R: Plot Heat Map Reordered Using Seriation

hmap {seriation}

R Documentation

Plot Heat Map Reordered Using Seriation

Description

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 = dist, method = "OLO", control = NULL,
  scale = c("none", "row", "column"), showDend = TRUE, col = NULL,
  row_labels = NULL, col_labels = NULL, ...)

gghmap(x, distfun = dist, method = "OLO", 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 (default: `dist`). 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 `heatmap` in stats 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.
`showDend`	Show dendrograms in the margin?
`col`	a list of colors used.
`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.
`prop`	logical; change the aspect ratio so cells in the image have a equal width and height.
`...`	further arguments passed on to `heatmap` in package stats.

Details

For dendrogram based heat maps, the arguments are passed on to 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 (see gpar in package grid).
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: string 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.

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).

Author(s)

Michael Hahsler

Examples

data("Wood")

# regular heatmap from package stats
heatmap(Wood, main = "Wood (standard heatmap)")

# Default heatmap does Euclidean distance, hierarchical clustering with
# average-link and optimal leaf ordering
hmap(Wood, main = "Wood (opt. leaf ordering)")

# Heatmap shown without dendrograms (used pimage)
hmap(Wood, main = "Wood (opt. leaf ordering)",  showDend = FALSE)

# 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(1 - cor(t(x)))
hmap(Wood, distfun = dist_cor, col = greenred(100), row_labels = FALSE,
  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, method = "OLO", main = "Wood (Euclidean distances)")

# order-based with dissimilarity matrices
hmap(Wood, method = "MDS_angle", showdist = "both",
  col = greenred(100), col_dist = greens(100),
  keylab = "norm. Expression", main = "Wood (reporderd with distances)")

# Manually seriate and plot as pimage.
o <- seriate(Wood, method = "heatmap", control = list(dist_fun = dist, seriation_method = "OLO"))
o

pimage(Wood, o, prop = FALSE)

# Note: method heatmap calculates reorderd hclust objects which can be used for many heatmap
# implementations.
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")

  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 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"))
  o

  ggpimage(Wood, o, prop = FALSE)
}

[Package seriation version 1.3.2 Index]