Create a heatmap with tracks and dendrograms from any matrix.
Source:R/plot_heatmap.r
plot_heatmap.Rd
Create a heatmap with tracks and dendrograms from any matrix.
Usage
plot_heatmap(
mtx,
grid = list(label = "Grid Value", colors = "imola"),
tracks = NULL,
label = TRUE,
label_size = NULL,
rescale = "none",
trees = TRUE,
clust = "complete",
dist = "euclidean",
asp = 1,
tree_height = 10,
track_height = 10,
legend = "right",
title = NULL,
xlab.angle = "auto",
...
)
Arguments
- mtx
A numeric
matrix
with named rows and columns.- grid
Color palette name, or a list with entries for
label
,colors
,range
,bins
,na.color
, and/orguide
. See the Track Definitions section for details. Default:list(label = "Grid Value", colors = "imola")
- tracks
List of track definitions. See details below. Default:
NULL
.- label
Label the matrix rows and columns. You can supply a list or logical vector of length two to control row labels and column labels separately, for example
label = c(rows = TRUE, cols = FALSE)
, or simplylabel = c(TRUE, FALSE)
. Other valid options are"rows"
,"cols"
,"both"
,"bottom"
,"right"
, and"none"
. Default:TRUE
- label_size
The font size to use for the row and column labels. You can supply a numeric vector of length two to control row label sizes and column label sizes separately, for example
c(rows = 20, cols = 8)
, or simplyc(20, 8)
. Default:NULL
, which computes:pmax(8, pmin(20, 100 / dim(mtx)))
- rescale
Rescale rows or columns to all have a common min/max. Options:
"none"
,"rows"
, or"cols"
. Default:"none"
- trees
Draw a dendrogram for rows (left) and columns (top). You can supply a list or logical vector of length two to control the row tree and column tree separately, for example
trees = c(rows = TRUE, cols = FALSE)
, or simplytrees = c(TRUE, FALSE)
. Other valid options are"rows"
,"cols"
,"both"
,"left"
,"top"
, and"none"
. Default:TRUE
- clust
Clustering algorithm for reordering the rows and columns by similarity. You can supply a list or character vector of length two to control the row and column clustering separately, for example
clust = c(rows = "complete", cols = NA)
, or simplyclust = c("complete", NA)
. Options are:FALSE
orNA
-Disable reordering.
- An
hclust
class object E.g. from
stats::hclust()
.- A method name -
"ward.D"
,"ward.D2"
,"single"
,"complete"
,"average"
,"mcquitty"
,"median"
, or"centroid"
.
Default:
"complete"
- dist
Distance algorithm to use when reordering the rows and columns by similarity. You can supply a list or character vector of length two to control the row and column clustering separately, for example
dist = c(rows = "euclidean", cols = "maximum")
, or simplydist = c("euclidean", "maximum")
. Options are:- A
dist
class object E.g. from
stats::dist()
orbdiv_distmat()
.- A method name -
"euclidean"
,"maximum"
,"manhattan"
,"canberra"
,"binary"
, or"minkowski"
.
Default:
"euclidean"
- A
- asp
Aspect ratio (height/width) for entire grid. Default:
1
(square)- tree_height, track_height
The height of the dendrogram or annotation tracks as a percentage of the overall grid size. Use a numeric vector of length two to assign
c(top, left)
independently. Default:10
(10% of the grid's height)- legend
Where to place the legend. Options are:
"right"
or"bottom"
. Default:"right"
- title
Plot title. Default:
NULL
.- xlab.angle
Angle of the labels at the bottom of the plot. Options are
"auto"
,'0'
,'30'
, and'90'
. Default:"auto"
.- ...
Additional arguments to pass on to ggplot2::theme().
Value
A ggplot2
plot.
The computed data points and ggplot
command are available as $data
and $code
,
respectively.
Track Definitions
One or more colored tracks can be placed on the left and/or top of the heatmap grid to visualize associated metadata values.
## Categorical ----------------------------
cat_vals <- sample(c("Male", "Female"), 10, replace = TRUE)
tracks <- list('Sex' = cat_vals)
tracks <- list('Sex' = list(values = cat_vals, colors = "bright"))
tracks <- list('Sex' = list(
values = cat_vals,
colors = c('Male' = "blue", 'Female' = "red")) )
## Numeric --------------------------------
num_vals <- sample(25:40, 10, replace = TRUE)
tracks <- list('Age' = num_vals)
tracks <- list('Age' = list(values = num_vals, colors = "greens"))
tracks <- list('Age' = list(values = num_vals, range = c(0,50)))
tracks <- list('Age' = list(
label = "Age (Years)",
values = num_vals,
colors = c("azure", "darkblue", "darkorchid") ))
## Multiple Tracks ------------------------
tracks <- list('Sex' = cat_vals, 'Age' = num_vals)
tracks <- list(
list(label = "Sex", values = cat_vals, colors = "bright"),
list(label = "Age", values = num_vals, colors = "greens") )
mtx <- matrix(sample(1:50), ncol = 10)
dimnames(mtx) <- list(letters[1:5], LETTERS[1:10])
plot_heatmap(mtx = mtx, tracks = tracks)
The following entries in the track definitions are understood:
values
-The metadata values. When unnamed, order must match matrix.
range
-The c(min,max) to use for scale values.
label
-Label for this track. Defaults to the name of this list element.
side
-Options are
"top"
(default) or"left"
.colors
-A pre-defined palette name or custom set of colors to map to.
na.color
-The color to use for
NA
values.bins
-Bin a gradient into this many bins/steps.
guide
-A list of arguments for guide_colorbar() or guide_legend().
All built-in color palettes are colorblind-friendly. See Mapping Metadata to Aesthetics for images of the palettes.
Categorical palette names: "okabe"
, "carto"
, "r4"
,
"polychrome"
, "tol"
, "bright"
, "light"
,
"muted"
, "vibrant"
, "tableau"
, "classic"
,
"alphabet"
, "tableau20"
, "kelly"
, and "fishy"
.
Numeric palette names: "reds"
, "oranges"
, "greens"
,
"purples"
, "grays"
, "acton"
, "bamako"
,
"batlow"
, "bilbao"
, "buda"
, "davos"
,
"devon"
, "grayC"
, "hawaii"
, "imola"
,
"lajolla"
, "lapaz"
, "nuuk"
, "oslo"
,
"tokyo"
, "turku"
, "bam"
, "berlin"
,
"broc"
, "cork"
, "lisbon"
, "roma"
,
"tofino"
, "vanimo"
, and "vik"
.
See also
Other visualization:
adiv_boxplot()
,
adiv_corrplot()
,
bdiv_boxplot()
,
bdiv_corrplot()
,
bdiv_heatmap()
,
bdiv_ord_plot()
,
rare_corrplot()
,
rare_multiplot()
,
rare_stacked()
,
stats_boxplot()
,
stats_corrplot()
,
taxa_boxplot()
,
taxa_corrplot()
,
taxa_heatmap()
,
taxa_stacked()
Examples
library(rbiom)
set.seed(123)
mtx <- matrix(runif(5*8), nrow = 5, dimnames = list(LETTERS[1:5], letters[1:8]))
plot_heatmap(mtx)
plot_heatmap(mtx, grid="oranges")
plot_heatmap(mtx, grid=list(colors = "oranges", label = "Some %", bins = 5))
tracks <- list(
'Number' = sample(1:ncol(mtx)),
'Person' = list(
values = factor(sample(c("Alice", "Bob"), ncol(mtx), TRUE)),
colors = c('Alice' = "purple", 'Bob' = "darkcyan") ),
'State' = list(
side = "left",
values = sample(c("TX", "OR", "WA"), nrow(mtx), TRUE),
colors = "bright" )
)
plot_heatmap(mtx, tracks=tracks)