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 gpar specifying the behaviour of row lines in the right covariate bars

covariates.grid.col

A list of parameters passed to gpar specifying the behaviour of column lines in the right covariate bars

covariates.grid.border

A list of parameters passed to gpar specifying the behaviour of the border around the right covariate bars

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.grid.row is not specified

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.grid.col is not specified

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 gpar specifying the behaviour of row lines in the top covariate bars

covariates.top.grid.col

A list of parameters passed to gpar specifying the behaviour of column lines in the top covariate bars

covariates.top.grid.border

A list of parameters passed to gpar specifying the behaviour of the border around the top covariate bars

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.grid.row is not specified

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.grid.col is not specified

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 legends argument of legend.grob for more information

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 gpar specifying line options for the legend border, defaults to NULL (no border drawn)

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.side

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)
    );

create.heatmap(
    filename = tempfile(pattern = 'Heatmap_Simple_Using_Colours', fileext = '.tiff'),
    x = simple.data.col, 
    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
    );


[Package BoutrosLab.plotting.general version 6.0.3 Index]