arguments include:

gp an object of class gpar containing graphical parameters (see gpar() in package grid).
newpage a logical indicating whether to start plot on a new page
prop a logical indicating whether the height and width of x should be plotted proportional to its dimensions.
showdist Display seriated dissimilarity matrices? Values are "none", "both", "rows" or "columns".
key logical; show a colorkey?
key.lab Label plotted next to the color key.
margins bottom and right-hand-side margins are calculated automatically or can be specifies as a vector of two numbers (in lines).
zlim range of values displayed.
col, col_dist color palettes used.

Author

Michael Hahsler

Examples

data("Wood")

# 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 (reordered by corr. between obs.)")


# 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 (reordered with distances)")


# Manually create a simple heatmap with pimage.
o <- seriate(Wood, method = "heatmap",
   control = list(dist_fun = dist, seriation_method = "OLO_ward"))
o
#> object of class ‘ser_permutation’, ‘list’
#> contains permutation vectors for 2-mode data
#> 
#>   vector length seriation method
#> 1           136          Heatmap
#> 2             6          Heatmap

pimage(Wood, o)


# Note: method heatmap calculates reordered 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")

  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 = "Reordered 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)
}
#> Scale for fill is already present.
#> Adding another scale for fill, which will replace the existing scale.