| create.heatmap {BoutrosLab.plotting.general} | R Documentation |
Make a heatmap
Description
Takes a data.frame and creates a heatmap
Usage
create.heatmap(
x,
filename = NULL,
clustering.method = 'diana',
cluster.dimensions = 'both',
rows.distance.method = 'correlation',
cols.distance.method = 'correlation',
cor.method = 'pearson',
row.dendrogram = list(),
col.dendrogram = list(),
plot.dendrograms = 'both',
force.clustering = FALSE,
criteria.list = TRUE,
covariates = list(),
covariates.grid.row = NULL,
covariates.grid.col = NULL,
covariates.grid.border = NULL,
covariates.row.lines = NULL,
covariates.col.lines = NULL,
covariates.reorder.grid.index = FALSE,
covariates.padding = 0.25,
covariates.top = list(),
covariates.top.grid.row = NULL,
covariates.top.grid.col = NULL,
covariates.top.grid.border = NULL,
covariates.top.row.lines = NULL,
covariates.top.col.lines = NULL,
covariates.top.reorder.grid.index = FALSE,
covariates.top.padding = 0.25,
covariate.legends = list(),
legend.cex = 1,
legend.title.cex = 1,
legend.title.just = 'centre',
legend.title.fontface = 'bold',
legend.border = NULL,
legend.border.padding = 1,
legend.layout = NULL,
legend.between.col = 1,
legend.between.row = 1,
legend.side = 'left',
main = list(label = ''),
main.just = "center",
main.x = 0.5,
main.y = 0.5,
main.cex = 3,
right.size.add = 1,
top.size.add = 1,
right.dendrogram.size = 2.5,
top.dendrogram.size = 2.5,
scale.data = FALSE,
yaxis.lab = NULL,
xaxis.lab = NULL,
xaxis.lab.top = NULL,
xaxis.cex = 1.5,
xaxis.top.cex = NULL,
yaxis.cex = 1.5,
xlab.cex = 2,
ylab.cex = 2,
xlab.top.label = NULL,
xlab.top.cex = 2,
xlab.top.col = 'black',
xlab.top.just = "center",
xlab.top.x = 0.5,
xlab.top.y = 0,
xat = TRUE,
xat.top = NULL,
yat = TRUE,
xaxis.tck = NULL,
xaxis.top.tck = NULL,
yaxis.tck = NULL,
xaxis.col = 'black',
yaxis.col = 'black',
col.pos = NULL,
row.pos = NULL,
cell.text = '',
text.fontface = 1,
text.cex = 1,
text.col = 'black',
text.position = NULL,
text.offset = 0,
text.use.grid.coordinates = TRUE,
colourkey.cex = 3.6,
xaxis.rot = 90,
xaxis.rot.top = 90,
yaxis.rot = 0,
xlab.label = '' ,
ylab.label = '',
xlab.col = 'black',
ylab.col = 'black',
axes.lwd = 2,
gridline.order = 'h',
grid.row = FALSE,
grid.col = FALSE,
force.grid.row = FALSE,
force.grid.col = FALSE,
grid.limit = 50,
row.lines = seq(0, ncol(x), 1) + 0.5,
col.lines = seq(0, nrow(x), 1) + 0.5,
colour.scheme = c(),
total.colours = 99,
colour.centering.value = 0,
colour.alpha = 1,
fill.colour = 'darkgray',
at = NULL,
print.colour.key = TRUE,
colourkey.labels.at = NULL,
colourkey.labels = NULL,
top.padding = 0.1,
bottom.padding = 0.5,
right.padding = 0.5,
left.padding = 0.5,
x.alternating = 1,
shrink = 1,
row.colour = 'black',
col.colour = 'black',
row.lwd = 1,
col.lwd = 1,
grid.colour = NULL,
grid.lwd = NULL,
width = 6,
height = 6,
size.units = 'in',
resolution = 1600,
enable.warnings = FALSE,
xaxis.covariates = NULL,
xaxis.covariates.y = 0,
yaxis.covariates = NULL,
yaxis.covariates.x = NULL,
description = 'Created with BoutrosLab.plotting.general',
xaxis.fontface = 'bold',
yaxis.fontface = 'bold',
symbols = list(borders = NULL,
squares = NULL,
circles = NULL),
same.as.matrix = FALSE,
input.colours = FALSE,
axis.xlab.padding = 0.1,
stratified.clusters.rows = NULL,
stratified.clusters.cols = NULL,
inside.legend = NULL,
style = 'BoutrosLab',
preload.default = 'custom',
use.legacy.settings = FALSE
);
Arguments
x |
Either a data-frame or a matrix from which the heatmap is to created |
filename |
Filename for tiff output, or if NULL returns the trellis object itself |
clustering.method |
Method used to cluster the records – “none” gives unclustered data. Accepts all agglomerative clustering methods available in hclust, plus “diana” (which is divisive). |
cluster.dimensions |
Should clustering be performed on rows, columns, or both – supersedes setting of plot.dendrograms |
rows.distance.method |
Method name of the distance measure between rows to be used for clustering. Defaults to “correlation”. Other supported methods are same as in ?dist. Also supports “jaccard” which is useful for clustering categorical variables. “euclidean” is sometimes more robust when ties cause “Unclusterable matrix: some col-distances are null” errors. Note, rows and cols are switched due an internal transposition of the data. |
cols.distance.method |
Method name of the distance measure between columns to be used for clustering. Defaults to “correlation”. Other supported methods are same as in ?dist. Also supports “jaccard” which is useful for clustering categorical variables. “euclidean” is sometimes more robust when ties cause “Unclusterable matrix: some col-distances are null” errors. Note, rows and cols are switched due an internal transposition of the data. |
cor.method |
The method used for calculating correlation. Defaults to “pearson” |
row.dendrogram |
A dendrogram object corresponding to the heatmap rows. If provided, row clustering cannot be performed |
col.dendrogram |
A dendrogram object corresponding to the heatmap columns. If provided, column clustering cannot be performed |
plot.dendrograms |
If clustering is performed or dendrograms are provided, which dendrograms should be plotted – “none”, “right”, “top”, or “both” |
force.clustering |
Binary to over-ride the control that prevents clustering of too-large matrices |
criteria.list |
A vector indicating which rows should be retained |
covariates |
Any row-wise covariate annotate to add to the plot, as a fully formed list (placed on right side of plot) |
covariates.grid.row |
A list of parameters passed to |
covariates.grid.col |
A list of parameters passed to |
covariates.grid.border |
A list of parameters passed to |
covariates.row.lines |
Vector of row indices where grid lines should be drawn on the right covariate bars. If NULL (default), all row lines are drawn. Ignored if |
covariates.col.lines |
Vector of column indices where grid lines should be drawn on the right covariate bars. If NULL (default), all column lines are drawn. Ignored if |
covariates.reorder.grid.index |
Boolean specifying whether grid line indices for the right covariate bars should be re-ordered with clustering |
covariates.padding |
Amount of empty space (in “lines”) to place between the right covariate bars and dendrogram |
covariates.top |
Any column-wise covariate annotate to add to the plot, as a fully formed list |
covariates.top.grid.row |
A list of parameters passed to |
covariates.top.grid.col |
A list of parameters passed to |
covariates.top.grid.border |
A list of parameters passed to |
covariates.top.row.lines |
Vector of row indices where grid lines should be drawn on the top covariate bars. If NULL (default), all row lines are drawn. Ignored if |
covariates.top.col.lines |
Vector of column indices where grid lines should be drawn on the top covariate bars. If NULL (default), all column lines are drawn. Ignored if |
covariates.top.reorder.grid.index |
Boolean specifying whether grid line indices for the top covariate bars should be re-ordered with clustering |
covariates.top.padding |
Amount of empty space (in “lines”) to place between the top covariate bars and dendrogram |
covariate.legends |
A list defining covariate legends to add to the plot. See |
legend.cex |
Size of text labels in covariate legends, defaults to 1 |
legend.title.cex |
Size of title text in covariate legends, defaults to 1 |
legend.title.just |
Justification of title text in covariate legends, defaults to “centre” |
legend.title.fontface |
Font face of title text in covariate legends – “plain”, “bold”, “italic”, etc. |
legend.border |
A list of parameters passed to |
legend.border.padding |
The amount of empty space (split equally on both sides) to add between the legend and its border, in “lines” units |
legend.layout |
Numeric vector of length 2 specifying the number of columns and rows for the legend layout, defaults to a logical layout based on |
legend.between.col |
Amount of space to add between columns in the layout, in “lines” units |
legend.between.row |
Amount of space to add between rows in the layout, in “lines” units |
legend.side |
Side of the plot where the legends should be drawn – “left”, “right”, or “top” |
main |
The main title for the plot (space is reclaimed if NULL) |
main.just |
The justification of the main title for the plot, default is centered |
main.x |
The x location of the main title, deault is 0.5 |
main.y |
The y location of the main title, default is 0.5 |
main.cex |
Size of text for main plot title, defaults to 2.5 |
right.size.add |
The size of each extra covariate row in the right dendrogram in units of “lines” |
top.size.add |
The size of each extra covariate row in the top dendrogram in units of “lines” |
right.dendrogram.size |
Size of right dendrogram |
top.dendrogram.size |
Size of top dendrogram |
scale.data |
TRUE/FALSE to do row-wise scaling with mean-centering and sd-scaling |
xaxis.lab |
A vector of row labels, NA = use existing rownames, NULL = none |
xaxis.lab.top |
The label for the top x-axis. Required only if you want to print a top *and* bottom xaxis, otherwise use x.alternating = 2 for top axis only. Defaults to NULL |
yaxis.lab |
A vector of col labels, NA = use existing colnames, NULL = none |
xaxis.cex |
Size of x-axis label text - defaults to values found in a look-up table |
xaxis.top.cex |
Size of top x axis label text |
yaxis.cex |
Size of y-axis label text - defaults to values found in a look-up table |
xaxis.rot |
Rotation of x-axis tick labels; defaults to 90 |
xaxis.rot.top |
Rotation of the top x-axis tick labels; defaults to 90 |
yaxis.rot |
Rotation of y-axis tick labels; defaults to 0 |
xaxis.col |
Colour of the x-axis tick labels, defaults to “black” |
yaxis.col |
Colour of the y-axis tick labels, defaults to “black” |
xlab.label |
The label for the x-axis |
ylab.label |
The label for the y-axis |
xlab.cex |
Size of x-axis label, defaults to 2 |
ylab.cex |
Size of y-axis label, defaults to 2 |
xlab.col |
Colour of the x-axis label, defaults to “black” |
ylab.col |
Colour of the y-axis label, defaults to “black” |
xlab.top.label |
The label for the top x-axis |
xlab.top.cex |
Size of top x-axis label |
xlab.top.col |
Colour of the top x-axis label |
xlab.top.just |
Justification of the top x-axis label, defaults to centered |
xlab.top.x |
The x location of the top x-axis label |
xlab.top.y |
The y location of the top y-axis label |
xat |
Vector listing where the x-axis labels should be drawn, defaults to automatic |
xat.top |
Vector listing where the x-axis labels should be drawn on the top of the plot. Required only when you want bottom and top axis, otherwise use x.alternating = 2, to get top axis only. Defaults to NULL |
yat |
Vector listing where the y-axis labels should be drawn, defaults to automatic |
xaxis.tck |
Size of x-axis tick marks. Defaults to NULL for intelligent choice based on covariate size. |
xaxis.top.tck |
Size of top x-axis tick marks. Defaults to NULL for intelligent choice based on covariate size. |
yaxis.tck |
Size of y-axis tick marks. Defaults to NULL for intelligent choice based on covariate size. |
col.pos |
Vector of column positions for adding text to cell, defaults to NULL |
row.pos |
Vector of row positions for adding text to cell, defaults to NULL |
cell.text |
Text to add to cell, defaults to an empty string |
text.fontface |
1 = Plain, 2 = Bold, 3 = Italic, default is 1 |
text.cex |
Text size, default is 1 |
text.col |
Text colour, default is black. |
text.position |
The position of the text, defaults to center. |
text.offset |
The offset of the position, defaults to 0. |
text.use.grid.coordinates |
Indetifier if grid coordinates or npc coordinates should be used |
colourkey.cex |
Size of colourkey label text |
axes.lwd |
Width of heatmap border. Note it also changes the colourkey border and ticks |
gridline.order |
Character specifying order in which to draw interior grid-lines ('h' or 'v'). Defaults to 'h' for horizontal first. |
grid.row |
Allow turning off of the interior grid-lines. Default FALSE |
grid.col |
Allow turning off of the interior grid-lines. Default FALSE |
force.grid.row |
Overrides default behaviour of turning off grid lines when number of rows exceed grid.limit. Defaults to FALSE |
force.grid.col |
Overrides default behaviour of turning off grid lines when number of columns exceed grid.limit. Defaults to FALSE |
grid.limit |
Limit set for when to turn off column and row lines if data size exceeds it. Defaults to 50 |
row.lines |
Vector specifying location of lines, default is seq(1, ncol(x), 1) + 0.5. Note: Add 0.5 to customized vector |
col.lines |
Vector specifying location of lines, default is seq(1, nrow(x), 1) + 0.5. Note: Add 0.5 to customized vector |
colour.scheme |
Heatmap colouring. Accepts old-style themes, or a vector of either two or three colours that are gradiated to create the final palette. |
total.colours |
Total number of colours to plot |
colour.centering.value |
What should be the center of the colour-map |
colour.alpha |
Bias to be added to colour selection (uses x^colour.alpha in mapping). Set to “automatic” for auto-adjustment. |
fill.colour |
The background fill (only exposed where missing values are present |
print.colour.key |
Should the colour key be printed at all? |
at |
A vector specifying the breakpoints along the range of x; each interval specified by these breakpoints are assigned to a colour from the palette. Defaults to NULL, which corresponds to the range of x being divided into total.colours equally spaced intervals. If x has values outside of the range specified by “at” those values are shown with the colours corresponding to the extreme ends of the colour spectrum and a warning is given. |
colourkey.labels.at |
A vector specifying the tick-positions on the colourkey |
colourkey.labels |
A vector specifying tick-labels of the colourkey |
top.padding |
A number specifying the distance to the top margin, defaults to 0.1 |
bottom.padding |
A number specifying the distance to the bottom margin, defaults to 0.5 |
right.padding |
A number specifying the distance to the right margin, defaults to 0.5 |
left.padding |
A number specifying the distance to the left margin, defaults to 0.5 |
x.alternating |
A value specifying the position of the col names, defaults to 1. 1 means below the graph, 2 means above the graph. Use 3 to get tick marks below and above graph, but still need to specify xat.top and xaxis.lab.top to get values there |
shrink |
Allows rectangles to be scaled, defaults to 1 |
row.colour |
Interior grid-line colour, defaults to “black”. Can be a vector |
col.colour |
Interior grid-line colour, defaults to “black”. Can be a vector |
row.lwd |
Interior grid-line width, defaults to 1. Setting to zero is equivalent to grid.row = FALSE and grid.col = FALSE. Can be a vector. |
col.lwd |
Interior grid-line width, defaults to 1. Setting to zero is equivalent to grid.row = FALSE and grid.col = FALSE. Can be a vector. |
grid.colour |
Interior grid-line colour, defaults to “black”. Can be a vector. Applies to both rows and columns. DEPRECATED |
grid.lwd |
Interior grid-line width, defaults to 1. Setting to zero is equivalent to grid.row = FALSE and grid.col = FALSE. Applies to both rows and columns. DEPRECATED |
width |
Figure width in size.units |
height |
Figure height in size.units |
size.units |
Units of size for the figure |
resolution |
Figure resolution in dpi |
enable.warnings |
Print warnings if set to TRUE, defaults to FALSE |
xaxis.covariates |
Any column-wise covariate annotate to add to the plot, as a fully formed list |
xaxis.covariates.y |
The y coordinate of the location of the x axis covariates |
yaxis.covariates |
Any row-wise covariate annotate to add to the plot, as a fully formed list |
yaxis.covariates.x |
The x coordinate of the lcoation of the y axis covariates |
description |
Short description of image/plot; default NULL. |
xaxis.fontface |
Fontface for the x-axis scales |
yaxis.fontface |
Fontface for the y-axis scales |
symbols |
Extra symbols to be added (borders, squares and circles) |
same.as.matrix |
Prevents the flipping of the matrix that the function normally does |
input.colours |
boolean expressing whether or not the matrix was specified using colours or integer values. Defaults to FALSE |
axis.xlab.padding |
Padding between axis of plot and x label |
stratified.clusters.rows |
the row locations of the rows to be combined into a strata |
stratified.clusters.cols |
the column locations of the columns to be combined into a strata |
inside.legend |
legend specification for the inside legend/key of the heatmap |
style |
defaults to “BoutrosLab”, also accepts “Nature”, which changes parameters according to Nature formatting requirements |
preload.default |
ability to set multiple sets of diffrent defaults depending on publication needs |
use.legacy.settings |
boolean to set wheter or not to use legacy mode settings (font) |
Value
If filename is NULL then returns the trellis object, otherwise creates a plot and returns a 0/1 success code.
Warning
If this function is called without capturing the return value, or specifying a filename, it may crash while trying to draw the heatmap. In particular, if a script that uses such a call of create heatmap is called by reading the script in from the command line, it will fail badly, with an error message about unavailable fonts:
Error in grid.Call.graphics("L_text", as.graphicsAnnot(x$label), x$x, )
Invalid font type
Calls: print ... drawDetails.text -> grid.Call.graphics -> .Call.graphics
Note that we would very much like to be able to pass xaxis.cex and yaxis.cex as vectors of the same length as the actual data-table. However lattice does not support that, because it currently expects them as a two-element vectors to specify left/right or top/bottom axes separately. I've raised a bug report on requesting an enhancement, but this would require an API change so... not sure if it will happen. Here's the bug-report:
https://r-forge.r-project.org/tracker/index.php?func=detail&aid=1702&group_id=638&atid=2567
Author(s)
Paul C. Boutros
See Also
covariates.grob, create.dendrogram, legend.grob
Examples
set.seed(12345);
simple.data <- data.frame(
x <- rnorm(n = 15),
y <- rnorm(n = 15),
z <- rnorm(n = 15),
v <- rnorm(n = 15),
w <- rnorm(n = 15)
);
simple.1D.data <- data.frame(x = rnorm(n = 15));
create.heatmap(
# filename = tempfile(pattern = 'Heatmap_1D_Inside_Legend', fileext = '.tiff'),
x = simple.1D.data,
clustering.method='none',
inside.legend = list(fun = draw.key,
args = list(
key = list(
text = list(
lab = c('test','test','test','test'),
cex = 1,
fontface = 'bold'
),
padding.text = 3,
background = 'white',
alpha.background = 0
)
),
x = 0.5,
y = 0.5
),
resolution = 100
)
create.heatmap(
# filename = tempfile(pattern = 'Heatmap_Simple', fileext = '.tiff'),
x = simple.data,
main = 'Simple',
description = 'Heatmap created using BoutrosLab.plotting.general',
resolution = 100
);
simple.data.col <- data.frame(
x <- c('blue','green','red','yellow','blue','red','black','white','purple','grey'),
y <- rep('red',10),
z <- rep('yellow',10),
v <- rep('green',10),
w <- rep('purple',10)
);
# Input Colours Provided
create.heatmap(
# filename = tempfile(pattern = 'Heatmap_Simple_Using_Colours', fileext = '.tiff'),
x = simple.data.col,
clustering.method = 'none',
input.colours = TRUE,
resolution = 100
);
# Single Input Colour Provided
create.heatmap(
# filename = tempfile(pattern = 'Heatmap_Simple_Using_Single_Colour', fileext = '.tiff'),
x = simple.data.col[, ncol(simple.data.col), drop = FALSE],
clustering.method = 'none',
input.colours = TRUE,
resolution = 100
);
# Minimal Input
create.heatmap(
# filename = tempfile(pattern = 'Heatmap_Minimal_Input', fileext = '.tiff'),
x = microarray[1:20, 1:20],
main = 'Minimal input',
description = 'Heatmap created using BoutrosLab.plotting.general',
resolution = 100
);
# Axes and labels
create.heatmap(
# filename = tempfile(pattern = 'Heatmap_Axes_Labels', fileext = '.tiff'),
x = microarray[1:20, 1:20],
main = 'Axes & labels',
# Changing axes
xlab.label = 'Genes',
ylab.label = 'Samples',
# Turning on default row and column labels
xaxis.lab = NA,
yaxis.lab = 1:20,
# Adjusting font sizes
xaxis.cex = 0.75,
yaxis.cex = 0.75,
xaxis.fontface = 1,
yaxis.fontface = 1,
# Changing colourkey
colourkey.cex = 1,
colourkey.labels.at = seq(2,12,1),
description = 'Heatmap created using BoutrosLab.plotting.general',
resolution = 100
);
# Custom Axes
create.heatmap(
# filename = tempfile(pattern = 'Heatmap_Custom_Axes', fileext = '.tiff'),
x = microarray[1:20, 1:20],
main = 'Customized axes',
xlab.label = 'Genes',
ylab.label = 'Samples',
xaxis.lab = NA,
yaxis.lab = 1:20,
xaxis.cex = 0.75,
yaxis.cex = 0.75,
xaxis.fontface = 1,
yaxis.fontface = 1,
colourkey.cex = 1,
# Specify where to place tick marks
colourkey.labels.at = c(3,4, 6, 7, 10, 11),
# Specify label colours (note: this is based on the pre-clustering order)
xaxis.col = c('black', 'red',rep('black',6), 'red','black', 'black','red',rep('black',8)),
description = 'Heatmap created using BoutrosLab.plotting.general',
resolution = 200
);
# Two-sided Colour Scheme
create.heatmap(
# filename = tempfile(pattern = 'Heatmap_Colour_Scheme_1', fileext = '.tiff'),
x = microarray[1:20, 1:20],
main = 'Colour scheme',
xlab.label = 'Genes',
ylab.label = 'Samples',
xaxis.lab = NA,
yaxis.lab = 1:20,
xaxis.cex = 0.75,
yaxis.cex = 0.75,
xaxis.fontface = 1,
yaxis.fontface = 1,
colourkey.cex = 1,
colourkey.labels.at = seq(2,12,1),
# Changing the colours
colour.scheme = c('white','firebrick'),
description = 'Heatmap created using BoutrosLab.plotting.general',
resolution = 200
);
# Three-sided Colour Scheme
# Note: when using a three-sided colour scheme, it is advised to have two-sided data
create.heatmap(
# filename = tempfile(pattern = 'Heatmap_Colour_Scheme_2', fileext = '.tiff'),
x = microarray[1:20, 1:20],
main = 'Colour scheme',
xlab.label = 'Genes',
ylab.label = 'Samples',
xaxis.lab = NA,
yaxis.lab = 1:20,
xaxis.cex = 0.75,
yaxis.cex = 0.75,
xaxis.fontface = 1,
yaxis.fontface = 1,
colourkey.cex = 1,
# Changing the colours
colour.scheme = c('red','white','turquoise'),
# Scale the data to center around the mean
scale.data = TRUE,
description = 'Heatmap created using BoutrosLab.plotting.general',
resolution = 200
);
# Colour Alpha
create.heatmap(
# filename = tempfile(pattern = 'Heatmap_Colour_Alpha', fileext = '.tiff'),
x = microarray[1:20, 1:20],
main = 'Colours alpha',
xlab.label = 'Genes',
ylab.label = 'Samples',
xaxis.lab = NA,
yaxis.lab = 1:20,
xaxis.cex = 0.75,
yaxis.cex = 0.75,
xaxis.fontface = 1,
yaxis.fontface = 1,
colourkey.cex = 1,
colourkey.labels.at = seq(2,12,1),
# Adjusting the alpha value of the colours
colour.alpha = 'automatic',
description = 'Heatmap created using BoutrosLab.plotting.general',
resolution = 200
);
# Clustering
create.heatmap(
# filename = tempfile(pattern = 'Heatmap_No_Clustering', fileext = '.tiff'),
x = microarray[1:20, 1:20],
main = 'No clustering',
xlab.label = 'Genes',
ylab.label = 'Samples',
xaxis.lab = NA,
yaxis.lab = 1:20,
xaxis.cex = 0.75,
yaxis.cex = 0.75,
xaxis.fontface = 1,
yaxis.fontface = 1,
colourkey.cex = 1,
colourkey.labels.at = seq(2,12,1),
colour.alpha = 'automatic',
# Turning clustering off
clustering.method = 'none',
description = 'Heatmap created using BoutrosLab.plotting.general',
resolution = 200
);
# Clustering
create.heatmap(
# filename = tempfile(pattern = 'Heatmap_Clustering_Methods', fileext = '.tiff'),
x = microarray[1:20, 1:20],
main = 'Clustering methods',
xlab.label = 'Genes',
ylab.label = 'Samples',
xaxis.lab = NA,
yaxis.lab = 1:20,
xaxis.cex = 0.75,
yaxis.cex = 0.75,
xaxis.fontface = 1,
yaxis.fontface = 1,
colourkey.cex = 1,
colourkey.labels.at = seq(2,12,1),
colour.alpha = 'automatic',
# Clustering method defaults to 'diana', but can be set to other options
clustering.method = 'complete',
# Also setting the distance measures
rows.distance.method = 'euclidean',
cols.distance.method = 'manhattan',
description = 'Heatmap created using BoutrosLab.plotting.general',
resolution = 200
);
# Stratified Clustering
create.heatmap(
# filename = tempfile(pattern = 'Heatmap_Stratified_Clustering', fileext = '.tiff'),
x = microarray[1:20, 1:20],
main = 'Stratified clustering',
xlab.label = 'Genes',
ylab.label = 'Samples',
xaxis.lab = NA,
yaxis.lab = 1:20,,
xaxis.cex = 0.75,
yaxis.cex = 0.75,
xaxis.fontface = 1,
yaxis.fontface = 1,
colourkey.cex = 1,
colourkey.labels.at = seq(2,12,1),
colour.alpha = 'automatic',
# Stratifying the clustering by rows
stratified.clusters.rows = list(c(1:10), c(11:20)),
# Adding line to show highlight the division between the two strata
grid.row = TRUE,
row.lines = 10.5,
row.lwd = 2,
description = 'Heatmap created using BoutrosLab.plotting.general',
resolution = 200
);
# Dendrogram provided
col.dendrogram <- BoutrosLab.plotting.general::create.dendrogram(
x = microarray[1:20, 1:20],
cluster.dimension = 'col'
);
row.dendrogram <- BoutrosLab.plotting.general::create.dendrogram(
x = microarray[1:20, 1:20],
cluster.dimension = 'row'
);
create.heatmap(
# filename = tempfile(pattern = 'Heatmap_Dendrogram_Provided', fileext = '.tiff'),
x = microarray[1:20, 1:20],
main = 'Dendrogram provided',
xlab.label = 'Genes',
ylab.label = 'Samples',
xaxis.lab = NA,
yaxis.lab = 1:20,
xaxis.cex = 0.75,
yaxis.cex = 0.75,
xaxis.fontface = 1,
yaxis.fontface = 1,
colourkey.cex = 1,
colourkey.labels.at = seq(2,12,1),
colour.alpha = 'automatic',
# note: row/column dendrograms are switched because the function inverts rows and columns
clustering.method = 'none',
row.dendrogram = col.dendrogram,
col.dendrogram = row.dendrogram,
# Adjusting the size of the dendrogram
right.dendrogram.size = 3,
top.dendrogram.size = 2.5,
description = 'Heatmap created using BoutrosLab.plotting.general',
resolution = 200
);
# Covariates and Legends
# Note: covariates can also be added using the create.multiplot function
# set the colour schemes for the covariates
sex.colours <- patient$sex;
sex.colours[sex.colours == 'male'] <- 'dodgerblue';
sex.colours[sex.colours == 'female'] <- 'pink';
stage.colours <- patient$stage;
stage.colours[stage.colours == 'I'] <- 'plum1';
stage.colours[stage.colours == 'II'] <- 'orchid1';
stage.colours[stage.colours == 'III'] <- 'orchid3';
stage.colours[stage.colours == 'IV'] <- 'orchid4';
# create an object to draw the covariates from
sample.covariate <- list(
rect = list(
col = 'black',
fill = sex.colours,
lwd = 1.5
),
rect = list(
col = 'black',
fill = stage.colours,
lwd = 1.5
)
);
# create a legend for the covariates
sample.cov.legend <- list(
legend = list(
colours = c('dodgerblue', 'pink'),
labels = c('male','female'),
title = 'Sex'
),
legend = list(
colours = c('plum1', 'orchid1','orchid3', 'orchid4'),
labels = c('I','II', 'III', 'IV'),
title = 'Stage'
)
);
create.heatmap(
# filename = tempfile(pattern = 'Heatmap_Covariates_Simple', fileext = '.tiff'),
x = microarray[1:20, 1:20],
main = 'Covariates',
xlab.label = 'Genes',
ylab.label = 'Samples',
xaxis.lab = NA,
yaxis.lab = 1:20,
xaxis.cex = 0.75,
yaxis.cex = 0.75,
xaxis.fontface = 1,
yaxis.fontface = 1,
colourkey.cex = 1,
colourkey.labels.at = seq(2,12,1),
colour.alpha = 'automatic',
# adding covariates and corresponding legend
covariates = sample.covariate,
covariate.legend = sample.cov.legend,
description = 'Heatmap created using BoutrosLab.plotting.general',
resolution = 200
);
# Top covariate and legend customization
chr.cov.colours <- microarray$Chr;
chr.cov.colours[microarray$Chr == 1] <- default.colours(3, palette.type = 'chromosomes')[1];
chr.cov.colours[microarray$Chr == 2] <- default.colours(3, palette.type = 'chromosomes')[2];
chr.cov.colours[microarray$Chr == 3] <- default.colours(3, palette.type = 'chromosomes')[3];
chr.covariate <- list(
rect = list(
col = 'white',
fill = chr.cov.colours,
lwd = 1.5
)
);
# join covariate legends
combo.cov.legend <- list(
legend = list(
colours = default.colours(3, palette.type = 'chromosomes'),
labels = c('1','2', '3'),
title = 'Chromosome',
border = 'white'
),
legend = list(
colours = c('dodgerblue', 'pink'),
labels = c('male','female'),
title = 'Sex'
),
legend = list(
colours = c('plum1', 'orchid1','orchid3', 'orchid4'),
labels = c('I','II', 'III', 'IV'),
title = 'Stage'
)
);
create.heatmap(
# filename = tempfile(pattern = 'Heatmap_Covariate_Legend_Custom', fileext = '.tiff'),
x = microarray[1:20, 1:20],
main = 'Custom covariates & legend',
xlab.label = 'Genes',
ylab.label = 'Samples',
xaxis.lab = NA,
yaxis.lab = 1:20,
xaxis.cex = 0.75,
yaxis.cex = 0.75,
xaxis.fontface = 1,
yaxis.fontface = 1,
colourkey.cex = 1,
colourkey.labels.at = seq(2,12,1),
colour.alpha = 'automatic',
clustering.method = 'none',
# side covariate
covariates = sample.covariate,
# top covariate and covariate border specification
covariates.top = chr.covariate,
covariate.legend = combo.cov.legend,
# making outline of border a matching green
covariates.top.grid.border = list(col = 'lightblue', lwd = 2),
# making certain column divisions a different colour
covariates.top.col.lines = c(5,6),
covariates.top.grid.col = list(col = 'blue', lwd = 2),
# legend customization
legend.side = c('right','left','top'),
legend.title.cex = 0.75,
legend.cex = 0.75,
legend.title.just = 'left',
legend.border = list(lwd = 1),
description = 'Heatmap created using BoutrosLab.plotting.general',
resolution = 200
);
# Custom gridlines
create.heatmap(
# filename = tempfile(pattern = 'Heatmap_Gridlines', fileext = '.tiff'),
x = microarray[1:20, 1:20],
main = 'Gridlines',
xlab.label = 'Genes',
ylab.label = 'Samples',
xaxis.lab = NA,
yaxis.lab = 1:20,
xaxis.cex = 0.75,
yaxis.cex = 0.75,
xaxis.fontface = 1,
yaxis.fontface = 1,
colourkey.cex = 1,
colourkey.labels.at = seq(2,12,1),
colour.alpha = 'automatic',
# colouring gridlines
grid.row = TRUE,
grid.col = TRUE,
row.colour = 'white',
col.colour = 'white',
row.lwd = 1.5,
col.lwd = 1.5,
description = 'Heatmap created using BoutrosLab.plotting.general',
resolution = 200
);
# Label cells
create.heatmap(
# filename = tempfile(pattern = 'Heatmap_Labelled_Cells', fileext = '.tiff'),
x = microarray[1:20, 1:20],
main = 'Labelled cells',
xlab.label = 'Genes',
ylab.label = 'Samples',
xaxis.cex = 0.75,
yaxis.cex = 0.75,
xaxis.fontface = 1,
yaxis.fontface = 1,
colourkey.cex = 1,
colourkey.labels.at = seq(2,12,1),
colour.alpha = 'automatic',
grid.row = TRUE,
grid.col = TRUE,
row.colour = 'white',
col.colour = 'white',
row.lwd = 1.5,
col.lwd = 1.5,
clustering.method = 'none',
# conditionally labelling cells
# flipping rows and columns because the heatmap function does so
row.pos = which(microarray[1:20, 1:20] > 11, arr.ind = TRUE)[,2],
col.pos = which(microarray[1:20, 1:20] > 11, arr.ind = TRUE)[,1],
cell.text = microarray[1:20, 1:20][microarray[1:20, 1:20] > 11],
text.col = 'white',
text.cex = 0.65,
description = 'Heatmap created using BoutrosLab.plotting.general',
resolution = 200
);
# Label cells
create.heatmap(
# filename = tempfile(pattern = 'Heatmap_Labelled_Cells_NPC', fileext = '.tiff'),
x = microarray[1:20, 1:20],
main = 'Labelled cells',
xlab.label = 'Genes',
ylab.label = 'Samples',
xaxis.cex = 0.75,
yaxis.cex = 0.75,
xaxis.fontface = 1,
yaxis.fontface = 1,
colourkey.cex = 1,
colourkey.labels.at = seq(2,12,1),
colour.alpha = 'automatic',
grid.row = TRUE,
grid.col = TRUE,
row.colour = 'white',
col.colour = 'white',
row.lwd = 1.5,
col.lwd = 1.5,
clustering.method = 'none',
text.use.grid.coordinates = FALSE,
# conditionally labelling cells
# flipping rows and columns because the heatmap function does so
cell.text = c("text1","text2"),
text.col = 'white',
text.cex = 0.65,
text.position = list(c(0.5,0.5),c(0.75,0.75)),
description = 'Heatmap created using BoutrosLab.plotting.general',
resolution = 200
);
# Method 1 of adding symbols (very similar to how text is added)
points <- microarray[1:20, 1:20][microarray[1:20, 1:20] > 11];
size.from <- range(points, na.rm = TRUE);
size.to <- c(1,3);
point.size <- (points - size.from[1])/diff(size.from) * diff(size.to) + size.to[1];
point.colour <- grey(runif(sum(microarray[1:20, 1:20] > 11), max = 0.5));
create.heatmap(
# filename = tempfile(pattern = 'Heatmap_Symbols_1', fileext = '.tiff'),
x = microarray[1:20, 1:20],
main = 'Symbols',
xlab.label = 'Genes',
ylab.label = 'Samples',
xaxis.cex = 0.75,
yaxis.cex = 0.75,
xaxis.fontface = 1,
yaxis.fontface = 1,
colourkey.cex = 1,
colourkey.labels.at = seq(2,12,1),
colour.alpha = 'automatic',
clustering.method = 'none',
# conditionally adding points to cells
# flipping rows and columns because the heatmap function does so
row.pos = which(microarray[1:20, 1:20] > 11, arr.ind = TRUE)[,2],
col.pos = which(microarray[1:20, 1:20] > 11, arr.ind = TRUE)[,1],
cell.text = rep(expression("\u25CF"), times = sum(microarray[1:20, 1:20] > 11)),
text.col = point.colour,
text.cex = point.size,
description = 'Heatmap created using BoutrosLab.plotting.general',
resolution = 200
);
# Method 2 of Adding Symbols
# Create matrices to describe the symbols
circle.matrix <- matrix(
nrow = 20,
ncol = 20,
data = FALSE
);
circle.colour.matrix <- matrix(
nrow = 20,
ncol = 20,
data = 'pink'
);
circle.size.matrix <- matrix(
nrow = 20,
ncol = 20,
data = 20
);
border.matrix <- matrix(
nrow = 20,
ncol = 20,
data = FALSE
);
border.colour.matrix <- matrix(
nrow = 20,
ncol = 20,
data = 'black'
);
border.size.matrix <- matrix(
nrow = 20,
ncol = 20,
data = 4
);
square.matrix <- matrix(
nrow = 20,
ncol = 20,
data = FALSE
);
square.colour.matrix <- matrix(
nrow = 20,
ncol = 20,
data = 'pink'
);
square.size.matrix <- matrix(
nrow = 20,
ncol = 20,
data = 10
);
# setting up the symbols
symbol.locations <- list(
circles = list(
list(
x = circle.matrix,
col = circle.colour.matrix,
size = circle.size.matrix
)
),
borders = list(
list(
x = border.matrix,
col = border.colour.matrix,
size = border.size.matrix
),
# creating a border encompassing a larger area
list(
xright = 12.10,
xleft = 12,
ybottom = 1,
ytop = 20,
size = 4,
col = 'pink'
)
),
squares = list(
list(
x = square.matrix,
col = square.colour.matrix,
size = square.size.matrix
)
)
);
# Set which items in the matrix will be shown
# symbol.locations$borders[[1]]$x <- FALSE;
# symbol.locations$squares[[1]]$x <- FALSE;
symbol.locations$circles[[1]]$x[which(microarray[1:20,1:20] > 11, arr.ind = TRUE)] <- TRUE;
create.heatmap(
# filename = tempfile(pattern = 'Heatmap_Symbols_2', fileext = '.tiff'),
x = microarray[1:20, 1:20],
main = 'Symbols',
xlab.label = 'Genes',
ylab.label = 'Samples',
xaxis.cex = 0.75,
yaxis.cex = 0.75,
xaxis.fontface = 1,
yaxis.fontface = 1,
colourkey.cex = 1,
colourkey.labels.at = seq(2,12,1),
colour.alpha = 'automatic',
clustering.method = 'none',
# adding symbols
symbols = symbol.locations,
description = 'Heatmap created using BoutrosLab.plotting.general',
resolution = 200
);
# Rotate matrix
create.heatmap(
# filename = tempfile(pattern = 'Heatmap_Rotated_Matrix', fileext = '.tiff'),
x = microarray[1:20, 1:20],
main = 'Rotated matrix',
# Also flip labels
ylab.label = 'Genes',
xlab.label = 'Samples',
xaxis.lab = NA,
yaxis.lab = NA,
xaxis.cex = 0.75,
yaxis.cex = 0.75,
xaxis.fontface = 1,
yaxis.fontface = 1,
colourkey.cex = 1,
colourkey.labels.at = seq(2,12,1),
colour.alpha = 'automatic',
grid.row = TRUE,
grid.col = TRUE,
row.colour = 'white',
col.colour = 'white',
row.lwd = 1.5,
col.lwd = 1.5,
# stop heatmap function from rotating matrix
same.as.matrix = TRUE,
description = 'Heatmap created using BoutrosLab.plotting.general',
resolution = 200
);
# Example of using discrete data
discrete.data <- microarray[1:10,1:40];
# Looking for values greater than 10
discrete.data[which(discrete.data < 10, arr.ind = TRUE)] <- 0;
discrete.data[which(discrete.data > 0, arr.ind = TRUE)] <- 1;
sex.colour <- as.character(patient$sex);
sex.colour[sex.colour == 'male'] <- 'dodgerblue';
sex.colour[sex.colour == 'female'] <- 'pink';
stage.colour <- as.character(patient$stage)
stage.colour[stage.colour == 'I'] <- 'plum1'
stage.colour[stage.colour == 'II'] <- 'orchid1'
stage.colour[stage.colour == 'III'] <- 'orchid3'
stage.colour[stage.colour == 'IV'] <- 'orchid4'
msi.colour <- as.character(patient$msi)
msi.colour[msi.colour == 'MSS'] <- 'chartreuse4'
msi.colour[msi.colour == 'MSI-High'] <- 'chartreuse2'
discrete.covariate <- list(
rect = list(
col = 'transparent',
fill = sex.colour,
lwd = 1.5
),
rect = list(
col = 'transparent',
fill = stage.colour,
lwd = 1.5
),
rect = list(
col = 'transparent',
fill = msi.colour,
lwd = 1.5
)
);
discrete.covariate.legend <- list(
legend = list(
colours = c('dodgerblue', 'pink'),
labels = c('male','female'),
title = expression(underline('Sex'))
),
legend = list(
colours = c('plum1', 'orchid1', 'orchid3', 'orchid4'),
labels = c('I','II', 'III', 'IV'),
title = expression(underline('Stage'))
),
legend = list(
colours = c('chartreuse4', 'chartreuse2'),
labels = c('MSS','MSI-High'),
title = expression(underline('MSI'))
)
);
create.heatmap(
# filename = tempfile(pattern = 'Heatmap_Discrete_Data', fileext = '.tiff'),
x = discrete.data,
main = 'Discrete data',
xlab.label = 'Samples',
same.as.matrix = TRUE,
# Customize plot
clustering.method = 'none',
total.colours = 3,
colour.scheme = c('white','black'),
fill.colour = 'grey95',
# Changing axes
xat = seq(0,40,5),
xaxis.lab = seq(0,40,5),
yaxis.lab = rownames(microarray)[1:10],
yaxis.cex = 0.75,
xaxis.cex = 0.75,
xaxis.rot = 0,
xlab.cex = 1,
# Covariates
covariates.top = discrete.covariate,
covariate.legend = discrete.covariate.legend,
legend.side = 'right',
legend.title.cex = 0.75,
legend.cex = 0.75,
legend.title.just = 'left',
legend.between.row = 0.2,
legend.border = list(col = 'transparent'),
legend.border.padding = 2,
shrink = 0.7,
covariates.top.grid.border = list(col = 'black', lwd = 2),
scale.data = FALSE,
print.colour.key = FALSE,
description = 'Heatmap created using BoutrosLab.plotting.general',
resolution = 200
);
# Correlation matrix
# Example of how to visualize the relationship between (e.x.) different cellularity estimates
# Generate a correlation matrix
cor.data <- cor(t(microarray[1:10,1:10]), method = 'spearman');
colnames(cor.data) <- colnames(microarray)[1:10];
# ensure that input data matrix is equal to what the heatmap clustering produces
distance.matrix <- as.dist(1 - cor(t(cor.data), use = "pairwise", method = "pearson"));
clustered.order <- hclust(d = distance.matrix, method = "ward")$order;
cor.data <- cor.data[clustered.order, clustered.order];
# prepare labels
x <- round(cor.data, 2);
x[x == 1] <- colnames(x);
y <- x;
for (i in 1:(ncol(y)-1)) {
y[i, (i+1):nrow(y)] <- "";
};
create.heatmap(
# filename = tempfile(pattern = 'Heatmap_Cellularity_Estimates', fileext = '.tiff'),
x = cor.data,
main = 'Correlation matrix',
xaxis.lab = NULL,
yaxis.lab = NULL,
cell.text = y,
clustering.method = 'ward',
plot.dendrograms = 'none',
rows.distance.method = 'correlation',
cols.distance.method = 'correlation',
cor.method = 'pearson',
col.pos = which(y != '1', arr.ind = TRUE)[,1],
row.pos = which(y != '1', arr.ind = TRUE)[,2],
text.fontface = 2,
text.col = 'white',
text.cex = 0.70,
colourkey.cex = 1,
colour.scheme = c('blue', 'darkgrey', 'brown'),
colour.centering.value = 0,
at = seq(-1, 1, 0.001),
colour.alpha = 1.5,
grid.row = TRUE,
grid.col = TRUE,
description = 'Heatmap created using BoutrosLab.plotting.general',
resolution = 200
);
# Discrete sequential colours
create.heatmap(
# filename = tempfile(pattern = 'Heatmap_Discrete_Colours_Sequential', fileext = '.tiff'),
x = microarray[1:20, 1:20],
main = 'Discrete colours',
xlab.label = 'Genes',
ylab.label = 'Samples',
xaxis.lab = NA,
yaxis.lab = 1:20,
xaxis.cex = 0.75,
yaxis.cex = 0.75,
xaxis.fontface = 1,
yaxis.fontface = 1,
colourkey.cex = 1,
# Adjusting total colours plotted
colourkey.labels.at = seq(2,12,1),
at = seq(2,12,1),
# Add one to account for a 'null' colour
total.colours = 11,
description = 'Heatmap created using BoutrosLab.plotting.general',
resolution = 200
);
# Discrete qualitative colours
create.heatmap(
# filename = tempfile(pattern = 'Heatmap_Discrete_Colours_Qualitative', fileext = '.tiff'),
x = microarray[1:20, 1:20],
main = 'Discrete colours',
xlab.label = 'Genes',
ylab.label = 'Samples',
xaxis.lab = NA,
yaxis.lab = 1:20,
xaxis.cex = 0.75,
yaxis.cex = 0.75,
xaxis.fontface = 1,
yaxis.fontface = 1,
colourkey.cex = 1,
# Adjusting total colours plotted
colourkey.labels.at = seq(2,12,1),
colourkey.labels = seq(2,12,1),
at = seq(2,12,1),
# Add one to account for a 'null' colour
total.colours = 11,
colour.scheme = default.colours(10),
description = 'Heatmap created using BoutrosLab.plotting.general',
resolution = 200
);
# Nature style
create.heatmap(
# filename = tempfile(pattern = 'Heatmap_Nature_style', fileext = '.tiff'),
x = microarray[1:20, 1:20],
main = 'Nature style',
xaxis.lab = NA,
yaxis.lab = 1:20,
xaxis.cex = 0.75,
yaxis.cex = 0.75,
xaxis.fontface = 1,
yaxis.fontface = 1,
colourkey.cex = 1,
# Adjusting total colours plotted
colourkey.labels.at = seq(2,12,1),
colourkey.labels = seq(2,12,1),
at = seq(2,12,1),
# Add one to account for a 'null' colour
total.colours = 11,
colour.scheme = default.colours(10),
# set style to Nature
style = 'Nature',
# demonstrating how to italicize character variables
ylab.label = expression(paste('italicized ', italic('a'))),
# demonstrating how to create en-dashes
xlab.label = expression(paste('en dashs: 1','\u2013', '10'^'\u2013', ''^3)),
description = 'Heatmap created using BoutrosLab.plotting.general',
resolution = 200
);
# create heatmap with key like legend - used to show range of continuous variables
# First create legend with discrete colours
sex.colour <- as.character(patient$sex);
sex.colour[sex.colour == 'male'] <- 'dodgerblue';
sex.colour[sex.colour == 'female'] <- 'pink';
stage.colour <- as.character(patient$stage)
stage.colour[stage.colour == 'I'] <- 'plum1'
stage.colour[stage.colour == 'II'] <- 'orchid1'
stage.colour[stage.colour == 'III'] <- 'orchid3'
stage.colour[stage.colour == 'IV'] <- 'orchid4'
msi.colour <- as.character(patient$msi)
msi.colour[msi.colour == 'MSS'] <- 'chartreuse4'
msi.colour[msi.colour == 'MSI-High'] <- 'chartreuse2'
discrete.covariate <- list(
rect = list(
col = 'transparent',
fill = sex.colour,
lwd = 1.5
),
rect = list(
col = 'transparent',
fill = stage.colour,
lwd = 1.5
),
rect = list(
col = 'transparent',
fill = msi.colour,
lwd = 1.5
)
);
discrete.covariate.legend <- list(
legend = list(
colours = c('dodgerblue', 'pink'),
labels = c('male','female'),
title = expression(underline('Sex'))
),
legend = list(
colours = c('plum1', 'orchid1', 'orchid3', 'orchid4'),
labels = c('I','II', 'III', 'IV'),
title = expression(underline('Stage'))
),
legend = list(
colours = c('chartreuse4', 'chartreuse2'),
labels = c('MSS','MSI-High'),
title = expression(underline('MSI'))
),
legend = list(
colours = c('grey0', 'grey100'),
labels = c('want key like','legend here'),
title = expression(underline('one')),
continuous = TRUE,
height=3
),
legend = list(
colours = c('grey0', 'grey100'),
labels = c('want key like','legend here'),
title = expression(underline('two'))
),
legend = list(
colours = c('grey0', 'grey100'),
labels = c(0,10),
title = expression(underline('three')),
continuous = TRUE,
width = 3,
tck = 1,
tck.number = 3,
at = c(0,100),
angle = -90,
just = c("center","bottom")
)
);
create.heatmap(
# filename = tempfile(pattern = 'Heatmap_ContinuousVariablesKey', fileext = '.tiff'),
x = patient[1:20, 4:6],
xlab.label = 'Samples',
ylab.label = 'Scaled Data',
xaxis.cex = 0.75,
yaxis.cex = 0.75,
clustering.method = 'none',
print.colour.key = FALSE,
scale=TRUE,
same.as.matrix = FALSE,
covariates.top = discrete.covariate,
covariates.top.grid.row = list(lwd = 1),
covariate.legends = discrete.covariate.legend,
legend.title.just = 'left',
colour.scheme = c('gray0','grey100'),
fill.colour = 'grey95',
axis.xlab.padding = 1.5,
resolution = 200
);
create.heatmap(
# filename = tempfile(pattern = 'Heatmap_borderRemoved', fileext = '.tiff'),
x = simple.data,
main = 'Simple',
description = 'Heatmap created using BoutrosLab.plotting.general',
axes.lwd = 0,
resolution = 200
);