Copy number aberration (CNA) data from colon cancer patients

Description

CNA calls from 30 genes across 58 colon cancer patients. Additional data on the patient samples is found in the patient dataset. The same patient samples are described in the microarray and SNV datasets.

Usage

CNA

Format

A data frame with 58 columns and 30 rows. The columns indicate the patient sample, and the rows indicate the gene. The contents of the data frame are encoded such that 0 indicates no CNA, -1 indicates a CNA loss, and 1 indicates a CNA gain.

Author(s)

Christine P'ng

Examples

data(CNA);
create.dotmap(
    # filename = tempfile(pattern = 'Using_CNA_dataset', fileext = '.tiff'),
    x = CNA[1:15, 1:15],
    main = 'CNA data',
    xaxis.cex = 0.8,
    yaxis.cex = 0.8,
    xaxis.rot = 90,
    description = 'Dotmap created by BoutrosLab.plotting.general',
    resolution = 50
    );

Single nucleotide variant (SNV) data from colon cancer patients

Description

SNV calls from 30 genes across 58 colon cancer patients. Additional data on the patient samples is found in in the "patient" dataset. The same patient samples are described in the "microarray" and "CNA" datasets.

Usage

SNV

Format

A data frame with 58 columns and 30 rows. The columns indicate the patient sample, and the rows indicate the gene. The contents of the data frame are either NA (indicating no SNV call was made) or one of:

• 1 - nonsynonymous SNV

• 2 - stopgain SNV

• 3 - frameshift insertion

• 4 - frameshift deletion

• 5 - nonframeshift insertion

• 6 - nonframeshift deletion

• 7 - splicing

• 8 - unknown

Author(s)

Christine P'ng

Examples

len <- apply(SNV[1:15], 2, function(x){mutation.count <- length(which(x == 1))});

create.barplot(
    # filename = tempfile(pattern = 'Using_SNV_dataset', fileext = '.tiff'),
    formula = len ~ colnames(SNV[1:15]) ,
    data = SNV,
    main = 'SNV dataset',
    xaxis.rot = 45, 
    ylimits = c(0,30),
    yat = seq(0,30,5),
    description = 'Barplot created by BoutrosLab.plotting.general'
    );

Create ideal labels and values for a given numeric vector (detects log scales)

Description

Takes a numeric vector and several parameters and outputs an object with values and labels ideal for given data

Usage

auto.axis(
    x,
    pretty = TRUE,
    log.scaled = NA,
    log.zero = 0.1,
    max.factor = 1,
    min.factor = 1,
    include.origin = TRUE,
    num.labels = 5,
    max.min.log10.diff = 2
)

Arguments

Author(s)

Takafumi N. Yamaguchi

See Also

stripplot, lattice or the Lattice book for an overview of the package.

Examples

set.seed(223);
simple.data <- data.frame(
    x = sample(1:15, 10),
    y = LETTERS[1:10]
    );

auto.axis(simple.data$x)

data2 <- c(1,10,100,1000)

auto.axis(data2)

Creates a colour gradient

Description

Creates a sequential palette of colours.

Usage

colour.gradient(
	colour, 
	length
	);

Arguments

Author(s)

Ren Sun & Christine P'ng

Examples

display.colours(colour.gradient('dodgerblue2', 6));

display.colours(colour.gradient(default.colours(1), 3));

Create one or more covariate bars

Description

Takes a list of covariate bar annotates and creates a grid graphical object for them

Usage

covariates.grob(
	covariates,
	ord,
	side = 'right',
	size = 1,
	grid.row = NULL,
	grid.col = NULL,
	grid.border = NULL,
	row.lines = NULL,
	col.lines = NULL,
	reorder.grid.index = FALSE,
        x = 0.5,
        y = 0.5
	);

Arguments

Value

A grid graphical object (grob) representing the covariate bar(s)

Notes

This code is an adaptation of the dendrogramGrob function in the latticeExtra package. It uses functions of the grid package.

By default, the covariate bar grid is drawn via borders around individual rectangles using the parameters specified in the covariates argument (col, lwd, etc.). If grid.row, grid.col, or grid.border are specified by the user, additional grid lines are drawn over any existing ones using the parameters in these lists.

Author(s)

Lauren Chong

See Also

gpar

Examples

# The 'cairo' graphics is preferred but on M1 Macs this is not available
bitmap.type = getOption('bitmapType')
if (capabilities('cairo')) {
	bitmap.type <- 'cairo';
	}

# create temp data
set.seed(1234567890);

x <- outer(-5:5, -5:5, '*') + matrix(nrow = 11, ncol = 11, data = runif(11 * 11));
colnames(x) <- paste('col', 1:11, sep = '-');
rownames(x) <- paste('row', 1:11, sep = '-');

# set covariates
covariate.colours1 <- x[,1]
covariate.colours1[covariate.colours1 >= 0] <- default.colours(3)[1];
covariate.colours1[covariate.colours1 != default.colours(3)[1]] <- default.colours(3)[2];

covariate.colours2 <- x[,1]
covariate.colours2[covariate.colours2 >= 0] <- default.colours(3)[2];
covariate.colours2[covariate.colours2 != default.colours(3)[2]] <- default.colours(3)[3];

# create an object to draw the covariates from
covariates1 <- list(
    rect = list(
        col = 'black',
        fill = covariate.colours1,
        lwd = 1.5
        ),
    rect = list(
        col = 'black',
        fill = covariate.colours2,
        lwd = 1.5
        )
    );

# create a covariates grob using a simple incremental ordering and default behaviour
covariates.grob1 <- covariates.grob(
    covariates = covariates1,
    ord = c(1:ncol(x)),
    side = 'right'
    );

# create a dendrogram for x
cov.dendrogram <- BoutrosLab.plotting.general::create.dendrogram(
    x = x,
    clustering.method = 'average'
    );

covariates2 <-list(
    rect = list(
        col = 'black',
        fill = covariate.colours2,
        lwd = 1.5
        )
    );

# create a covariates grob using the dendrogram ordering and double the default size
covariates.grob2 <- covariates.grob(
    covariates = covariates2,
    ord = order.dendrogram(cov.dendrogram),
    side = 'top',
    size = 2
    );

# add a border of a different colour
covariates.grob3 <- covariates.grob(
    covariates = covariates1,
    ord = c(1:ncol(x)),
    side = 'right',
    grid.border = list(col = 'red', lwd = 1.5)
    );

# create covariates with transparent rectangle borders
covariates3 <- list(
    rect = list(
        col = 'transparent',
        fill = covariate.colours1,
        lwd = 1.5
        ),
    rect = list(
        col = 'transparent',
        fill = covariate.colours2,
        lwd = 1.5
        )
    );

# add column grid lines and a border with default gpar settings
covariates.grob4 <- covariates.grob(
    covariates = covariates3,
    ord = c(1:nrow(x)),
    side = 'top',
    grid.col = list(col = 'black', lty = 3),
    grid.border = list()
    );

# draw a subset of row/column lines
covariates.grob5 <- covariates.grob(
    covariates = covariates3,
    ord = order.dendrogram(cov.dendrogram),
    side = 'right',
    grid.row = list(lineend = 'butt', lwd = 2),
    row.lines = 6,
    reorder.grid.index = FALSE, # note: this is already set by default
    grid.col = list(lty = 2),
    col.lines = c(0,1)
    );

Make a barplot

Description

Takes a data.frame and creates a barplot

Usage

create.barplot(
	formula,
	data,
	groups = NULL,
	stack = FALSE,
	filename = NULL,
	main = NULL,
	main.just = 'center',
	main.x = 0.5,
	main.y = 0.5,
	main.cex = 3,
	xlab.label = tail(sub('~', '', formula[-2]), 1),
	ylab.label = tail(sub('~', '', formula[-3]), 1),
	xlab.cex = 2,
	ylab.cex = 2,
	xlab.col = 'black',
	ylab.col = 'black',
	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,
	abline.h = NULL,
	abline.v = NULL,
	abline.lty = 1,
	abline.lwd = NULL,
	abline.col = 'black',
	axes.lwd = 1,
	add.grid = FALSE,
	xgrid.at = xat,
	ygrid.at = yat,
	grid.lwd = 5,
	grid.col = NULL,
	xaxis.lab = TRUE,
	yaxis.lab = TRUE,
	xaxis.col = 'black',
	yaxis.col = 'black',
	xaxis.fontface = 'bold',
	yaxis.fontface = 'bold',
	xaxis.cex = 1.5,
	yaxis.cex = 1.5,
	xaxis.rot = 0,
	yaxis.rot = 0,
	xaxis.tck = 1,
	yaxis.tck = 1,
	xlimits = NULL,
	ylimits = NULL,
	xat = TRUE,
	yat = TRUE,
	layout = NULL,
	as.table = FALSE,
	x.spacing = 0,
	y.spacing = 0,
	x.relation = 'same',
	y.relation = 'same',
	top.padding = 0.5,
	bottom.padding = 1,
	right.padding = 1,
	left.padding = 1,
	key.bottom = 0.1,
	ylab.axis.padding = 0.5,
	xlab.axis.padding = 0.5,
	col = 'black',
	border.col = 'black',
	border.lwd = 1,
	plot.horizontal = FALSE,
	background.col = 'transparent',
	origin = 0,
	reference = TRUE,
	box.ratio = 2,
	sample.order = 'none',
	group.labels = FALSE,
	key = list(text = list(lab = c(''))),
	legend = NULL,
	add.text = FALSE,
	text.labels = NULL,
	text.x = NULL,
	text.y = NULL,
	text.col = 'black',
	text.cex = 1,
	text.fontface = 'bold',
	strip.col = 'white',
	strip.cex = 1,
	y.error.up = NULL,
	y.error.down = y.error.up,
	y.error.bar.col = 'black',
	error.whisker.width = width/(nrow(data)*4),
	error.bar.lwd = 1,
	error.whisker.angle = 90,
	add.rectangle = FALSE,
	xleft.rectangle = NULL,
	ybottom.rectangle = NULL,
	xright.rectangle = NULL,
	ytop.rectangle = NULL,
	col.rectangle = 'grey85',
	alpha.rectangle = 1,
	line.func = NULL,
	line.from = 0,
	line.to = 0,
	line.col = 'transparent',
	line.infront = TRUE,
	text.above.bars = list(labels = NULL,
		padding = NULL,
		bar.locations = NULL,
		rotation = 0
		),
	raster = NULL,
	raster.vert = TRUE,
	raster.just = 'center',
	raster.width.dim = unit(2/37, 'npc'),
	height = 6,
	width = 6,
	size.units = 'in',
	resolution = 1600,
	enable.warnings = FALSE,
	description = 'Created with BoutrosLab.plotting.general',
	style = 'BoutrosLab',
	preload.default = 'custom',
	use.legacy.settings = FALSE,
	inside.legend.auto = FALSE,
	disable.factor.sorting = FALSE
	);

Arguments

.

,

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 histogram. In particular, if a script that uses such a call of create histogram 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
    

Author(s)

Mehrdad Shamsi

See Also

barchart, lattice or the Lattice book for an overview of the package.

Examples

set.seed(12345);

simple.data <- data.frame(
    x = sample(1:15, 5),
    y = LETTERS[1:5]
    );

# Simple example
create.barplot(
    # filename = tempfile(pattern = 'Barplot_Simple', fileext = '.tiff'),
    formula = x ~ y,
    data = simple.data,
    yat = seq(0,16,2),
    resolution = 30
    );

# set up the data
total.counts <- apply(SNV[1:15], 2, function(x){ mutation.count <- (30 - sum(is.na(x)))});
count.nonsyn <- function(x){
    mutation.count <- length(which(x == 1));
    }
nonsynonymous.SNV <- apply(SNV[1:15], 2, count.nonsyn);
other.mutations <- total.counts - nonsynonymous.SNV;

# subset the first fifteen samples
barplot.data <- data.frame(
    samples = rep(1:15, 2),
    mutation = c(rep('nonsynonymous', 15), rep('other',15)),
    type = c(rep(1, 15), rep(2,15)),
    values = c(nonsynonymous.SNV, other.mutations),
    sex = rep(patient$sex[1:15], 2),
    stage = rep(patient$stage[1:15], 2),
    msi = rep(patient$msi[1:15], 2)
    );

# Minimal input
create.barplot(
    # filename = tempfile(pattern = 'Barplot_Minimal_Input', fileext = '.tiff'),
    formula = values ~ samples ,
    data = barplot.data[barplot.data$mutation == 'nonsynonymous',],
    main = 'Minimal input',
    # Editing the metadata
    description = 'Barplot created by BoutrosLab.plotting.general',
    resolution = 100
    );

# Axes labels & limits
create.barplot(
    # filename = tempfile(pattern = 'Barplot_Custom_Axes', fileext = '.tiff'),
    formula = values ~ samples,
    data = barplot.data[barplot.data$mutation == 'nonsynonymous',],
    main = 'Axes labels & limits',
    # Setting axes labels
    xlab.lab = 'Sample',
    ylab.lab = 'Nonsynonymous SNVs',
    # Setting y-axis limits and tick-mark locations
    ylimits = c(0,30),
    yat = seq(0,30,5),
    description = 'Barplot created by BoutrosLab.plotting.general',
    resolution = 100
    );

# Font size and font face
create.barplot(
    # filename = tempfile(pattern = 'Barplot_Font_Changes', fileext = '.tiff'),
    formula = values ~ samples,
    data = barplot.data[barplot.data$mutation == 'nonsynonymous',],
    main = 'Font changes',
    xlab.lab = 'Samples',
    ylab.lab = 'Nonsynonymous SNVs',
    ylimits = c(0,30),
    yat = seq(0,30,5),
    # Changing font sizes
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    # Changing font type
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    description = 'Barplot created by BoutrosLab.plotting.general',
    resolution = 100
    );


# Sorting data
create.barplot(
    # filename = tempfile(pattern = 'Barplot_Sorted', fileext = '.tiff'),
    formula = values ~ samples,
    data = barplot.data[barplot.data$mutation == 'nonsynonymous',],
    main = 'Sorted bars',
    xlab.lab = 'Samples',
    ylab.lab = 'Nonsynonymous SNVs',
    ylimits = c(0,30),
    yat = seq(0,30,5),
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    # Order bars either by 'increasing' or 'decreasing'
    sample.order = 'decreasing',
    description = 'Barplot created by BoutrosLab.plotting.general',
    resolution = 100
    );

# Sorting data with horizontal barplot
create.barplot(
    formula = samples ~ values,
    data = barplot.data[barplot.data$mutation == 'nonsynonymous',],
    main = 'Sorted bars',
    xlab.lab = 'Samples',
    ylab.lab = 'Nonsynonymous SNVs',
    xlimits = c(0,30),
    xat = seq(0,30,5),
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    # Order bars either by 'increasing' or 'decreasing'
    sample.order = 'decreasing',
    plot.horizontal = TRUE,
    resolution = 100
    )

# Log-Scaled Axis
log.data <- data.frame(
    x = 10 ** sample(1:15, 5),
    y = LETTERS[1:5]
    );

create.barplot(
    formula = x ~ y,
    data = log.data,
    # Log base 10 scale y-axis
    yat = 'auto.log',
    main = 'Log Scaled',
    description = 'Barplot created by BoutrosLab.plotting.general',
    resolution = 100
    );

# Colour changes
sex.colours <- replace(as.vector(barplot.data$sex), which(barplot.data$sex == 'male'),'dodgerblue');
sex.colours <- replace(sex.colours, which(barplot.data$sex == 'female'), 'pink');

create.barplot(
    # filename = tempfile(pattern = 'Barplot_Colour_Changes', fileext = '.tiff'),
    formula = values ~ samples,
    data = barplot.data[barplot.data$mutation == 'nonsynonymous',],
    main = 'Colour changes',
    xlab.lab = 'Samples',
    ylab.lab = 'Nonsynonymous SNVs',
    ylimits = c(0,30),
    yat = seq(0,30,5),
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    # Colour bars based on sex
    col = sex.colours,
    description = 'Barplot created by BoutrosLab.plotting.general',
    resolution = 100
    );

# Legend
create.barplot(
    # filename = tempfile(pattern = 'Barplot_Legend', fileext = '.tiff'),
    formula = values ~ samples,
    data = barplot.data[barplot.data$mutation == 'nonsynonymous',],
    main = 'Legend',
    xlab.lab = 'Samples',
    ylab.lab = 'Nonsynonymous SNVs',
    ylimits = c(0,30),
    yat = seq(0,30,5),
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    col = sex.colours,
    # Adding legend to explain bar colour-coding
    legend = list(
        inside = list(
            fun = draw.key,
            args = list(
                key = list(
                    points = list(
                        col = 'black',
                        pch = 22,
                        cex = 3,
                        fill = c('dodgerblue', 'pink')
                        ),
                    text = list(
                        lab = c('Male','Female')
                        ),
                    padding.text = 5,
                    cex = 1
                    )
                ),
                # Positioning legend on plot
                x = 0.75,
                y = 0.95
            )
        ),
    description = 'Barplot created by BoutrosLab.plotting.general',
    resolution = 100
    );

# Grouped barplot
create.barplot(
    # filename = tempfile(pattern = 'Barplot_Grouped', fileext = '.tiff'),
    formula = values ~ samples,
    data = barplot.data,
    main = 'Grouped bar chart',
    xlab.lab = 'Samples',
    ylab.lab = 'Mutations',
    ylimits = c(0,30),
    yat = seq(0,30,5),
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    # Setting groups
    groups = mutation,
    col = default.colours(12, is.greyscale = FALSE)[11:12],
    legend = list(
        inside = list(
            fun = draw.key,
            args = list(
                key = list(
                    points = list(
                        col = 'black',
                        pch = 22,
                        cex = 2,
                        fill = default.colours(12, is.greyscale = FALSE)[11:12]
                        ),
                    text = list(
                        lab = c('Nonsynonymous SNV','Other SNV')
                        ),
                    padding.text = 3,
                    cex = 1
                    )
                ),
            x = 0.55,
            y = 0.95
            )
        ),
    description = 'Barplot created by BoutrosLab.plotting.general',
    resolution = 100
    );

# Grouped labels
create.barplot(
    # filename = tempfile(pattern = 'Barplot_Grouped_Labels', fileext = '.tiff'),
    formula = values ~ samples,
    data = barplot.data,
    main = 'Grouped labels',
    xlab.lab = 'Samples',
    ylab.lab = 'Mutations',
    ylimits = c(0,30),
    yat = seq(0,30,5),
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    # Setting groups
    groups = mutation,
    col = default.colours(12, is.greyscale = FALSE)[11:12],
    # Grouped labels
    xaxis.lab = rep(c('nonsynonymous', 'other'), 15),
    xaxis.rot = 90,
    group.labels = TRUE,
    description = 'Barplot created by BoutrosLab.plotting.general',
    resolution = 200
    );

# Stacked barplot
create.barplot(
    # filename = tempfile(pattern = 'Barplot_Stacked', fileext = '.tiff'),
    formula = values ~ samples,
    data = barplot.data,
    main = 'Stacked bar chart',
    xlab.lab = 'Samples',
    ylab.lab = 'Mutations',
    ylimits = c(0,30),
    yat = seq(0,30,5),
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    groups = mutation,
    col = default.colours(12, is.greyscale = FALSE)[11:12],
    legend = list(
        inside = list(
            fun = draw.key,
            args = list(
                key = list(
                    points = list(
                        col = 'black',
                        pch = 22,
                        cex = 2,
                        # reverse order to match stacked bar order
                        fill = rev(default.colours(12, is.greyscale = FALSE)[11:12])
                        ),
                    text = list(
                        # reverse order to match stacked bar order
                        lab = rev(c('Nonsynonymous SNV','Other SNV'))
                        ),
                    padding.text = 3,
                    cex = 1
                    )
                ),
            x = 0.55,
            y = 0.95
            )
        ),
    # Changing the plot from a grouped plot to a stacked plot
    stack = TRUE,
    description = 'Barplot created by BoutrosLab.plotting.general',
    resolution = 200
    );

# Panel organization
create.barplot(
    # filename = tempfile(pattern = 'Barplot_Panel_Layout_numeric_conditioning', fileext = '.tiff'),
    # Setting the panel layout
    formula = values ~ samples | type,
    data = barplot.data,
    main = 'Panel layout',
    xlab.lab = 'Samples',
    ylab.lab = 'Mutations',
    ylimits = c(0,30),
    yat = seq(0,30,5),
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    description = 'Barplot created by BoutrosLab.plotting.general',
    resolution = 200
    );

create.barplot(
    # Setting the panel layout
    formula = values ~ samples | mutation,
    data = barplot.data,
    main = 'Panel layout',
    xlab.lab = 'Samples',
    ylab.lab = 'Mutations',
    ylimits = c(0,30),
    yat = seq(0,30,5),
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    description = 'Barplot created by BoutrosLab.plotting.general',
    resolution = 200
    );

# Panel organization 2
create.barplot(
    # filename = tempfile(pattern = 'Barplot_Panel_Layout_2', fileext = '.tiff'),
    formula = values ~ samples | mutation,
    data = barplot.data,
    main = 'Panel layout',
    xlab.lab = 'Samples',
    ylab.lab = 'Mutations',
    ylimits = c(0,30),
    yat = seq(0,30,5),
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    # Adjusting the panel layout
    layout = c(1,2),
    y.spacing = 1,
    description = 'Barplot created by BoutrosLab.plotting.general',
    resolution = 200
    );

# Covariates
# Note: Covariates can also be created using the create.multiplot function

# set covariate colour schemes
covariate.colours.sex <- as.character(barplot.data$sex);
covariate.colours.sex[covariate.colours.sex == 'male'] <- 'dodgerblue';
covariate.colours.sex[covariate.colours.sex == 'female'] <- 'pink';

covariate.colours.stage <- as.character(barplot.data$stage);
covariate.colours.stage[covariate.colours.stage == 'I'] <- 'plum1';
covariate.colours.stage[covariate.colours.stage == 'II'] <- 'orchid1';
covariate.colours.stage[covariate.colours.stage == 'III'] <- 'orchid3';
covariate.colours.stage[covariate.colours.stage == 'IV'] <- 'orchid4';

covariate.colours.msi <- as.character(barplot.data$msi);
covariate.colours.msi[covariate.colours.msi == 'MSS'] <- 'chartreuse4';
covariate.colours.msi[covariate.colours.msi == 'MSI-High'] <- 'chartreuse2';

# create object to draw covariates
covariates.object <- list(
    rect = list(
        col = 'white',
        fill = covariate.colours.sex,
        lwd = 1.5
        ),
    rect = list(
        col = 'white',
        fill = covariate.colours.stage,
        lwd = 1.5
        ),
    rect = list(
        col = 'white',
        fill = covariate.colours.msi,
        lwd = 1.5
        )
    );

# see BoutrosLab.plotting.general::covariates.grob() for more information
covariate.object.grob <- covariates.grob(
    covariates = covariates.object,
    ord = c(1:15),
    side = 'top',
    size = 0.8
    );

# Create legend to explain covariates
covariates.legends <- list(
    legend = list(
        colours = c('dodgerblue','pink'),
        labels = c('male','female'),
        title = 'Sex',
        border = 'white'
        ),
    legend = list(
        colours = c('plum1', 'orchid1', 'orchid3', 'orchid4'),
        labels = c('I','II','III','IV'),
        title = 'Stage',
        border = 'white'
        ),
    legend = list(
        colours = c('chartreuse4','chartreuse2'),
        labels = c('MSS','MSI-High'),
        title = 'MSI',
        border = 'white'
        )
    );

# see BoutrosLab.plotting.general::legend.grob() for more information
covariate.legend.grob <- legend.grob(
    legends = covariates.legends,
    title.just = 'left'
    );

create.barplot(
    # filename = tempfile(pattern = 'Barplot_Covariates', fileext = '.tiff'),
    formula = values ~ samples,
    data = barplot.data[barplot.data$mutation == 'nonsynonymous',],
    main = 'Covariates',
    ylab.lab = 'Mutations',
    ylimits = c(0,30),
    yat = seq(0,30,5),
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    yaxis.fontface = 1,
    # removing x-axis formatting to give space to covariates
    xaxis.tck = 0,
    xaxis.lab = rep('',15),
    xaxis.cex = 0,
    # covariates
    legend = list(
        bottom = list(fun = covariate.object.grob),
        right = list(fun = covariate.legend.grob)
        ),
    key = list(
        x = 1,
        y = -0.028,
        text = list(
            lab = c('Sex','Stage','MSI')
            ),
        padding.text = 1
        ),
    bottom.padding = 4,
    description = 'Barplot created by BoutrosLab.plotting.general',
    resolution = 200
    );

create.barplot(
    # filename = tempfile(pattern = 'Barplot_Auto_legend', fileext = '.tiff'),
    formula = values ~ samples,
    data = barplot.data[barplot.data$mutation == 'nonsynonymous',],
    main = 'Covariates',
    ylab.lab = 'Mutations',
    ylimits = c(0,30),
    yat = seq(0,30,5),
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    yaxis.fontface = 1,
    # removing x-axis formatting to give space to covariates
    xaxis.tck = 0,
    xaxis.lab = rep('',15),
    xaxis.cex = 0,
    # covariates
    legend = list(
        inside = list(fun = covariate.legend.grob)
        ),
    bottom.padding = 4,
    inside.legend.auto = TRUE,
    description = 'Barplot created by BoutrosLab.plotting.general',
    resolution = 200
    );

# Horizontal orientation
create.barplot(
    # filename = tempfile(pattern = 'Barplot_Horizontal', fileext = '.tiff'),
    # switch formula order
    formula = samples ~ values,
    data = barplot.data[barplot.data$mutation == 'nonsynonymous',],
    main = 'Plot horizontally',
    # Adjusting the panel layout
    plot.horizontal = TRUE,
    # covariates
    legend = list(
        inside = list(fun = covariate.legend.grob)
        ),
    inside.legend.auto = TRUE,
    description = 'Barplot created by BoutrosLab.plotting.general',
    resolution = 200
    );

# Change bar thickness and add text labels
create.barplot(
    # filename = tempfile(pattern = 'Barplot_Text_Labels', fileext = '.tiff'),
    # switch formula order
    formula = samples ~ values,
    data = barplot.data[barplot.data$mutation == 'nonsynonymous',],
    main = 'Text labels and thin bars',
    # Adjusting the panel layout
    plot.horizontal = TRUE,
    box.ratio = 0.6,
    add.text = TRUE,
    text.x = 27.75,
    text.y = 1:15,
    text.labels = barplot.data[barplot.data$mutation == 'nonsynonymous','values'],
    text.cex = 0.8,
    text.fontface = 'italic',
    description = 'Barplot created by BoutrosLab.plotting.general',
    resolution = 200
    );

# Error bars
error.data <- data.frame(
    genes = rownames(microarray)[1:15],
    values = apply(microarray[1:15,1:58], 1, mean),
    error = apply(microarray[1:15,1:58], 1, sd)
    );

create.barplot(
    # filename = tempfile(pattern = 'Barplot_Error_Bars', fileext = '.tiff'),
    # needs sequential x-axis
    formula = values ~ 1:15,
    data = error.data,
    y.error.up = error.data$error,
    xaxis.lab = error.data$genes,
    main = 'Error bars',
    xlab.lab = 'Gene',
    ylab.lab = 'Change in Expression',
    ylimits = c(0,14),
    yat = seq(0,14,2),
    xaxis.cex = 1,
    yaxis.cex = 1,
    xaxis.rot = 45,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    description = 'Barplot created by BoutrosLab.plotting.general',
    resolution = 100
    );


create.barplot(
    # filename = tempfile(pattern = 'Barplot_Error_Bars_Horizontal', fileext = '.tiff'),
    # needs sequential x-axis
    formula = values ~ 1:15,
    data = error.data,
    y.error.up = error.data$error,
    yaxis.lab = error.data$genes,
    plot.horizontal = TRUE,
    main = 'Error bars',
    xlab.lab = 'Gene',
    ylab.lab = 'Change in Expression',
    xaxis.cex = 1,
    yaxis.cex = 1,
    xaxis.rot = 45,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    description = 'Barplot created by BoutrosLab.plotting.general',
    resolution = 100
    );

# Grid lines
create.barplot(
    # filename = tempfile(pattern = 'Barplot_Gridlines', fileext = '.tiff'),
    formula = values ~ samples,
    data = barplot.data[barplot.data$mutation == 'nonsynonymous',],
    main = 'Gridlines',
    xlab.lab = 'Samples',
    ylab.lab = 'Nonsynonymous SNVs',
    ylimits = c(0,30),
    yat = seq(0,30,5),
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    # Grid lines
    add.grid = TRUE,
    xgrid.at = seq(0,15,2),
	col = sex.colours,
    legend = list(
        inside = list(
            fun = draw.key,
            args = list(
                key = list(
                    points = list(
                        col = 'black',
                        pch = 22,
                        cex = 3,
                        fill = c('dodgerblue', 'pink')
                        ),
                    text = list(
                        lab = c('Male','Female')
                        ),
                    padding.text = 5,
                    cex = 1
                    )
                ),
                # Positioning legend on plot
                x = 0.75,
                y = 0.95
            )
        ),
    description = 'Barplot created by BoutrosLab.plotting.general',
    resolution = 200
    );

# Grid lines 2
create.barplot(
    # filename = tempfile(pattern = 'Barplot_Gridlines_GreyBG', fileext = '.tiff'),
    formula = values ~ samples,
    data = barplot.data[barplot.data$mutation == 'nonsynonymous',],
    main = 'Gridlines & grey background',
    xlab.lab = 'Samples',
    ylab.lab = 'Nonsynonymous SNVs',
    ylimits = c(0,30),
    yat = seq(0,30,5),
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    # Grid lines
    background.col = 'grey85',
    add.grid = TRUE,
    xgrid.at = seq(0,15,2),
    col = sex.colours,
    legend = list(
        inside = list(
            fun = draw.key,
            args = list(
                key = list(
                    points = list(
                        col = 'black',
                        pch = 22,
                        cex = 3,
                        fill = c('dodgerblue', 'pink')
                        ),
                    text = list(
                        lab = c('Male','Female')
                        ),
                    padding.text = 5,
                    cex = 1
                    )
                ),
                # Positioning legend on plot
                x = 0.75,
                y = 0.95
            )
        ),
    description = 'Barplot created by BoutrosLab.plotting.general',
    resolution = 200
    );

# Labels
create.barplot(
    # filename = tempfile(pattern = 'Barplot_Labels', fileext = '.tiff'),
    formula = values ~ samples,
    data = barplot.data[barplot.data$mutation == 'nonsynonymous',],
    main = 'Labels',
    xlab.lab = 'Samples',
    ylab.lab = 'Nonsynonymous SNVs',
    ylimits = c(0,30),
    yat = seq(0,30,5),
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    # Labels
    text.above.bars = list(
        labels = c('*','27','15','*'),
        padding = 0.75,
        bar.locations = c(1, 3, 12, 14),
        rotation = 0
        ),
    col = sex.colours,
    legend = list(
        inside = list(
            fun = draw.key,
            args = list(
                key = list(
                    points = list(
                        col = 'black',
                        pch = 22,
                        cex = 3,
                        fill = c('dodgerblue', 'pink')
                        ),
                    text = list(
                        lab = c('Male','Female')
                        ),
                    padding.text = 5,
                    cex = 1
                    )
                ),
                # Positioning legend on plot
                x = 0.75,
                y = 0.95
            )
        ),
    description = 'Barplot created by BoutrosLab.plotting.general',
    resolution = 200
    );

# lines
create.barplot(
    # filename = tempfile(pattern = 'Barplot_Lines', fileext = '.tiff'),
    formula = values ~ samples,
    data = barplot.data[barplot.data$mutation == 'nonsynonymous',],
    main = 'Lines',
    xlab.lab = 'Samples',
    ylab.lab = 'Nonsynonymous SNVs',
    ylimits = c(0,30),
    yat = seq(0,30,5),
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    # Lines
    sample.order = 'increasing',
    line.func = function(x) {0.1*x**2},
    line.from = 0,
    line.to = 16,
    line.col = 'darkgrey',
    abline.h = 10,
    abline.col = 'red',
    col = sex.colours,
    legend = list(
        inside = list(
            fun = draw.key,
            args = list(
                key = list(
                    points = list(
                        col = 'black',
                        pch = 22,
                        cex = 3,
                        fill = c('dodgerblue', 'pink')
                        ),
                    text = list(
                        lab = c('Male','Female')
                        ),
                    padding.text = 5,
                    cex = 1
                    )
                ),
                # Positioning legend on plot
                x = 0.75,
                y = 0.95
            )
        ),
    description = 'Barplot created by BoutrosLab.plotting.general',
    resolution = 200
    );

# Background rectangle
create.barplot(
    # filename = tempfile(pattern = 'Barplot_Bg_Rectangle', fileext = '.tiff'),
    formula = values ~ samples,
    data = barplot.data[barplot.data$mutation == 'nonsynonymous',],
    main = 'Background rectangle',
    xlab.lab = 'Samples',
    ylab.lab = 'Nonsynonymous SNVs',
    ylimits = c(0,30),
    yat = seq(0,30,5),
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    sample.order = 'increasing',
    # Background rectangle
    add.rectangle = TRUE,
    xleft.rectangle = seq(0.5, 14.5, 2),
    ybottom.rectangle = 0,
    xright.rectangle = seq(1.5, 15.5, 2),
    ytop.rectangle = 30,
    col.rectangle = 'lightgrey',
    col = sex.colours,
    legend = list(
        inside = list(
            fun = draw.key,
            args = list(
                key = list(
                    points = list(
                        col = 'black',
                        pch = 22,
                        cex = 3,
                        fill = c('dodgerblue', 'pink')
                        ),
                    text = list(
                        lab = c('Male','Female')
                        ),
                    padding.text = 5,
                    cex = 1
                    )
                ),
                # Positioning legend on plot
                x = 0.75,
                y = 0.95
            )
        ),
    description = 'Barplot created by BoutrosLab.plotting.general',
    resolution = 200
    );

# Raster
create.barplot(
    # filename = tempfile(pattern = 'Barplot_with_raster', fileext = '.tiff'),
    formula = values ~ samples,
    data = barplot.data[barplot.data$mutation == 'nonsynonymous',],
    main = 'Raster fill',
    xlab.lab = 'Samples',
    ylab.lab = 'Nonsynonymous SNVs',
    ylimits = c(0,30),
    yat = seq(0,30,5),
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    # filling bars with raster
    raster = 1:10/10,
    raster.just = 'bottom',
    description = 'Description of image here',
    resolution = 200
    );

# Nature format
create.barplot(
    # filename = tempfile(pattern = 'Barplot_Nature_style', fileext = '.tiff'),
    formula = x ~ y,
    data = simple.data,
    yat = seq(0,16,2),
    main = 'Nature style',

    # set style to Nature
    style = 'Nature',

    # demonstrating how to italicize character variables
    ylab.lab = expression(paste('italicized ', italic('a'))),

    # demonstrating how to create en-dashes
    xlab.lab = expression(paste('en dashs: 1','\u2013', '10'^'\u2013', ''^3)),

    resolution = 200
    );

# Left Justified Example
create.barplot(
    # filename = tempfile(pattern = 'Barplot_TwoTopLabelsLeftJustified', fileext = '.tiff'),
    formula = x ~ y,
    data = simple.data,
    yat = seq(0,16,2),
    ylab.label = NULL,
    # set top label details
    xlab.top.label = 'Sample Label',
    xlab.top.cex = 1.5,
    xlab.top.x = -0.125,
    xlab.top.y = 0.5,
    xlab.top.just = 'left',
    # set main label details
    main = 'Sample Main',
    main.just = 'left',
    main.x = 0,
    main.y = 0.6,
    top.padding = 1,
    resolution = 200
    );

Make a boxplot

Description

Takes a data.frame and creates a boxplot

Usage

create.boxplot(
	formula,
	data,
	filename = NULL,
	main = NULL,
	main.just = 'center',
	main.x = 0.5,
	main.y = 0.5,
	main.cex = 3,
	add.stripplot = FALSE,
	jitter.factor = 1,
	jitter.amount = NULL,
	points.pch = 19,
	points.col = 'darkgrey',
	points.cex = 0.5,
	points.alpha = 1,
	abline.h = NULL,
	abline.v = NULL,
	abline.lty = NULL,
	abline.lwd = NULL,
	abline.col = 'black',
	add.rectangle = FALSE,
	xleft.rectangle = NULL,
	ybottom.rectangle = NULL,
	xright.rectangle = NULL,
	ytop.rectangle = NULL,
	col.rectangle = 'transparent',
	alpha.rectangle = 1,
	box.ratio = 1,
	col = 'transparent',
	alpha = 1,
	border.col = 'black',
	symbol.cex = 0.8,
	lwd = 1,
	outliers = TRUE,
	sample.order = 'none',
	order.by = 'median',
	xlab.label = tail(sub('~', '', formula[-2]), 1),
	ylab.label = tail(sub('~', '', formula[-3]), 1),
	xlab.cex = 2,
	ylab.cex = 2,
	xlab.col = 'black',
	ylab.col = 'black',
	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,
	xlimits = NULL,
	ylimits = NULL,
	xat = TRUE,
	yat = TRUE,
	xaxis.lab = TRUE,
	yaxis.lab = TRUE,
	xaxis.cex = 1.5,
	yaxis.cex = 1.5,
	xaxis.col = 'black',
	yaxis.col = 'black',
	xaxis.fontface = 'bold',
	yaxis.fontface = 'bold',
	xaxis.rot = 0,
	yaxis.rot = 0,
	xaxis.tck = c(1,0),
	yaxis.tck = 1,
	layout = NULL,
	as.table = FALSE,
	x.spacing = 0,
	y.spacing = 0,
	x.relation = 'same',
	y.relation = 'same',
	top.padding = 0.5,
	bottom.padding = 2,
	right.padding = 1,
	left.padding = 2,
	ylab.axis.padding = 0,
	add.text = FALSE,
	text.labels = NULL,
	text.x = NULL,
	text.y = NULL,
	text.anchor = 'centre',
	text.col = 'black',
	text.cex = 1,
	text.fontface = 'bold',
	key = NULL,
	legend = NULL,
	strip.col = 'white',
	strip.cex = 1,
	strip.fontface = 'bold',
	line.func = NULL,
	line.from = 0,
	line.to = 0,
	line.col = 'transparent',
	line.infront = TRUE,
	height = 6,
	width = 6,
	size.units = 'in',
	resolution = 1600,
	enable.warnings = FALSE,
	description = 'Created with BoutrosLab.plotting.general',
	style = 'BoutrosLab',
	preload.default = 'custom',
        use.legacy.settings = FALSE,
	disable.factor.sorting = FALSE
	);

Arguments

.

,

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 histogram. In particular, if a script that uses such a call of create histogram 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
    

Author(s)

Maud H.W. Starmans

See Also

bwplot, lattice or the Lattice book for an overview of the package.

Examples

set.seed(12345);

simple.data <- data.frame(
    x = rnorm(1000),
    y = rep('A',1000)
    );

create.boxplot(
    # filename = tempfile(pattern = 'Boxplot_Simple', fileext = '.tiff'),
    formula = y ~ x,
    data = simple.data,
    main = 'Simple',
    description = 'Boxplot created by BoutrosLab.plotting.general',
    resolution = 50
    );

# add stripplot behind boxplot
create.boxplot(
    # filename = tempfile(pattern = 'Boxplot_with_Stripplot', fileext = '.tiff'),
    formula = y ~ x,
    data = simple.data,
    main = 'With Stripplot',
    add.stripplot = TRUE,
    description = 'Boxplot created by BoutrosLab.plotting.general',
    resolution = 50
    );

# Multi-coloured stripplot
strip.data <- data.frame(
    score = c(rnorm(30, 15, 3), rnorm(50, 20, 4)),
    sex = sample(c('male', 'female'), 80, replace = TRUE),
    gene = sample(c('a', 'b'), 80, replace = TRUE)
    );

create.boxplot(
    filename = NULL,
    formula = score ~ sex | gene,
    data = strip.data,
    main = 'Multi-Coloured Stripplot',
    add.stripplot = TRUE,
    points.col = c('pink', 'dodgerblue'),
    description = 'Boxplot created by BoutrosLab.plotting.general',
    resolution = 100
    );


# format data
reformatted.data <- data.frame(
    x = as.vector(t(microarray[1:10,1:58])),
    y = as.factor(rep(rownames(microarray[1:10,1:58]),each = 58)),
    z = sample(1:10, 580, replace = TRUE)
    );

# Minimal Input
create.boxplot(
    # filename = tempfile(pattern = 'Boxplot_Minimal_Input', fileext = '.tiff'),
    formula = y ~ x,
    data = reformatted.data,
    main = 'Minimal input',
    description = 'Boxplot created by BoutrosLab.plotting.general',
    resolution = 50
    );

# Minimal Input
create.boxplot(
    # filename = tempfile(pattern = 'Boxplot_Disable_Factor_Sorting_Input', fileext = '.tiff'),
    formula = y ~ x,
    data = reformatted.data,
    main = 'No Factor Sorting',
    disable.factor.sorting = TRUE,
    description = 'Boxplot created by BoutrosLab.plotting.general',
    resolution = 50
    );

# Axes and labels
create.boxplot(
    # filename = tempfile(pattern = 'Boxplot_Axes_Labels', fileext = '.tiff'),
    formula = y ~ x,
    data = reformatted.data,
    main = 'Axes & labels',
    # Adjusting axes size
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    # Adding y-axis label
    ylab.label = 'Gene',
    # setting axes limits
    xlimits = c(0,13),
    xat = seq(0,12,2),
    description = 'Boxplot created by BoutrosLab.plotting.general',
    resolution = 100
    );


# Sorting
create.boxplot(
    # filename = tempfile(pattern = 'Boxplot_Sorted', fileext = '.tiff'),
    formula = y ~ x,
    data = reformatted.data,
    main = 'Sorting',
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    ylab.label = 'Gene',
    xlimits = c(0,13),
    xat = seq(0,12,2),
    # Reordered by median
    sample.order = 'increasing',
    description = 'Boxplot created by BoutrosLab.plotting.general',
    resolution = 100
    );

# Colour change
sex.colour <- as.character(patient$sex);
sex.colour[sex.colour == 'male'] <- 'dodgerblue';
sex.colour[sex.colour == 'female'] <- 'pink';

create.boxplot(
    # filename = tempfile(pattern = 'Boxplot_Colour_Change', fileext = '.tiff'),
    formula = y ~ x,
    data = reformatted.data,
    main = 'Colour change',
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    ylab.label = 'Gene',
    xlimits = c(0,13),
    xat = seq(0,12,2),
    # Colour change
    col = sex.colour,
    description = 'Boxplot created by BoutrosLab.plotting.general',
    resolution = 100
    );
    
# Remove y-axis labels
create.boxplot(
    formula = y ~ x,
    data = reformatted.data,
    main = 'Remove y-axis labels',
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    ylab.label = 'Gene',
    xlimits = c(0,13),
    xat = seq(0,12,2),
    yaxis.lab = NULL, # Remove labels with NULL
    # Colour change
    col = sex.colour,
    description = 'Boxplot created by BoutrosLab.plotting.general',
    resolution = 100
    );

# Log Scaled Axis
log.data <- data.frame(
    x = 10 ** rnorm(1000, 5, 2),
    y = rep('A',1000)
    );

create.boxplot(
    formula = x ~ y,
    data = log.data,
    # Log base 10 scale y axis
    yat = 'auto.log',
    main = 'Log Scale',
    description = 'Boxplot created by BoutrosLab.plotting.general',
    resolution = 100
    );

# Legend
create.boxplot(
    # filename = tempfile(pattern = 'Boxplot_Legend', fileext = '.tiff'),
    formula = y ~ x,
    data = reformatted.data,
    main = 'Legend',
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    ylab.label = 'Gene',
    xlimits = c(0,13),
    xat = seq(0,12,2),
    col = sex.colour,
    # legend
    legend = list(
        inside = list(
            fun = draw.key,
            args = list(
                key = list(
                    points = list(
                        col = 'black',
                        pch = 22,
                        cex = 1.5,
                        fill = c('dodgerblue','pink')
                        ),
                    text = list(
                        lab = c('male','female')
                        ),
                    cex = 1
                    )
                ),
            x = 0.03,
            y = 0.97,
            corner = c(0,1),
            draw = FALSE
            )
        ),
    description = 'Boxplot created by BoutrosLab.plotting.general',
    resolution = 100
    );

# Orientation
create.boxplot(
    # filename = tempfile(pattern = 'Boxplot_Orientation', fileext = '.tiff'),
    # switch the order
    formula = x ~ y,
    data = reformatted.data,
    main = 'Orientation',
    xaxis.cex = 1,
    yaxis.cex = 1,
    # adjust the axes
    ylimits = c(0,13),
    yat = seq(0,12,2),
    # rotate the labels
    xaxis.rot = 90,
    xlab.label = 'Gene',
    xlab.cex = 1.5,
    col = sex.colour,
    # legend
    legend = list(
        inside = list(
            fun = draw.key,
            args = list(
                key = list(
                    points = list(
                        col = 'black',
                        pch = 22,
                        cex = 1.5,
                        fill = c('dodgerblue','pink')
                        ),
                    text = list(
                        lab = c('male','female')
                        ),
                    cex = 1
                    )
                ),
            x = 0.23,
            y = 0.97,
            corner = c(0,1),
            draw = FALSE
            )
        ),
    description = 'Boxplot created by BoutrosLab.plotting.general',
    resolution = 200
    );

# Background rectangle
create.boxplot(
    # filename = tempfile(pattern = 'Boxplot_BG_Rect', fileext = '.tiff'),
    formula = y ~ x,
    data = reformatted.data,
    main = 'Bg rectangle',
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    ylab.label = 'Gene',
    xlimits = c(0,13),
    xat = seq(0,12,2),
    # draw rectangle
    add.rectangle = TRUE,
    xleft.rectangle = 0,
    xright.rectangle = 13,
    ybottom.rectangle = seq(0.5, 8.5, 2),
    ytop.rectangle = seq(1.5, 9.5, 2),
    col.rectangle = 'grey',
    alpha.rectangle = 0.5,
    col = sex.colour,
    # legend
    legend = list(
        inside = list(
            fun = draw.key,
            args = list(
                key = list(
                    points = list(
                        col = 'black',
                        pch = 22,
                        cex = 1.5,
                        fill = c('dodgerblue','pink')
                        ),
                    text = list(
                        lab = c('male','female')
                        ),
                    cex = 1
                    )
                ),
            x = 0.03,
            y = 0.97,
            corner = c(0,1),
            draw = FALSE
            )
        ),
    description = 'Boxplot created by BoutrosLab.plotting.general',
    resolution = 200
    );

# Line
create.boxplot(
    # filename = tempfile(pattern = 'Boxplot_Line', fileext = '.tiff'),
    formula = y ~ x,
    data = reformatted.data,
    main = 'Line',
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    ylab.label = 'Gene',
    xlimits = c(0,13),
    xat = seq(0,12,2),
    # draw line
    line.func = function(x){c(0.5, 10.5)},
    line.from = 11,
    line.to = 11,
    line.col = 'grey',
    description = 'Boxplot created by BoutrosLab.plotting.general',
    resolution = 200
    );

# Panel Organization
create.boxplot(
    # filename = tempfile(pattern = 'Boxplot_Panels_numeric_conditioning', fileext = '.tiff'),
    formula = ~ x | z,
    data = reformatted.data,
    main = 'Panels',
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlimits = c(0,13),
    xat = seq(0,12,2),
    # Setting up the layout
    layout = c(2,5),
    x.relation = 'free',
    x.spacing = 1,
    description = 'Boxplot created by BoutrosLab.plotting.general',
    resolution = 200
    );

create.boxplot(
    # filename = tempfile(pattern = 'Boxplot_Panels_factor_conditioning', fileext = '.tiff'),
    formula = ~ x | y,
    data = reformatted.data,
    main = 'Panels',
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlimits = c(0,13),
    xat = seq(0,12,2),
    # Setting up the layout
    layout = c(2,5),
    x.relation = 'free',
    x.spacing = 1,
    description = 'Boxplot created by BoutrosLab.plotting.general',
    resolution = 200
    );

# Nature format
create.boxplot(
    # filename = tempfile(pattern = 'Boxplot_Nature_style', fileext = '.tiff'),
    formula = y ~ x,
    data = reformatted.data,
    main = 'Nature style',
    xaxis.cex = 1,
    yaxis.cex = 1,

    # set style to Nature
    style = 'Nature',

    # demonstrating how to italicize character variables
    ylab.lab = expression(paste('italicized ', italic('a'))),

    # demonstrating how to create en-dashes
    xlab.lab = expression(paste('en dashs: 1','\u2013', '10'^'\u2013', ''^3)),
    resolution = 1200
    );

# Sorting by mean and multiple filenames
create.boxplot(
    filename = c(
        tempfile(pattern = 'Boxplot_Sorted1', fileext = '.tiff'),
        tempfile(pattern = 'Boxplot_Sorted2', fileext = '.tiff')
        ),
    formula = y ~ x,
    data = reformatted.data,
    main = 'Sorting',
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    ylab.label = 'Gene',
    xlimits = c(0,13),
    xat = seq(0,12,2),
    # Reordered by median
    sample.order = 'increasing',
    order.by = 'mean',
    description = 'Boxplot created by BoutrosLab.plotting.general',
    resolution = 200
    );


# Adding text to plot
# Generate normally distributed variables with two different means
set.seed(779);
groupA <- rnorm(n = 100, mean = 10, sd = 2);
groupB <- rnorm(n = 134, mean = 10.5, sd = 2);

# Create data frame for plotting
to.plot <- data.frame(
	y = rep(
		c('1', '2'),
		times = c(100, 134)
		),
	x = c(groupA, groupB)
	);

# Get difference between means
diff.mean <- round(mean(groupB) - mean(groupA), 2);

# Plot and display difference
create.boxplot(
	formula = x ~ y,
	# filename = tempfile(pattern = 'boxplot_with_text', fileext = '.tiff'),
	data = to.plot,
	add.stripplot = TRUE,
	add.text = TRUE,
	text.labels = bquote(mu[B] - mu[A] == .(diff.mean)),
	text.x = 2.1,
	text.y = 15.3,
	text.col = 'black',
	text.cex = 1.5,
	text.fontface = 'bold',
	ylimits = c(
		min(to.plot$x) - abs(min(to.plot$x) * 0.1),
		max(to.plot$x) + abs(max(to.plot$x) * 0.1)
		),
	resolution = 200
    );

Create Colourkey

Description

A function for generating and placing a colour key. Good for use in multiplots when a smaller colour key is desired.

Usage

create.colourkey(
	x,
	scale.data = FALSE,
	colour.scheme = c(),
	total.colours = 99,
	colour.centering.value = 0,
	colour.alpha = 1,
	fill.colour = 'darkgray',
	at = NULL,
	colourkey.labels.at = NULL,
	colourkey.labels = colourkey.labels.at,
	colourkey.labels.cex = 1,
	placement = NULL
	);

Arguments

Value

Returns a key in the format specified in the xyplot documentation.

Author(s)

Stephenie Prokopec

See Also

xyplot, plotmath

Examples

set.seed(1234567890);
x <- outer(-5:5, -5:5, '*') + matrix(nrow = 11, ncol = 11, data = runif(11 * 11));
colnames(x) <- paste('col', 1:11, sep = '-');
rownames(x) <- paste('row', 1:11, sep = '-');

y <- as.data.frame(x);
y$mean <- apply(x,1,mean);

# example of a simple multiplot with colourkey
heatmap1 <- create.heatmap(
    x = t(x),
    filename = NULL,
    clustering.method = 'none',
    scale.data = FALSE,
    yaxis.lab = NA,
    print.colour.key = FALSE,
    colour.scheme = c('chartreuse3', 'white', 'blue'),
    at = seq(-25, 25, 0.01)
    );

barplot1 <- create.barplot(
    1:nrow(y) ~ mean,
    y,
    plot.horizontal = TRUE
    );

create.multiplot(
    plot.objects = list(heatmap1, barplot1),
    # filename = tempfile(pattern = 'multiplot_with_colourkey', fileext = '.tiff'),
    plot.layout = c(2,1),
    panel.widths = c(2,1),
    yat = list(1:nrow(y), NULL),
    yaxis.labels = rownames(y),
    xlimits = list(NULL, c(0,1)),
    xat = list(NULL, seq(0,1,0.5)),
    xaxis.labels = list(NULL, seq(0,1,0.5)),
    x.spacing = 0,
    print.new.legend = TRUE,
    legend = list(
        inside = list(
            fun = BoutrosLab.plotting.general::create.colourkey(
                x = x,
                colour.scheme = c('chartreuse3', 'white', 'blue'),
                at = seq(-25, 25, 0.01),
                colourkey.labels.at = c(-25, 0, 25),
                placement = viewport(just = 'left', x = 0.55, y = -0.55, width = 0.5)
                )
            )
        ),
    bottom.padding = 4,
    width = 10,
    height = 8,
    resolution = 500
    );

Generate a dendrogram

Description

Takes a matrix and creates a row-wise or column-wise dendrogram

Usage

create.dendrogram(
	x,
	clustering.method = 'diana',
	cluster.dimension = 'col',
	distance.method = 'correlation',
	cor.method = 'pearson',
	force.clustering = FALSE,
	same.as.matrix = FALSE
	);

Arguments

Value

Returns an object of the dendrogram class corresponding to the row-wise or column-wise dendrogram for x

Author(s)

Lauren Chong

Examples

# create temp data
x <- outer(-5:5, -5:5, '*') + matrix(nrow = 11, ncol = 11, data = runif(11 * 11));
colnames(x) <- paste('col', 1:11, sep = '-');
rownames(x) <- paste('row', 1:11, sep = '-');

# example of generating a column-wise dendrogram using default values
create.dendrogram(
    x = x
    );

# example of generating a column-wise dendrogram using different distance and clustering methods
create.dendrogram(
    x = x,
    clustering.method = 'median',
    cluster.dimension = 'cols',
    distance.method = 'euclidean'
    );

# generate row-wise dendrogram using default distance and clustering methods
create.dendrogram(
    x = x,
    cluster.dimension = 'row'
    );

# generate row-wise dendrogram using different distance and clustering methods
create.dendrogram(
    x = x,
    clustering.method = 'ward',
    cluster.dimension = 'rows',
    distance.method = 'manhattan'
    );

Make a density plot

Description

Takes a list of vectors and creates a density-plot with each vector as a separate curve

Usage

create.densityplot(
	x,
	filename = NULL,
	main = NULL,
	main.just = 'center',
	main.x = 0.5,
	main.y = 0.5,
	main.cex = 3,
	xlab.label = NULL,
	ylab.label = 'Density',
	xlab.cex = 2,
	ylab.cex = 2,
	xlab.col = 'black',
	ylab.col = 'black',
	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,
	type = 'l',
	lty = 'solid',
	cex = 0.75,
	pch = 19,
	col = 'black',
	lwd = 2,
	bandwidth = 'nrd0',
	bandwidth.adjust = 1,
	xlimits = NULL,
	ylimits = NULL,
	xat = TRUE,
	yat = TRUE,
	xaxis.lab = NA,
	yaxis.lab = NA,
	xaxis.cex = 1.5,
	yaxis.cex = 1.5,
	xaxis.rot = 0,
	yaxis.rot = 0,
	xaxis.col = 'black',
	yaxis.col = 'black',
	xaxis.fontface = 'bold',
	yaxis.fontface = 'bold',
	xaxis.tck = 1,
	yaxis.tck = 1,
	xgrid.at = xat,
	ygrid.at = yat,
	key = list(text = list(lab = c(''))),
	legend = NULL,
	top.padding = 0.1,
	bottom.padding = 0.7,
	left.padding = 0.5,
	right.padding = 0.1,
	add.axes = FALSE,
	abline.h = NULL,
	abline.v = NULL,
	abline.lty = NULL,
	abline.lwd = NULL,
	abline.col = 'black',
	add.rectangle = FALSE,
	xleft.rectangle = NULL,
	ybottom.rectangle = NULL,
	xright.rectangle = NULL,
	ytop.rectangle = NULL,
	col.rectangle = 'transparent',
	alpha.rectangle = 1,
	add.text = FALSE, 
	text.labels = NULL, 
	text.x = NULL, 
	text.y = NULL, 
	text.anchor = "centre", 
	text.col = "black", 
	text.cex = 1,
        text.fontface = "bold",
	height = 6,
	width = 6,
	size.units = 'in',
	resolution = 1600,
	enable.warnings = FALSE,
	description = 'Created with BoutrosLab.plotting.general',
	style = 'BoutrosLab',
	preload.default = 'custom',
        use.legacy.settings = FALSE,
	inside.legend.auto = FALSE
	);

Arguments

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 histogram. In particular, if a script that uses such a call of create histogram 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
    

Author(s)

Paul C. Boutros

See Also

xyplot, lattice or the Lattice book for an overview of the package.

Examples

set.seed(12345);

simple.data <- data.frame(
    x = rnorm(1000),
    y = rnorm(1000, mean = 3, sd = 3)
    );

create.densityplot(
    # filename = tempfile(pattern = 'Densityplot_Simple', fileext = '.tiff'),
    x = simple.data,
    main = 'Simple',
    description = 'Barplot created by BoutrosLab.plotting.general'
    );

# format data
format.data <- microarray[1:3,1:58];
format.data <- as.data.frame(t(format.data));

# Minimal Input
create.densityplot(
    # filename = tempfile(pattern = 'Densityplot_Minimal_Input', fileext = '.tiff'),
    x = format.data,
    main = 'Minimal input',
    description = 'Barplot created by BoutrosLab.plotting.general',
    resolution = 50
    );

# Line type
create.densityplot(
    # filename = tempfile(pattern = 'Densityplot_Line_Type', fileext = '.tiff'),
    x = format.data,
    main = 'Line type',
    # Line type
    lty = c('solid','dashed','dotted'),
    description = 'Barplot created by BoutrosLab.plotting.general',
    resolution = 50
    );

# Axes & Labels
create.densityplot(
    # filename = tempfile(pattern = 'Densityplot_Axes_Labels', fileext = '.tiff'),
    x = format.data,
    main = 'Axes & labels',
    lty = c('solid','dashed','dotted'),
    # Axes & Labels
    ylimits = c(-0.1, 2.5),
    ylab.cex = 1.5,
    xat = seq(0, 13, 1),
    description = 'Barplot created by BoutrosLab.plotting.general',
    resolution = 50
    );


# Colour change & Legend
create.densityplot(
    # filename = tempfile(pattern = 'Densityplot_Colour_Legend', fileext = '.tiff'),
    x = format.data,
    main = 'Colour & legend',
    lty = c('solid','dashed','dotted'),
    ylimits = c(-0.1, 2.5),
    ylab.cex = 1.5,
    # Colours
    col = default.colours(3),
    # Legend
    legend = list(
        inside = list(
            fun = draw.key,
            args = list(
                key = list(
                    points = list(
                        col = default.colours(3),
                        pch = 21,
                        cex = 1.5,
                        fill = default.colours(3)
                        ),
                    text = list(
                        lab = colnames(format.data)
                        ),
                    padding.text = c(0,5,0),
                    cex = 1
                    )
                ),
            x = 0.65,
            y = 0.97,
            draw = FALSE
            )
        ),
    description = 'Barplot created by BoutrosLab.plotting.general',
    resolution = 50
    );

# Correlation key
create.densityplot(
    # filename = tempfile(pattern = 'Densityplot_Correlation_Key', fileext = '.tiff'),
    x = format.data[,1:2],
    main = 'Correlation key',
    lty = c('solid','dotted'),
    ylimits = c(-0.1, 2.5),
    ylab.cex = 1.5,
    col = default.colours(2),
    legend = list(
        inside = list(
            fun = draw.key,
            args = list(
                key = list(
                    points = list(
                        col = default.colours(2),
                        pch = 21,
                        cex = 1.5,
                        fill = default.colours(2)
                        ),
                    text = list(
                        lab = colnames(format.data)[1:2]
                        ),
                    padding.text = c(0,5,0),
                    cex = 1
                    )
                ),
            x = 0.65,
            y = 0.97,
            draw = FALSE
            ),
        # Correlation key accepts two vectors
        inside = list(
            fun = draw.key,
            args = list(
                key = get.corr.key(
                    x = as.numeric(format.data[,1]),
                    y = as.numeric(format.data[,2]),
                    label.items = c('pearson','beta1'),
                    alpha.background = 1,
                    key.cex = 1.2
                    )
                ),
            x = 0.65,
            y = 0.85,
            corner = c(0,1)
            )
        ),
    description = 'Barplot created by BoutrosLab.plotting.general',
    resolution = 200
    );

# Gridlines
create.densityplot(
    # filename = tempfile(pattern = 'Densityplot_Gridlines_1', fileext = '.tiff'),
    x = format.data,
    main = 'Gridlines',
    lty = c('solid','dashed','dotted'),
    ylimits = c(-0.1, 2.5),
    ylab.cex = 1.5,
    col = default.colours(3),
    legend = list(
        inside = list(
            fun = draw.key,
            args = list(
                key = list(
                    points = list(
                        col = default.colours(3),
                        pch = 21,
                        cex = 1.5,
                        fill = default.colours(3)
                        ),
                    text = list(
                        lab = colnames(format.data)
                        ),
                    padding.text = c(0,5,0),
                    cex = 1
                    )
                ),
            x = 0.65,
            y = 0.97,
            draw = FALSE
            )
        ),
    # Grid lines
    type = c('l','g'),
    description = 'Barplot created by BoutrosLab.plotting.general',
    resolution = 200
    );

# Gridlines
create.densityplot(
    # filename = tempfile(pattern = 'Densityplot_Gridlines_2', fileext = '.tiff'),
    x = format.data,
    main = 'Gridlines',
    lty = c('solid','dashed','dotted'),
    ylimits = c(-0.1, 2.5),
    ylab.cex = 1.5,
    col = default.colours(3),
    legend = list(
        inside = list(
            fun = draw.key,
            args = list(
                key = list(
                    points = list(
                        col = default.colours(3),
                        pch = 21,
                        cex = 1.5,
                        fill = default.colours(3)
                        ),
                    text = list(
                        lab = colnames(format.data)
                        ),
                    padding.text = c(0,5,0),
                    cex = 1
                    )
                ),
            x = 0.65,
            y = 0.97,
            draw = FALSE
            )
        ),
    # Grid lines
    type = c('l','g'),
    xgrid.at = seq(0,14,1),
    ygrid.at = seq(0,2.5,0.25),
    description = 'Barplot created by BoutrosLab.plotting.general',
    resolution = 200
    );

# Nature style
create.densityplot(
    # filename = tempfile(pattern = 'Densityplot_Nature_style', fileext = '.tiff'),
    x = format.data,
    main = 'Nature style',
    lty = c('solid','dashed','dotted'),
    ylimits = c(-0.1, 2.5),
    ylab.cex = 1.5,
    xlab.cex = 1.5,
    col = default.colours(3),
    legend = list(
        inside = list(
            fun = draw.key,
            args = list(
                key = list(
                    points = list(
                        col = default.colours(3),
                        pch = 21,
                        cex = 1.5,
                        fill = default.colours(3)
                        ),
                    text = list(
                        lab = colnames(format.data)
                        ),
                    padding.text = c(0,5,0),
                    cex = 1
                    )
                ),
            x = 0.65,
            y = 0.97,
            draw = FALSE
            )
        ),
    # Grid lines
    style = 'Nature',

    # demonstrating how to italicize character variables
    ylab.lab = expression(paste('italicized ', italic('a'))),
  
    # demonstrating how to create en-dashes
    xlab.lab = expression(paste('en dashs: 1','\u2013', '10'^'\u2013', ''^3)),
    resolution = 200
    );

Make a dotmap with coloured background

Description

Takes two data.frames and creates a dotmap with a coloured background. A dotmap is an ordered array of evenly-spaced dots whose size and colour can be user-specified to represent characteristics. For example, size gives the absolute magnitude of the correlation and colour gives the sign of the correlation. The coloured background may indicate p-values.

Usage

create.dotmap(
	x, 
	bg.data = NULL, 
	filename = NULL, 
	main = NULL, 
	main.just = "center", 
	main.x = 0.5, 
	main.y = 0.5, 
	pch = 19, 
	pch.border.col = 'black', 
	add.grid = TRUE, 
	xaxis.lab = colnames(x), 
	yaxis.lab = rownames(x), 
	xaxis.rot = 0, 
	yaxis.rot = 0, 
	main.cex = 3, 
	xlab.cex = 2, 
	ylab.cex = 2, 
	xlab.label = NULL, 
	ylab.label = NULL, 
	xlab.col = 'black', 
	ylab.col = 'black', 
	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, 
	xaxis.cex = 1.5, 
	yaxis.cex = 1.5, 
	xaxis.col = 'black', 
	yaxis.col = 'black', 
	xaxis.tck = 1, 
	yaxis.tck = 1, 
	axis.top = 1, 
	axis.bottom = 1, 
	axis.left = 1, 
	axis.right = 1, 
	top.padding = 0.1, 
	bottom.padding = 0.7, 
	right.padding = 0.1, 
	left.padding = 0.5, 
	key.ylab.padding = 0.1,
	key = list(text = list(lab = c(''))), 
	legend = NULL, 
	col.lwd = 1.5, 
	row.lwd = 1.5, 
	spot.size.function = 'default', 
	spot.colour.function = 'default', 
	na.spot.size = 7, 
	na.pch = 4, 
	na.spot.size.colour = 'black',
	grid.colour = NULL, 
	colour.scheme = 'white', 
	total.colours = 99, 
	at = NULL, 
	colour.centering.value = 0, 
	colourkey = FALSE, 
	colourkey.labels.at = NULL, 
	colourkey.labels = NULL, 
	colourkey.cex = 1,
	colour.alpha = 1, 
	bg.alpha = 0.5, 
	fill.colour = 'white', 
	key.top = 0.1, 
	height = 6, 
	width = 6, 
	size.units = 'in', 
	resolution = 1600, 
	enable.warnings = FALSE, 
	col.colour = 'black', 
	row.colour = 'black', 
	description = 'Created with BoutrosLab.plotting.general',
	add.rectangle = FALSE, 
	xleft.rectangle = NULL, 
	ybottom.rectangle = NULL, 
	xright.rectangle = NULL, 
	ytop.rectangle = NULL, 
	col.rectangle = 'transparent', 
	border.rectangle = NULL,
	lwd.rectangle = NULL,
	alpha.rectangle = 1,
	xaxis.fontface = 'bold', 
	yaxis.fontface = 'bold', 
	dot.colour.scheme = NULL, 
	style = 'BoutrosLab',
	preload.default = 'custom',
        use.legacy.settings = FALSE,
	remove.symmetric = FALSE,
        lwd = 2
);

Arguments

Details

It would be nice to have a library of suitable spot.size and spot.colour functions.

    Earlier ideas included: 
    (1) Changing the dot shape to triangles, so that upward or downward-pointing dots indicated direction of change. This would allow dot colour to be used to encode something else. This idea was not used because in the case of very small dots, the direction of the triangle might not be visible. 
    (2) Adding arrows above or below dots to indicate direction of change. This idea was not used because there may not always be enough space present to add such arrows. 
    (3) Adding line(s) in the background set at different angles to show data. This was found to be not intuitive to read.

A future addition may be to add the option of outlining boxes instead of adding a background. This would be applicable in cases where there is very little background space, and consequently, the background colour would not be very visible.
    

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 histogram. In particular, if a script that uses such a call of create histogram 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
    

Author(s)

Paul C. Boutros

See Also

xyplot, levelplot, lattice or the Lattice book for an overview of the package.

Examples

set.seed(12345);
simple.data <- data.frame(
    'A' = runif(n = 15, min = -1, max = 1),
    'B' = runif(n = 15, min = -1, max = 1),
    'C' = runif(n = 15, min = -1, max = 1),
    'D' = runif(n = 15, min = -1, max = 1),
    'E' = runif(n = 15, min = -1, max = 1)
    );

create.dotmap(
    # filename = tempfile(pattern = 'Dotmap_Simple', fileext = '.tiff'),
    x = simple.data,
    main = 'Simple',
    description = 'Dotmap created by BoutrosLab.plotting.general',
    resolution = 100
    );

# create a function to determine the spot sizes (default function works best with values < 1)
spot.size.med <- function(x) {abs(x)/3;}

# Minimal Input
create.dotmap(
    # filename = tempfile(pattern = 'Dotmap_Minimal_Input', fileext = '.tiff'),
    x = microarray[1:5,1:5],
    main = 'Minimal input',
    spot.size.function = spot.size.med,
    xaxis.rot = 90,
    description = 'Dotmap created by BoutrosLab.plotting.general',
    resolution = 100
    );

# Axes & Labels
spot.size.small <- function(x) {abs(x)/5;}

create.dotmap(
    # filename = tempfile(pattern = 'Dotmap_Axes_Labels', fileext = '.tiff'),
    x = microarray[1:15,1:15],
    main = 'Axes & labels',
    spot.size.function = spot.size.small,
    # Adjusting the font sizes and labels
    xaxis.cex = 0.8,
    yaxis.cex = 0.8,
    xaxis.lab = 1:15,
    xlab.label = 'Sample',
    ylab.label = 'Gene',
    xlab.cex = 1,
    ylab.cex = 1,
    description = 'Dotmap created by BoutrosLab.plotting.general',
    resolution = 100
    );

# Legend
key.sizes <- seq(2,12,2);

create.dotmap(
    # filename = tempfile(pattern = 'Dotmap_Legend', fileext = '.tiff'),
    x = microarray[1:15,1:15],
    main = 'Legend',
    spot.size.function = spot.size.small,
    xaxis.cex = 0.8,
    yaxis.cex = 0.8,
    xaxis.lab = 1:15,
    xlab.label = 'Sample',
    ylab.label = 'Gene',
    xlab.cex = 1,
    ylab.cex = 1,
    # Legend for dots
    key = list(
        space = 'right',
        points = list(
            cex = spot.size.small(key.sizes),
            col = default.colours(2, palette.type = 'dotmap')[2],
            pch = 19
            ),
        text = list(
            lab = as.character(key.sizes),
            cex = 1,
            adj = 1
            ),
        padding.text = 3,
        background = 'white'
        ),
    key.top = 1,
    description = 'Dotmap created by BoutrosLab.plotting.general',
    resolution = 100
    );


# Cluster by dots and add dendrogram
plot.data <- microarray[1:15,1:15];

# cluster data
clustered.data <- diana(plot.data);

# order data by cluster
plot.data <- plot.data[clustered.data$order,];

# create dendogram
dendrogram.data <- create.dendrogram(x = plot.data, clustering.method = 'diana',
	cluster.dimension = 'row');

dendrogram.grob <- latticeExtra::dendrogramGrob(
        x = dendrogram.data,
        side = 'right',
        type = 'rectangle'
        );

# create dotmap
create.dotmap(
    x = plot.data,
    # filename = tempfile(pattern = 'Dotmap_clustered_dendrogram', fileext = '.tiff'),
    main = 'Clustered & dendrogram',
    spot.size.function = spot.size.small,
    # Adjusting the font sizes and labels
    xaxis.cex = 0.8,
    yaxis.cex = 0.8,
    xaxis.lab = 1:15,
    xlab.label = 'Sample',
    ylab.label = 'Gene',
    xlab.cex = 1,
    ylab.cex = 1,
    legend = list(
        right = list(fun = dendrogram.grob)
        ),
    right.padding = 4,
    description = 'Dotmap created by BoutrosLab.plotting.general',
    resolution = 100
    );

# Add background data
key.sizes <- c(-1, 1);

CNA.colour.function <- function(x){
    colours <- rep('white', length(x));
    colours[sign(x) == 1] <- 'Red';
    colours[sign(x) == -1] <- 'Blue';
    colours[x == 0] <- 'transparent';
    return(colours);
    }

create.dotmap(
    # filename = tempfile(pattern = 'Dotmap_with_Background', fileext = '.tiff'),
    # added new data for the dots
    x = CNA[1:15,1:15],
    # Moving the dot-data to be background data
    bg.data = microarray[1:15,1:15],
    colour.scheme = c('white','black'),
    main = 'Background',
    spot.size.function = 1,
    spot.colour.function = CNA.colour.function,
    xaxis.cex = 0.8,
    yaxis.cex = 0.8,
    xaxis.lab = 1:15,
    xlab.label = 'Sample',
    ylab.label = 'Gene',
    xlab.cex = 1,
    ylab.cex = 1,
    key = list(
        space = 'right',
        points = list(
            cex = 1,
            col = CNA.colour.function(key.sizes),
            pch = 19
            ),
        text = list(
            lab = c('Gain', 'Loss'),
            cex = 1,
            adj = 1
            ),
        title = 'CNA',
        padding.text = 2,
        background = 'white'
        ),
    # Adding colourkey for background data
    colourkey = TRUE,
    key.top = 1,
    description = 'Dotmap created by BoutrosLab.plotting.general',
    resolution = 200
    );

# Discrete background colours
create.dotmap(
    # filename = tempfile(pattern = 'Dotmap_Discrete_Background', fileext = '.tiff'),
    x = CNA[1:15,1:15],
    bg.data = microarray[1:15,1:15],
    main = 'Discrete background',
    spot.size.function = 1,
    spot.colour.function = CNA.colour.function,
    xaxis.cex = 0.8,
    yaxis.cex = 0.8,
    xaxis.lab = 1:15,
    xlab.label = 'Sample',
    ylab.label = 'Gene',
    xlab.cex = 1,
    ylab.cex = 1,
    key = list(
        space = 'right',
        points = list(
            cex = 1,
            col = CNA.colour.function(key.sizes),
            pch = 19
            ),
        text = list(
            lab = c('Gain', 'Loss'),
            cex = 1,
            adj = 1
            ),
        title = 'CNA',
        padding.text = 2,
        background = 'white'
        ),
    colourkey = TRUE,
    key.top = 1,
    # Changing background colour scheme
    colour.scheme = c('lightyellow','gold','darkorange', 'darkorange3'),
    at = seq(0,12,3),
    colourkey.labels = seq(0,12,3),
    colourkey.labels.at = seq(0,12,3),
    bg.alpha = 0.65,
    description = 'Dotmap created by BoutrosLab.plotting.general'
    );

# Dot outlines
border.colours <- function(x){
    colours <- rep('transparent', length(x));
    colours[x > 0] <- 'black';
    colours[x == 0] <- 'transparent';
    return(colours);
    }

create.dotmap(
    # filename = tempfile(pattern = 'Dotmap_Outlined_Dots', fileext = '.tiff'),
    x = CNA[1:15,1:15],
    bg.data = microarray[1:15,1:15],
    main = 'Dot outlines',
    spot.size.function = 1,
    spot.colour.function = CNA.colour.function,
    xaxis.cex = 0.8,
    yaxis.cex = 0.8,
    xaxis.lab = 1:15,
    xlab.label = 'Sample',
    ylab.label = 'Gene',
    xlab.cex = 1,
    ylab.cex = 1,
    key = list(
        space = 'right',
        points = list(
            cex = 1,
            col = 'black',
            # Remember to also change the pch in the legend
            pch = 21,
            fill = CNA.colour.function(key.sizes)
            ),
        text = list(
            lab = c('Gain', 'Loss'),
            cex = 1,
            adj = 1
            ),
        title = 'CNA',
        padding.text = 2,
        background = 'white'
        ),
    colourkey = TRUE,
    key.top = 1,
    colour.scheme = c('lightyellow','gold','darkorange', 'darkorange3'),
    at = seq(0,12,3),
    colourkey.labels = seq(0,12,3),
    colourkey.labels.at = seq(0,12,3),
    bg.alpha = 0.65,
    # Change the plotting character to one which has an outline
    pch = 21,
    pch.border.col = border.colours(CNA[1:15,1:15]),
    description = 'Dotmap created by BoutrosLab.plotting.general'
    );

# Covariates & Legend
sex.colours <- patient$sex[1:15];
sex.colours[sex.colours == 'male'] <- 'dodgerblue';
sex.colours[sex.colours == 'female'] <- 'pink';

sample.covariate <- list(
    rect = list(
        col = 'black',
        fill = sex.colours,
        lwd = 1.5
        )
    );

cov.grob <- covariates.grob(
    covariates = sample.covariate,
    ord = c(1:15),
    side = 'top'
    );

sample.cov.legend <- list(
    legend = list(
        colours = c('dodgerblue', 'pink'),
        labels = c('male','female'),
        title = 'Sex'
        )
    );

cov.legend <- legend.grob(
    legends = sample.cov.legend
    );

create.dotmap(
    # filename = tempfile(pattern = 'Dotmap_Covariates', fileext = '.tiff'),
    x = CNA[1:15,1:15],
    bg.data = microarray[1:15,1:15],
    main = 'Covariates',
    spot.size.function = 1,
    spot.colour.function = CNA.colour.function,
    xaxis.cex = 0.8,
    yaxis.cex = 0.8,
    xaxis.lab = 1:15,
    xlab.label = 'Sample',
    ylab.label = 'Gene',
    xlab.cex = 1,
    ylab.cex = 1,
    key = list(
        space = 'right',
        points = list(
            cex = 1,
            col = 'black',
            pch = 21,
            fill = CNA.colour.function(key.sizes)
            ),
        text = list(
            lab = c('Gain', 'Loss'),
            cex = 1,
            adj = 1
            ),
        title = 'CNA',
        padding.text = 2,
        background = 'white'
        ),
    colourkey = TRUE,
    key.top = 1,
    colour.scheme = c('lightyellow','gold','darkorange', 'darkorange3'),
    at = seq(0,12,3),
    colourkey.labels = seq(0,12,3),
    colourkey.labels.at = seq(0,12,3),
    bg.alpha = 0.65,
    pch = 21,
    pch.border.col = border.colours(CNA[1:15,1:15]),
    # Insert covariates & legend
    legend = list(
        top = list(
            fun = cov.grob
            ),
        left = list( 
            fun = cov.legend
            )
        ),
    description = 'Dotmap created by BoutrosLab.plotting.general'
    );

# Side covariates with label
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
        )
    );

chr.cov.grob <- covariates.grob(
    covariates = chr.covariate,
    ord = c(1:15),
    side = 'right'
    );

# create dot legend
dot.grob <- draw.key(
    list(
        space = 'right',
         points = list(
                cex = 1,
                col = 'black',
                pch = 21,
                fill = CNA.colour.function(key.sizes)
                ),
            text = list(
                lab = c('Gain', 'Loss'),
                cex = 1,
                adj = 1
                ),
            title = 'CNA',
            padding.text = 2,
            background = 'white'
        )
    );

# Setting up the layout for the joint legends
right.layout <- grid.layout(
    nrow = 1,
    ncol = 2,
    width = unit(
        x = c(0,1),
        units = rep('lines',2)
        ),
    heights = unit(
        x = c(1,1),
        units = rep('npc', 1)
        )
    );

right.grob <- frameGrob(layout = right.layout);

right.grob <- packGrob(
    frame = right.grob,
    grob = chr.cov.grob,
    row = 1,
    col = 1
    );

right.grob <- packGrob(
    frame = right.grob,
    grob = dot.grob,
    row = 1,
    col = 2
    );

temp <- create.dotmap(
    # filename = tempfile(pattern = 'Dotmap_Covariates_Side', fileext = '.tiff'),
    x = CNA[1:15,1:15],
    bg.data = microarray[1:15,1:15],
    main = 'Both covariates',
    spot.size.function = 1,
    spot.colour.function = CNA.colour.function,
    xaxis.cex = 0.8,
    yaxis.cex = 0.8,
    xaxis.lab = 1:15,
    xlab.label = 'Sample',
    ylab.label = 'Gene',
    xlab.cex = 1,
    ylab.cex = 1,
    colourkey = TRUE,
    key.top = 1,
    colour.scheme = c('lightyellow','gold','darkorange', 'darkorange3'),
    at = seq(0,12,3),
    colourkey.labels = seq(0,12,3),
    colourkey.labels.at = seq(0,12,3),
    bg.alpha = 0.65,
    pch = 21,
    pch.border.col = border.colours(CNA[1:15,1:15]),
    # insert covariates & legend
    legend = list(
        right = list(
            fun = right.grob
            )
        ),
    description = 'Dotmap created by BoutrosLab.plotting.general'
    );

# add side label to covariate
print(temp, position = c(0,0,1,1), more = TRUE);

draw.key(
    key = list(
        text = list(
            lab = 'Covariate Label',
            cex = 1,
            adj = 1
            )
        ),
    # position label on the plot
    vp = viewport(x = 0.86, y = 0.155, height = 1, width = 0.5, angle = 90),
    draw = TRUE
    );

dev.off();

# Nature style
create.dotmap(
    # filename = tempfile(pattern = 'Dotmap_Nature_style', fileext = '.tiff'),
    x = CNA[1:15,1:15],
    bg.data = microarray[1:15,1:15],
    main = 'Nature style',
    spot.size.function = 1,
    spot.colour.function = CNA.colour.function,
    xaxis.cex = 0.8,
    yaxis.cex = 0.8,
    xaxis.lab = 1:15,
    xlab.cex = 1,
    ylab.cex = 1,
    key = list(
        space = 'right',
        points = list(
            cex = 1,
            col = 'black',
            # Remember to also change the pch in the legend
            pch = 21,
            fill = CNA.colour.function(key.sizes)
            ),
        text = list(
            lab = c('Gain', 'Loss'),
            cex = 1,
            adj = 1
            ),
        title = 'CNA',
        padding.text = 2,
        background = 'white'
        ),
    colourkey = TRUE,
    key.top = 1,
    colour.scheme = c('lightyellow','gold','darkorange', 'darkorange3'),
    at = seq(0,12,3),
    colourkey.labels = seq(0,12,3),
    colourkey.labels.at = seq(0,12,3),
    bg.alpha = 0.65,
    # Change the plotting character to one which has an outline
    pch = 21,
    pch.border.col = border.colours(CNA[1:15,1:15]),

    # set style to Nature 
    style = 'Nature',
    
    # demonstrating how to italicize character variables
    ylab.lab = expression(paste('italicized ', italic('a'))),
  
    # demonstrating how to create en-dashes
    xlab.lab = expression(paste('en dashs: 1','\u2013', '10'^'\u2013', ''^3)),
    
    resolution = 200
    );

simple.data.sym <- data.frame(
    '1' = runif(n = 7, min = -1, max = 1),
    '2' = runif(n = 7, min = -1, max = 1),
    '3' = runif(n = 7, min = -1, max = 1),
    '4' = runif(n = 7, min = -1, max = 1),
    '5' = runif(n = 7, min = -1, max = 1),
    '6' = runif(n = 7, min = -1, max = 1),
    '7' = runif(n = 7, min = -1, max = 1)
    );

create.dotmap(
    # filename = tempfile(pattern = 'Dotmap_remove_symmetric', fileext = '.tiff'),
    x = simple.data.sym,
    main = 'Simple',
    xaxis.lab = seq(1,7,1),
    description = 'Dotmap created by BoutrosLab.plotting.general',
    remove.symmetric = TRUE,
    resolution = 200
    );

Make a gif

Description

Takes a function and several sets of parameters and makes a gif of their function calls

Usage

create.gif(
	exec.func,
	parameters,
	number.of.frames,
	delay = 40,
	filename)

Arguments

Author(s)

Jeffrey Green

See Also

stripplot, lattice or the Lattice book for an overview of the package.

Examples

set.seed(223);

simple.data1 <- data.frame(
    x = sample(1:15, 10),
    y = LETTERS[1:10]
    );

simple.data2 <- data.frame(
    x = sample(1:15, 10),
    y = LETTERS[1:10]
    );

simple.data3 <- data.frame(
    x = sample(1:15, 10),
    y = LETTERS[1:10]
    );

p = list(
	list(formula = x ~ y,data = simple.data1, yat = seq(0,16,2)),
	list(formula = x ~ y,data = simple.data2, yat = seq(0,16,2)),
	list(formula = x ~ y,data = simple.data3, yat = seq(0,16,2))
	)

create.gif(
	exec.func = create.barplot,
	parameters = p,
	number.of.frames = 3,
	delay = 20,
	filename = tempfile(pattern = 'test', fileext = '.gif')
	)

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

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
    );

Make a hexagonally binned plot

Description

Takes a data.frame and writes a hexagonally binned plot

Usage

create.hexbinplot(
	formula,
	data,
	filename = NULL,
	main = NULL,
	main.just = 'center',
	main.x = 0.5,
	main.y = 0.5,
	main.cex = 3,
        aspect = 'xy',
	trans = NULL,
	inv = NULL,
	colour.scheme = NULL,
	colourkey = TRUE,
        colourcut = seq(0, 1, length = 11),
	mincnt = 1,
	maxcnt = NULL,
	xbins = 30,
	legend.title = NULL,
	xlab.label = tail(sub('~', '', formula[-2]), 1),
	ylab.label = tail(sub('~', '', formula[-3]), 1),
	xlab.cex = 2,
        ylab.cex = 2,
        xlab.col = 'black',
	ylab.col = 'black',
	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,
        xlimits = NULL,
	ylimits = NULL,
	xat = TRUE,
	yat = TRUE,
	xaxis.lab = NA,
	yaxis.lab = NA,
        xaxis.cex = 1.5,
	yaxis.cex = 1.5,
	xaxis.rot = 0,
	yaxis.rot = 0,
	xaxis.col = 'black',
        yaxis.col = 'black',
	xaxis.tck = 1,
	yaxis.tck = 1,
	xaxis.fontface = 'bold',
	yaxis.fontface = 'bold',
        layout = NULL,
	as.table = FALSE,
	x.relation = 'same',
	y.relation = 'same',
	x.spacing = 0,
	y.spacing = 0,
	strip.col = 'white',
        strip.cex = 1,
	strip.fontface = 'bold',
	add.grid = FALSE,
	abline.h = NULL,
	abline.v = NULL,
	abline.lty = NULL,
        abline.lwd = NULL,
	abline.col = 'black',
	abline.front = FALSE,
	add.xyline = FALSE,
	xyline.col = 'black',
        xyline.lwd = 1,
	xyline.lty = 1,
	add.curves = FALSE,
	curves.exprs = NULL,
	curves.from = min(data, na.rm = TRUE),
        curves.to = max(data, na.rm = TRUE),
	curves.col = 'black',
	curves.lwd = 2,
	curves.lty = 1,
        add.text = FALSE,
	text.labels = NULL,
	text.x = NULL,
	text.y = NULL,
	text.col = 'black',
	text.cex = 1,
        text.fontface = 'bold',
	add.axes = FALSE,
	top.padding = 0.1,
	bottom.padding = 0.7,
        left.padding = 0.5,
	right.padding = 0.1,
        add.rectangle = FALSE,
	xleft.rectangle = NULL,
	ybottom.rectangle = NULL,
	xright.rectangle = NULL,
        ytop.rectangle = NULL,
	col.rectangle = 'transparent',
	alpha.rectangle = 1,
        background.col = 'transparent',
	key = NULL,
	legend = NULL,
        height = 6,
	width = 6,
	size.units = 'in',
	resolution = 1600,
	enable.warnings = FALSE,
        description = 'Created with BoutrosLab.plotting.general',
	style = 'BoutrosLab',
	preload.default = 'custom',
        use.legacy.settings = FALSE,
        inside.legend.auto = FALSE
	);

Arguments

Details

WARNING: this function uses highly unusual semantics, different from the rest of the BoutrosLab.plotting.general library. The underlying hexbinplot function uses an argument called maxcnt to specify the maximum number of counts per cell. The default behaviour is not sensibly encoded via a NULL or an NA, but instead by using the missing function. As a result, we need to use do.call semantics to handle this function. This can mess up anything using substitute including things that generate p-values!

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 histogram. In particular, if a script that uses such a call of create histogram 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
If 'maxcnt' is passed, make sure it is not smaller than the actual maximum count (value depends on nbins). Otherwise, some data may be lost. If you aren't sure what the actual max count is, run this function without specifying the 'maxcnt' parameter using the desired number of bins.
    

Author(s)

Maud HW Starmans

See Also

xyplot, lattice or the Lattice book for an overview of the package.

Examples

set.seed(12345);

simple.data <- data.frame(
    x = rnorm(10000),
    y = rnorm(10000)
    );

create.hexbinplot(
    # filename = tempfile(pattern = 'Hexbinplot_Simple', fileext = '.tiff'),
    formula = y ~ x,
    data = simple.data,
    main = 'Simple',
    description = 'Hexbinplot created by BoutrosLab.plotting.general',
    resolution = 50
    );

create.hexbinplot(
    # filename = tempfile(pattern = 'Hexbinplot_Simple_underlined_legend_title', fileext = '.tiff'),
    formula = y ~ x,
    data = simple.data,
    legend.title = list(lab = expression(bold(underline('Counts'))), x = 1, y = 1.1),
    right.padding = 4,
    description = 'Hexbinplot created by BoutrosLab.plotting.general',
    resolution = 50
    );

# Set up data
hexbin.data <- data.frame(
    x = microarray[,1],
    y = microarray[,2]
    );

# Minimal Input
create.hexbinplot(
    # filename = tempfile(pattern = 'Hexbinplot_Minimal_Input', fileext = '.tiff'),
    formula = y ~ x,
    data = hexbin.data,
    main = 'Minimal input',
    # formatting bins
    colourcut = seq(0, 1, length = 11),
    # this sets the maximum value plotted -- values greater than this will not appear
    maxcnt = 50,
    description = 'Hexbinplot created by BoutrosLab.plotting.general',
    resolution = 100
    );

# Axes & Labels
create.hexbinplot(
    # filename = tempfile(pattern = 'Hexbinplot_Axes_Labels', fileext = '.tiff'),
    formula = y ~ x,
    data = hexbin.data,
    main = 'Axes & labels',
    colourcut = seq(0, 1, length = 11),
    maxcnt = 50,
    # Customize Axes and labels
    xaxis.cex = 1,
    yaxis.cex = 1,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xlab.label = 'Sample 1',
    ylab.label = 'Sample 2',
    xlimits = c(0,16),
    ylimits = c(0,16),
    xat = seq(0,16,2),
    yat = seq(0,16,2),
    description = 'Hexbinplot created by BoutrosLab.plotting.general',
    resolution = 100
    );

# Log Scaled Axis
log.data <- data.frame(
    x = microarray[,1],
    y = 10 ** microarray[,2]
    );

create.hexbinplot(
    formula = y ~ x,
    data = log.data,
    main = 'Log Scaled',
    # Log base 10 scale y-axis
    yat = 'auto.log',
    description = 'Hexbinplot created by BoutrosLab.plotting.general',
    resolution = 100
    );


# Aspect Ratio
create.hexbinplot(
    # filename = tempfile(pattern = 'Hexbinplot_Aspect_Ratio', fileext = '.tiff'),
    formula = y ~ x,
    data = hexbin.data,
    main = 'Aspect ratio',
    xaxis.cex = 1,
    yaxis.cex = 1,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xlab.label = 'Sample 1',
    ylab.label = 'Sample 2',
    xlimits = c(0,16),
    ylimits = c(0,16),
    xat = seq(0,16,2),
    yat = seq(0,16,2),
    colourcut = seq(0, 1, length = 11),
    maxcnt = 50,
    # Set the aspect ratio to control plot dimensions
    aspect = 2,
    description = 'Hexbinplot created by BoutrosLab.plotting.general',
    resolution = 200
    );

# Colour scheme
create.hexbinplot(
    # filename = tempfile(pattern = 'Hexbinplot_Colour_Change', fileext = '.tiff'),
    formula = y ~ x,
    data = hexbin.data,
    main = 'Colour change',
    xaxis.cex = 1,
    yaxis.cex = 1,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xlab.label = 'Sample 1',
    ylab.label = 'Sample 2',
    xlimits = c(0,16),
    ylimits = c(0,16),
    xat = seq(0,16,2),
    yat = seq(0,16,2),
    aspect = 1,
    colourcut = seq(0, 1, length = 11),
    maxcnt = 50,
    # Specify colour scheme
    colour.scheme = colorRampPalette(c('dodgerblue','paleturquoise','chartreuse','yellow',
        'orange','red')),
    description = 'Hexbinplot created by BoutrosLab.plotting.general',
    resolution = 200
    );

# Bin sizes
create.hexbinplot(
    # filename = tempfile(pattern = 'Hexbinplot_Bin_Sizes', fileext = '.tiff'),
    formula = y ~ x,
    data = hexbin.data,
    main = 'Bin sizes',
    xaxis.cex = 1,
    yaxis.cex = 1,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xlab.label = 'Sample 1',
    ylab.label = 'Sample 2',
    xlimits = c(0,16),
    ylimits = c(0,16),
    xat = seq(0,16,2),
    yat = seq(0,16,2),
    aspect = 1,
    colour.scheme = colorRampPalette(c('dodgerblue','paleturquoise','chartreuse', 'yellow',
        'orange','red')),
    # Specify bin sizes
    colourcut = seq(0,1,length = 6),
    description = 'Hexbinplot created by BoutrosLab.plotting.general',
    resolution = 200
    );

# Correlation Key
create.hexbinplot(
    # filename = tempfile(pattern = 'Hexbinplot_Correlation', fileext = '.tiff'),
    formula = y ~ x,
    data = hexbin.data,
    main = 'Correlation',
    xaxis.cex = 1,
    yaxis.cex = 1,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xlab.label = 'Sample 1',
    ylab.label = 'Sample 2',
    xlimits = c(0,16),
    ylimits = c(0,16),
    xat = seq(0,16,2),
    yat = seq(0,16,2),
    aspect = 1,
    colourcut = seq(0, 1, length = 11),
    maxcnt = 50,
    # Correlation Key
    legend = list(
        inside = list(
            fun = draw.key,
            args = list(
                key = get.corr.key(
                    x = hexbin.data$x,
                    y = hexbin.data$y,
                    label.items = c('beta1', 'spearman'),
                    alpha.background = 0
                    )
                ),
            x = 0.05,
            y = 0.95,
            corner = c(0,1),
            draw = FALSE
            )
        ),
    description = 'Hexbinplot created by BoutrosLab.plotting.general',
    resolution = 200
    );

# Grid lines and diagonal
create.hexbinplot(
    # filename = tempfile(pattern = 'Hexbinplot_Gridlines', fileext = '.tiff'),
    formula = y ~ x,
    data = hexbin.data,
    main = 'Gridlines',
    xaxis.cex = 1,
    yaxis.cex = 1,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xlab.label = 'Sample 1',
    ylab.label = 'Sample 2',
    xlimits = c(0,16),
    ylimits = c(0,16),
    xat = seq(0,16,2),
    yat = seq(0,16,2),
    aspect = 1,
    colourcut = seq(0, 1, length = 11),
    maxcnt = 50,
    # Grid & diagonal
    add.grid = TRUE,
    add.xyline = TRUE,
    description = 'Hexbinplot created by BoutrosLab.plotting.general',
    resolution = 200
    );

# Large range
# Generate some fake data with both very low and very high values
set.seed(12345);

x <- c(rnorm(100000,0,0.1),rnorm(1000,0,0.5),rnorm(1000,0,sd=0.75));
y <- c(rnorm(100000,0,0.1),rnorm(1000,0,0.5),rnorm(1000,0,sd=0.75));

fake.data <- data.frame(
    x = x,
    y = y,
    z = y + x*(x+1)/4
    );

create.hexbinplot(
    # filename = tempfile(pattern = 'Hexbinplot_Range', fileext = '.tiff'),
    formula = z ~ x,
    data = fake.data,
    main = 'Range',
    xaxis.cex = 1,
    yaxis.cex = 1,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xlab.label = 'Sample 1',
    ylab.label = 'Sample 2',
    aspect = 1,
    # Use colourcut to divide the bins appropriately
    colourcut = c(0,0.0002,0.0004,0.0008,0.0016,0.0032,0.0064,0.0128,0.0256,0.0512,0.1024,0.2048,
        0.4096,0.8192,1),
    # Change the colour scheme
    colour.scheme = function(n){BTC(n, beg=1, end=256)},
    background.col = 'grey',
    description = 'Hexbinplot created by BoutrosLab.plotting.general',
    resolution = 200
    );

# Outliers
# Generate data with upper bound outlier
set.seed(12345);

x <- c(rnorm(1000,0,0),rnorm(4000,0,0.5));
y <- c(rnorm(1000,0,0),rnorm(4000,0,0.5));

fake.data.outlier <- data.frame(
    x = x,
    y = y,
    z = y + x*(x+1)/4
    );

create.hexbinplot(
    # filename = tempfile(pattern = 'Hexbinplot_Outlier', fileext = '.tiff'),
    formula = z ~ x,
    data = fake.data.outlier,
    main = 'Outlier',
    xaxis.cex = 1,
    yaxis.cex = 1,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xlab.label = 'Sample 1',
    ylab.label = 'Sample 2',
    aspect = 1,
    # Use colourcut to divide the bins appropriately
    colourcut = c(seq(0,0.01, length = 4),seq(0.0125,0.1,length=4), seq(0.125,1,length=4)),
    xbins = 15,
    mincnt = 0,
    # Change the colour scheme
    colour.scheme = function(n){BTC(n, beg=1, end=256)},
    background.col = 'grey',
    description = 'Hexbinplot created by BoutrosLab.plotting.general',
    resolution = 200
    );

# Nature style
create.hexbinplot(
    # filename = tempfile(pattern = 'Hexbinplot_Nature_style', fileext = '.tiff'),
    formula = y ~ x,
    data = hexbin.data,
    main = 'Nature style',
    xaxis.cex = 1,
    yaxis.cex = 1,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xlimits = c(0,16),
    ylimits = c(0,16),
    xat = seq(0,16,2),
    yat = seq(0,16,2),
    aspect = 1,
    colourcut = seq(0, 1, length = 11),
    maxcnt = 50,
    # Grid & diagonal
    add.grid = TRUE,
    add.xyline = TRUE,

    # set style to Nature
    style = 'Nature',

    # demonstrating how to italicize character variables
    ylab.lab = expression(paste('italicized ', italic('a'))),

    # demonstrating how to create en-dashes
    xlab.lab = expression(paste('en dashs: 1','\u2013', '10'^'\u2013', ''^3)),

    description = 'Hexbinplot created by BoutrosLab.plotting.general',
    resolution = 200
    );

# Multiplot different groups
set.seed(73);

# Randomly generate groups
simple.data$groups <- sample(1:2, 10000, replace = TRUE);
simple.data$group.labels <- as.factor(simple.data$groups);

create.hexbinplot(
	formula = y ~ x | groups,
	# filename = tempfile(
  #      pattern = 'stratified_hexbinplot_numeric_conditioning',
  #      fileext = '.tiff'
  #      ),
	data = simple.data,
	description = 'Hexbinplot created by BoutrosLab.plotting.general',
	strip.col = 'white',
	strip.cex = 0.8,
	strip.fontface = 'bold',
	resolution = 200
	);

create.hexbinplot(
	formula = y ~ x | group.labels,
	# filename = tempfile(
  #      pattern = 'stratified_hexbinplot_factor_conditioning',
  #      fileext = '.tiff'
  #      ),
	data = simple.data,
	description = 'Hexbinplot created by BoutrosLab.plotting.general',
	strip.col = 'white',
	strip.cex = 0.8,
	strip.fontface = 'bold',
	resolution = 200
	);

Make a histogram

Description

Takes a vector and creates a histogram

Usage

create.histogram(
	x,
	data,
	filename = NULL,
	main = NULL,
	main.just = 'center',
	main.x = 0.5,
	main.y = 0.5,
	main.cex = 3,
	xlab.label = NULL,
	ylab.label = NULL,
	xlab.cex = 2,
	ylab.cex = 2,
	xlab.col = 'black',
	ylab.col = 'black',
	xaxis.lab = TRUE,
	yaxis.lab = TRUE,
	xaxis.cex = 1.5,
	yaxis.cex = 1.5,
	xlimits = NULL,
	ylimits = NULL,
	xat = TRUE,
	yat = TRUE,
	xaxis.rot = 0,
	yaxis.rot = 0,
	xaxis.col = 'black',
	yaxis.col = 'black',
	xaxis.tck = 1,
	yaxis.tck = 1,
	xaxis.fontface = 'bold',
	yaxis.fontface = 'bold',
	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,
	type = 'percent',
	breaks = NULL,
	col = 'white',
	border.col = 'black',
	lwd = 2,
	lty = 1,
	layout = NULL,
	x.spacing = 0,
	y.spacing = 0,
	x.relation = 'same',
	y.relation = 'same',
	strip.col = 'white',
	strip.cex = 1,
	top.padding = 0.1,
	bottom.padding = 0.7,
	right.padding = 0.1,
	left.padding = 0.5,
	ylab.axis.padding = 0,
	abline.h = NULL,
	abline.v = NULL,
	abline.col = 'black',
	abline.lwd = 1,
	abline.lty = 1,
	key = NULL,
	legend = NULL,
	add.text = FALSE,
	text.labels = NULL,
	text.x = NULL,
	text.y = NULL,
	text.col = 'black',
	text.cex = 1,
	text.fontface = 'bold',	
	add.rectangle = FALSE,
	xleft.rectangle = NULL,
	ybottom.rectangle = NULL,
	xright.rectangle = NULL,
	ytop.rectangle = NULL,
	col.rectangle = 'transparent',
	alpha.rectangle = 1,
	height = 6,
	width = 6,
	size.units = 'in',
	resolution = 1600,
	enable.warnings = FALSE,
	description = 'Created with BoutrosLab.plotting.general',
	style = 'BoutrosLab',
	preload.default = 'custom',
        use.legacy.settings = FALSE,
	inside.legend.auto = FALSE
	);

Arguments

.

,

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 histogram. In particular, if a script that uses such a call of create histogram 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
    

Author(s)

Paul C. Boutros

See Also

histogram, lattice or the Lattice book for an overview of the package.

Examples

set.seed(12345);

create.histogram(
    # filename = tempfile(pattern = 'Histogram_Simple', fileext = '.tiff'),
    x = rnorm(5000),
    main = 'Simple',
    description = 'Histogram created by BoutrosLab.plotting.general',
    resolution = 50
    );

create.histogram(
    # filename = tempfile(pattern = 'Histogram_Simple_Count', fileext = '.tiff'),
    x = rnorm(5000),
    main = 'Simple Count',
    description = 'Histogram created by BoutrosLab.plotting.general',
    type = 'count',
    resolution = 50
    );

# Minimal Input
create.histogram(
    # filename = tempfile(pattern = 'Histogram_Minimal_Input', fileext = '.tiff'),
    x = microarray[,1],
    main = 'Minimal input',
    description = 'Histogram created by BoutrosLab.plotting.general',
    resolution = 50
    );

# Formula Input - dividing by chromosome
chr.data <- data.frame(
    x = microarray$Chr,
    y = microarray[,1]
    );

create.histogram(
    # filename = tempfile(pattern = 'Histogram_Formula_Input', fileext = '.tiff'),
    x = y ~ x,
    data = chr.data,
    main = 'Formula input',
    description = 'Histogram created by BoutrosLab.plotting.general',
    resolution = 100
    );

# Axes and Labels
create.histogram(
    # filename = tempfile(pattern = 'Histogram_Axes_Labels', fileext = '.tiff'),
    x = microarray[,1],
    main = 'Axes & labels',
    # Customizing the axes and labels
    xlab.label = 'Bins',
    ylab.label = 'Counts',
    xlimits = c(0, 16),
    xat = seq(0,15,5),
    # set break points for bins
    breaks = seq(floor(min(microarray[,1])), ceiling(max(microarray[,1])), 1),
    description = 'Histogram created by BoutrosLab.plotting.general',
    resolution = 100
    );


# Colour change
create.histogram(
    # filename = tempfile(pattern = 'Histogram_Colours', fileext = '.tiff'),
    x = microarray[,1],
    main = 'Colours',
    xlab.label = 'Bins',
    ylab.label = 'Counts',
    xlimits = c(0, 16),
    xat = seq(0,15,5),
    breaks = seq(floor(min(microarray[,1])), ceiling(max(microarray[,1])), 1),
    # Colours
    col = 'lightgrey',
    description = 'Histogram created by BoutrosLab.plotting.general',
    resolution = 100
    );

# Line type
create.histogram(
    # filename = tempfile(pattern = 'Histogram_Line_Type', fileext = '.tiff'),
    x = microarray[,1],
    main = 'Line type',
    xlab.label = 'Bins',
    ylab.label = 'Counts',
    xlimits = c(0, 16),
    xat = seq(0,15,5),
    breaks = seq(floor(min(microarray[,1])), ceiling(max(microarray[,1])), 1),
    col = 'lightgrey',
    # Changing the line type
    lty = 2,
    description = 'Histogram created by BoutrosLab.plotting.general',
    resolution = 200
    );

# Nature style
create.histogram(
    # filename = tempfile(pattern = 'Histogram_Nature_style', fileext = '.tiff'),
    x = microarray[,1],
    main = 'Nature style',
    xlimits = c(0, 16),
    xat = seq(0,15,5),
    breaks = seq(floor(min(microarray[,1])), ceiling(max(microarray[,1])), 1),
    col = 'lightgrey',

    # 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 = 'Histogram created by BoutrosLab.plotting.general',
    resolution = 200
    );

Make a lollipopplot

Description

Takes a data.frame and creates a lollipopplot

Usage

create.lollipopplot(
	formula,
	data,
	filename = NULL,
	groups = NULL,
	main = NULL,
	main.just = 'center',
	main.x = 0.5,
        main.y = 0.5,
	main.cex = 3,
	xlab.label = tail(sub('~', '', formula[-2]), 1),
	ylab.label = tail(sub('~', '', formula[-3]), 1),
        xlab.cex = 2,
	ylab.cex = 2,
	xlab.col = 'black',
	ylab.col = 'black',
        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,
	xlimits = NULL,
	ylimits = NULL,
	xat = TRUE,
	yat = TRUE,
	xaxis.lab = NA,
	yaxis.lab = NA,
	xaxis.log = FALSE,
	yaxis.log = FALSE,
        xaxis.cex = 1.5,
	yaxis.cex = 1.5,
	xaxis.rot = 0,
	yaxis.rot = 0,
	xaxis.fontface = 'bold',
	yaxis.fontface = 'bold',
	xaxis.col = 'black',
	yaxis.col = 'black',
	xaxis.tck = c(1,1),
	yaxis.tck = c(1,1),
	add.grid = FALSE,
	xgrid.at = xat,
        ygrid.at = yat,
	grid.colour = NULL,
	horizontal = FALSE,
	type = 'p',
	cex = 0.75,
	pch = 19,
	col = 'black',
	col.border = 'black',
	lwd = 1,
	lty = 1,
	alpha = 1,
	axes.lwd = 1,
	strip.col = 'white',
	strip.cex = 1,
	strip.fontface = 'bold',
	y.error.up = NULL,
	y.error.down = y.error.up,
	x.error.right = NULL,
	x.error.left = x.error.right,
	y.error.bar.col = 'black',
	x.error.bar.col = y.error.bar.col,
	error.whisker.angle = 90,
	error.bar.lwd = 1,
	error.bar.length = 0.1,
	key = list(text = list(lab = c(''))),
	legend = NULL,
	top.padding = 0.1,
	bottom.padding = 0.7,
	right.padding = 0.1,
	left.padding = 0.5,
	key.top = 0.1,
	key.left.padding = 0,
	ylab.axis.padding = 1,
	axis.key.padding = 1,
	layout = NULL,
	as.table = FALSE,
	x.spacing = 0,
	y.spacing = 0,	
	x.relation = 'same',
	y.relation = 'same',
	add.axes = FALSE,
	axes.lty = 'dashed',
	add.xyline = FALSE,
	xyline.col = 'black',
	xyline.lwd = 1,
	xyline.lty = 1,
	abline.h = NULL,
	abline.v = NULL,
	abline.col = 'black',
	abline.lwd = 1,
	abline.lty = 1,
	add.curves = FALSE,
	curves.exprs = NULL,
	curves.from = min(data, na.rm = TRUE),
	curves.to = max(data, na.rm = TRUE),
	curves.col = 'black',
	curves.lwd = 2,
	curves.lty = 1,
	add.rectangle = FALSE,
	xleft.rectangle = NULL,
	ybottom.rectangle = NULL,
	xright.rectangle = NULL,
	ytop.rectangle = NULL,
	col.rectangle = 'transparent',
	alpha.rectangle = 1,
	add.points = FALSE,
	points.x = NULL,
	points.y = NULL,
	points.pch = 19,
	points.col = 'black',
	points.col.border = 'black',
	points.cex = 1,
	add.line.segments = FALSE,
	line.start = NULL,
	line.end = NULL,
	line.col = 'black',
	line.lwd = 1,
	add.text = FALSE,
	text.labels = NULL,
	text.x = NULL,
	text.y = NULL,
	text.col = 'black',
	text.cex = 1,
	text.fontface = 'bold',
	text.guess.labels = FALSE,
	text.guess.skip.labels = TRUE,
	text.guess.ignore.radius = FALSE,
	text.guess.ignore.rectangle = FALSE,
	text.guess.radius.factor = 1,
	text.guess.buffer.factor = 1,
	text.guess.label.position = NULL,
	height = 6,
	width = 6,
	size.units = 'in',
	resolution = 1600,
	enable.warnings = FALSE,
	description = 'Created with BoutrosLab.plotting.general',
	style = 'BoutrosLab',
	preload.default = 'custom',
	group.specific.colouring = TRUE,
	use.legacy.settings = FALSE,
	inside.legend.auto = FALSE,
	regions.labels = c(),
        regions.start = c(), 
	regions.stop = c(), 
	regions.color = c("red"), 
	regions.cex = 1, 
	regions.alpha = 1,
        lollipop.bar.y = NULL, 
	lollipop.bar.color = "gray",
	...
	);

Arguments

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 histogram. In particular, if a script that uses such a call of create histogram 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
    

Author(s)

Paul C. Boutros

See Also

xyplot, lattice or the Lattice book for an overview of the package.

Examples

set.seed(12345);
lollipop.data <- data.frame(
    y = seq(1,100,1),
    x = rnorm(100)
    );

create.lollipopplot(
    # filename = tempfile(pattern = 'Lollipop_Simple', fileext = '.tiff'),
    formula = x ~ y,
    data = lollipop.data,
    main = 'Lollipop plot',
    xaxis.cex = 1,
    xlimits = c(-1,102),
    yaxis.cex = 1,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    pch = 21,
    col = 'black',
    fill = 'transparent',
    description = 'Scatter plot created by BoutrosLab.plotting.general',
    regions.start = c(1,26,48),
    regions.stop = c(15,35,72),
    regions.labels = c("test 1", "test2", "test 3"),
    regions.color = c("#66b3ff", "#5cd65c", "#ff3333")
    );

Make a Manhattan plot

Description

Takes a data.frame and creates a Manhattan plot

Usage

create.manhattanplot(
	formula,
	data,
	filename = NULL,
	main = NULL,
	main.just = 'center',
	main.x = 0.5,
	main.y = 0.5,
	main.cex = 3,
	xlab.label = tail(sub('~', '', formula[-2]), 1),
	ylab.label = tail(sub('~', '', formula[-3]), 1),
	xlab.cex = 2,
	ylab.cex = 2,
	xlab.col = 'black',
	ylab.col = 'black',
	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,
	xlimits = NULL,
	ylimits = NULL,
	xat = TRUE,
	yat = TRUE,
	xaxis.lab = NA,
	yaxis.lab = NA,
	xaxis.log = FALSE,
	yaxis.log = FALSE,
	xaxis.cex = 1.5,
	yaxis.cex = 1.5,
	xaxis.rot = 0,
	yaxis.rot = 0,
	xaxis.fontface = 'plain',
	yaxis.fontface = 'plain',
	xaxis.col = 'black',
	yaxis.col = 'black',
	xaxis.tck = 0,
	yaxis.tck = c(1,1),
	horizontal = FALSE,
	type = 'p',
	cex = 2,
	pch = '.',
	col = 'black',
	lwd = 1,
	lty = 1,
	alpha = 1,
	strip.col = 'white',
	strip.cex = 1,
	axes.lwd = 1,
	axes.lty = 'dashed',
	key = list(text = list(lab = c(''))),
	legend = NULL,
	layout = NULL,
	as.table = FALSE,
	x.spacing = 0,
	y.spacing = 0,
	x.relation = 'same',
	y.relation = 'same',
	top.padding = 0,
	bottom.padding = 0,
	right.padding = 0,
	left.padding = 0,
	key.top = 0,
	key.left.padding = 0,
	ylab.axis.padding = 1,
	axis.key.padding = 1,
	abline.h = NULL,
	abline.col = 'black',
	abline.lwd = 1,
	abline.lty = 1,
	add.rectangle = FALSE,
	xleft.rectangle = NULL,
	ybottom.rectangle = NULL,
	xright.rectangle = NULL,
	ytop.rectangle = NULL,
	col.rectangle = 'transparent',
	alpha.rectangle = 1,
	add.points = FALSE,
	points.x = NULL,
	points.y = NULL,
	points.pch = 19,
	points.col = 'black',
	points.cex = 1,
	add.text = FALSE,
	text.labels = NULL,
	text.x = NULL,
	text.y = NULL,
	text.col = 'black',
	text.cex = 1,
	text.fontface = 'bold',
	height = 6,
	width = 10,
	size.units = 'in',
	resolution = 1600,
	enable.warnings = FALSE,
	style = 'BoutrosLab',
	description = 'Created with BoutrosLab.plotting.general',
	preload.default = 'custom',
        use.legacy.settings = FALSE,
        inside.legend.auto = FALSE,
	...
	);
    

Arguments

F

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 histogram. In particular, if a script that uses such a call of create histogram 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
    

Author(s)

Christine P'ng, Cindy Q. Yao

See Also

xyplot, lattice or the Lattice book for an overview of the package.

Examples

set.seed(12345);
simple.data <- data.frame(
    x = runif(20000, 0, 1),
    y = 1:20000
    );

create.manhattanplot(
    # filename = tempfile(pattern = 'Manhattan_Simple', fileext = '.tiff'),
    formula = -log10(x) ~ y,
    data = simple.data,
    main = 'Simple',
    description = 'Manhattan plot created using BoutrosLab.plotting.general',
    resolution = 50
    );

# set up chromosome covariate colours to use for chr covariate, below
chr.colours <- force.colour.scheme(microarray$Chr, scheme = 'chromosome');

# make chr covariate and chr labels 
chr.n.genes       <- vector();
chr.tck           <- vector();
chr.pos.genes     <- vector();
chr.break         <- vector();
chr.break[1]      <- 0;
# get a list of chromosomes to loop
chr <- unique(microarray$Chr);

# loop over each chromosome
for ( i in 1:length(chr) ) {

    # get the number of genes that belong to one chromosome
    n <- sum(microarray$Chr == chr[i]);

    # calculate where the labels go
    chr.n.genes[i]   <- n;
    chr.break[i+1] <- n + chr.break[i];
    chr.pos.genes[i] <- floor(chr.n.genes[i]/2);
    chr.tck[i]       <- chr.pos.genes[i] + which(microarray$Chr == chr[i])[1];
    }

# add an indicator function for the data-frame
microarray$ind <- 1:nrow(microarray);

# Minimal input
create.manhattanplot(
    # filename = tempfile(pattern = 'Manhattan_Minimal_Input', fileext = '.tiff'),
    formula = -log10(pval) ~ ind,
    data = microarray,
    main = 'Minimal input',
    description = 'Manhattan plot created using BoutrosLab.plotting.general',
    resolution = 100
    );

# Custom Axes
create.manhattanplot(
    # filename = tempfile(pattern = 'Manhattan_Custom_Axes', fileext = '.tiff'),
    formula = -log10(pval) ~ ind,
    data = microarray,
    main = 'Custom axes',
    xlab.label = expression('Chromosomes'),
    ylab.label = expression('P'['adjusted']),
    xat = chr.tck,
    xaxis.lab = c(1:22, 'X', 'Y'),
    xaxis.tck = 0,
    xaxis.cex = 1,
    yaxis.cex = 1,
    yat = seq(0,5,1),
    yaxis.lab = c(
        1,
        expression(10^-1),
        expression(10^-2),
        expression(10^-3),
        expression(10^-4)
        ),
    description = 'Manhattan plot created using BoutrosLab.plotting.general',
    resolution = 100
    );

# Log-Scaled Axis
log.data <- data.frame(
    x = 10 ** runif(20000, 1, 5),
    y = 1:20000
    );

create.manhattanplot(
    formula = x ~ y,
    data = log.data,
    main = 'Log Scaled',
    # Log base 10 scale x-axis
    xat = 'auto.log',
    description = 'Manhattan plot created using BoutrosLab.plotting.general',
    resolution = 50
    );

# Colour scheme
create.manhattanplot(
    # filename = tempfile(pattern = 'Manhattan_Colour_Scheme', fileext = '.tiff'),
    formula = -log10(pval) ~ ind,
    data = microarray,
    main = 'Colour scheme',
    xlab.label = expression('Chromosomes'),
    ylab.label = expression('P'['adjusted']),
    xat = chr.tck,
    xaxis.lab = c(1:22, 'X', 'Y'),
    xaxis.tck = 0,
    xaxis.cex = 1,
    yaxis.cex = 1,
    yat = seq(0,5,1),
    yaxis.lab = c(
        1,
        expression(10^-1),
        expression(10^-2),
        expression(10^-3),
        expression(10^-4)
        ),
    col = chr.colours,
    description = 'Manhattan plot created using BoutrosLab.plotting.general',
    resolution = 200
    );

# Plotting Character
create.manhattanplot(
    # filename = tempfile(pattern = 'Manhattan_Plotting_Character', fileext = '.tiff'),
    formula = -log10(pval) ~ ind,
    data = microarray,
    main = 'Plotting character',
    xlab.label = expression('Chromosomes'),
    ylab.label = expression('P'['adjusted']),
    xat = chr.tck,
    xaxis.lab = c(1:22, 'X', 'Y'),
    xaxis.tck = 0,
    xaxis.cex = 1,
    yaxis.cex = 1,
    yat = seq(0,5,1),
    yaxis.lab = c(
        1,
        expression(10^-1),
        expression(10^-2),
        expression(10^-3),
        expression(10^-4)
        ),
    col = chr.colours,
    # Change plotting character and size of plotting character
    pch = 18,
    cex = 0.75,
    description = 'Manhattan plot created using BoutrosLab.plotting.general',
    resolution = 200
    );


# Line
create.manhattanplot(
    # filename = tempfile(pattern = 'Manhattan_Added_Line', fileext = '.tiff'),
    formula = -log10(pval) ~ ind,
    data = microarray,
    main = 'Line',
    xlab.label = expression('Chromosomes'),
    ylab.label = expression('P'['adjusted']),
    xat = chr.tck,
    xaxis.lab = c(1:22, 'X', 'Y'),
    xaxis.tck = 0,
    xaxis.cex = 1,
    yaxis.cex = 1,
    yat = seq(0,5,1),
    yaxis.lab = c(
        1,
        expression(10^-1),
        expression(10^-2),
        expression(10^-3),
        expression(10^-4)
        ),
    col = chr.colours,
    pch = 18,
    cex = 0.75,
    # draw horizontal line
    abline.h = 2,
    abline.lty = 2,
    abline.lwd = 1,
    abline.col = 'black',
    description = 'Manhattan plot created using BoutrosLab.plotting.general',
    resolution = 200
    );

# Background shading
create.manhattanplot(
    # filename = tempfile(pattern = 'Manhattan_BG', fileext = '.tiff'),
    formula = -log10(pval) ~ ind,
    data = microarray,
    main = 'Bg rectangles',
    xlab.label = expression('Chromosomes'),
    ylab.label = expression('P'['adjusted']),
    xat = chr.tck,
    xaxis.lab = c(1:22, 'X', 'Y'),
    xaxis.tck = 0,
    xaxis.cex = 1,
    yaxis.cex = 1,
    yat = seq(0,5,1),
    yaxis.lab = c(
        1,
        expression(10^-1),
        expression(10^-2),
        expression(10^-3),
        expression(10^-4)
        ),
    col = chr.colours,
    pch = 18,
    cex = 0.75,
    abline.h = 2,
    abline.lty = 2,
    abline.lwd = 1,
    abline.col = 'black',
    # Adding rectangles
    add.rectangle = TRUE,
    xleft.rectangle = chr.break[seq(1, length(chr.break) - 1, 2)],
    ybottom.rectangle = 0,
    xright.rectangle = chr.break[seq(2, length(chr.break) - 1, 2)],
    ytop.rectangle = 4.5,
    col.rectangle = 'grey',
    alpha.rectangle = 0.5,
    description = 'Manhattan plot created using BoutrosLab.plotting.general',
    resolution = 200
    );

# Nature style
create.manhattanplot(
    # filename = tempfile(pattern = 'Manhattan_Nature_style', fileext = '.tiff'),
    formula = -log10(pval) ~ ind,
    data = microarray,
    main = 'Nature style',
    xat = chr.tck,
    xaxis.lab = c(1:22, 'X', 'Y'),
    xaxis.tck = 0,
    xaxis.cex = 1,
    yaxis.cex = 1,
    yat = seq(0,5,1),
    yaxis.lab = c(
        1,
        expression(10^-1),
        expression(10^-2),
        expression(10^-3),
        expression(10^-4)
        ),
    col = chr.colours,
    pch = 18,
    cex = 0.75,
    abline.h = 2,
    abline.lty = 2,
    abline.lwd = 1,
    abline.col = 'black',
    # Adding rectangles
    add.rectangle = TRUE,
    xleft.rectangle = chr.break[seq(1, length(chr.break) - 1, 2)],
    ybottom.rectangle = 0,
    xright.rectangle = chr.break[seq(2, length(chr.break) - 1, 2)],
    ytop.rectangle = 4.5,
    col.rectangle = 'grey',
    alpha.rectangle = 0.5,

    # 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 = 'Manhattan plot created using BoutrosLab.plotting.general',
    resolution = 1200
    );

Joins plots together

Description

Merges together multiple plots in the specified layout

Usage

create.multipanelplot(
	plot.objects = NULL,
	filename = NULL,
	height = 10,
	width = 10,
	resolution = 1000,
	plot.objects.heights = c(rep(1,layout.height)),
	plot.objects.widths = c(rep(1,layout.width)),
	layout.width = 1,
	layout.height = length(plot.objects),
	main = '',
	main.x = 0.5,
	main.y = 0.5,
	x.spacing = 0, 
	y.spacing = 0,
	xlab.label = '',
	xlab.cex = 2,
	ylab.label = '',
	ylab.label.right = '',
	ylab.cex = 2,
	main.cex = 3,
	legend = NULL,
	left.padding = 0,
	ylab.axis.padding = c(rep(0, layout.width)),
	xlab.axis.padding = c(rep(0, layout.height)),
	bottom.padding = 0,
	top.padding = 0,
	right.padding = 0,
	layout.skip = c(rep(FALSE, layout.width*layout.height)),
	left.legend.padding = 2,
	right.legend.padding = 2, 
	bottom.legend.padding = 2, 
	top.legend.padding = 2,
	description = 'Created with BoutrosLab.plotting.general',
	size.units = 'in',
	enable.warnings = FALSE,
	style = "BoutrosLab",
	use.legacy.settings = FALSE
);

Arguments

Warning

If this function is called without capturing the return value, or specifying a filename, it may crash while trying to draw the histogram. In particular, if a script that uses such a call of create histogram 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
    

Author(s)

Jeff Green

Examples

set.seed(12345);
# begin by creating the individual plots which will be combined into a multiplot
dist <- data.frame(
    a = rnorm(100, 1), 
    b = rnorm(100, 3), 
    c = rnorm(100, 5)
    );

simple.data <- data.frame(
    x = c(dist$a, dist$b, dist$c),
    y = rep(LETTERS[1:3], each = 100)
    );
fill.squares <- matrix(c(1, 0, 0, 0, 1, 0, 0, 0, 1), ncol = 3, byrow = TRUE);
rownames(fill.squares) <- c("Drug I only", "Drug II only" , "Drugs I & II");
colnames(fill.squares) <- levels(factor(simple.data$y));

# Create plot # 1
simple.boxplot <- create.boxplot(
    formula = x ~ y,
    data = simple.data,
    xaxis.lab = c('','',''),
    main.x = 0.57,
    ylab.label = 'Sugar Level',
    xlab.label = '',
    col = 'lightgrey',
    xaxis.tck = c(0,0),
    yaxis.tck = c(1,0),
    yaxis.lab = seq(-1,8,2) ,
    yat = seq(-1,8,2),
    left.padding = 0,
    right.padding = 0,
    lwd = 2
    );

# Create plot # 2
simple.heatmap <- create.heatmap(
    x = t(fill.squares),
    clustering.method = 'none',
    shrink = 0.8,
    yaxis.lab = c(3,2,3),
    yaxis.tck = 1,
    xaxis.lab = c('A','B','C'),
    ylab.label = 'Drug Regimen',
    xlab.label = 'Patient Group',
    colour.scheme = c("white", "grey20"),
    fill.colour = "white",
    print.colour.key = FALSE,
    left.padding = 0,
    xaxis.tck = c(1,0),
    right.padding = 0,
    xaxis.rot = 0
    );

create.multipanelplot(
    # filename = tempfile(pattern = 'Multipanelplot_Simple', fileext = '.tiff'),
    plot.objects = list(simple.boxplot,simple.heatmap), 
    y.spacing = 1,  
    ylab.axis.padding = 2, 
    main = 'Simple', 
    top.padding = 2,
    resolution = 200
    );

# Create plot # 2
simple.heatmap.with.legends <- create.heatmap(
    x = t(fill.squares),
    shrink = 0.8,
    yaxis.lab = c(3,2,3),
    yaxis.tck = 1,
    xaxis.lab = c('A','B','C'),
    ylab.label = 'Drug Regimen',
    xlab.label = '',
    colour.scheme = c("white", "grey20"),
    fill.colour = "white",
    left.padding = 0,
    xaxis.tck = c(1,0),
    right.padding = 0,
    xaxis.rot = 0
    );

create.multipanelplot(
    # filename = tempfile(pattern = 'Multipanelplot_Simple_Legends', fileext = '.tiff'),
    plot.objects = list(simple.boxplot,simple.heatmap.with.legends),
    y.spacing = 1,
    ylab.axis.padding = 2,
    main = 'Simple',
    top.padding = 2,
    resolution = 200
    );


# Create plot # 1
simple.boxplot2 <- create.boxplot(
    formula = x ~ y,
    data = simple.data,
    ylab.label = 'Sugar Level',
    xlab.label = '',
    col = 'lightgrey',
    xaxis.tck = c(0,0),
    xaxis.lab = c('','',''),
    yaxis.tck = c(1,0),
    yaxis.lab = seq(-1,8,2),
    yat = seq(-1,8,2),
    left.padding = 0,
    right.padding = 0,
    lwd = 2
    );

simple.violin2 <- create.violinplot(
    formula = x ~ y,
    data = simple.data,
    col = 'lightgrey',
    yaxis.tck = c(0,0),
    xlab.label = '',
    ylab.label = '',
    yaxis.lab = NULL,
    xaxis.lab = c('','',''),
    xaxis.tck = c(0,0)
    );

# Create plot # 2
simple.heatmap2 <- create.heatmap(
    x = t(fill.squares),
    clustering.method = 'none',
    shrink = 0.8,
    yaxis.lab = c(1,2,3),
    yaxis.tck = 1,
    xaxis.lab = c('A','B','C'),
    ylab.label = 'Drug Regimen',
    colour.scheme = c("white", "grey20"),
    fill.colour = "white",
    print.colour.key = FALSE,
    left.padding = 0,
    xaxis.tck = c(3,0),
    right.padding = 0,
    xaxis.rot = 0,
    ylab.cex = 2
    );


create.multipanelplot(
    # filename = tempfile(pattern = 'Multipanelplot_Simple_Layout', fileext = '.tiff'),
    plot.objects = list(simple.boxplot2, 
    simple.violin2,simple.heatmap2), 
    layout.width = 2, 
    layout.height = 2, 
    xlab.label = 'Patient Group', 
    main = 'Simple Layout', 
    top.padding = 2, 
    plot.objects.heights = c(3,1), 
    x.spacing = 1, 
    y.spacing = 1
    );

all.data <- data.frame(
    a = rnorm(n = 25, mean = 0, sd = 0.75),
    b = rnorm(n = 25, mean = 0, sd = 0.75),
    c = rnorm(n = 25, mean = 0, sd = 0.75),
    d = rnorm(n = 25, mean = 0, sd = 0.75),
    e = rnorm(n = 25, mean = 0, sd = 0.75),
    f = rnorm(n = 25, mean = 0, sd = 0.75),
    x = rnorm(n = 25, mean = 5),
    y = seq(1, 25, 1)
    );
# create the plot -- this allows for previewing of the individual plot
barplot.formatted <- create.barplot(
    formula = x ~ y,
    data = all.data[,7:8],
    yaxis.tck = c(1,0),
    border.lwd = 0,
    col = 'grey',
    xlab.label = '',
    xat = c(-100),
    ylab.label = '',
    yaxis.lab = seq(1, ceiling(max(all.data$x)), 1),
    yat = seq(1, ceiling(max(all.data$x)), 1),
    yaxis.cex = 1.5
    );


heatmap.formatted <- create.heatmap(
    x = all.data[,1:6],
    clustering.method = 'none',
    colour.scheme = c('magenta','white','green'),
    print.colour.key = FALSE,
    xlab.label = '',
    yaxis.tck = c(1,0),
    xaxis.tck = c(1,0),
    xat = c(1:25),
    yaxis.lab = c("BRCA1", "BRCA2", "APC", "TIN", "ARG", "FOO"),
    yat = c(1,2,3,4,5,6),
    xaxis.lab = c(1:25),
    xaxis.rot = 0,
    yaxis.cex = 1.5
    ); 

create.multipanelplot(
    # filename = tempfile(pattern = 'Multipanelplot_formatted', fileext = '.tiff'),
    plot.objects = list(barplot.formatted, heatmap.formatted), 
    plot.objects.heights = c(1,3), 
    y.spacing = -3.75, 
    main = 'Formatted', 
    top.padding = 0
    );

data.bars <- data.frame(
    x = sample(x = 5:35, size = 10),
    y = seq(1,10,1)
    );

data.cov <- data.frame(
    x = rnorm(n = 10, mean = 0, sd = 0.75),
    y = rnorm(n = 10, mean = 0, sd = 0.75),
    z = rnorm(n = 10, mean = 0, sd = 0.75)
    );

# Create main barplot
bars <- create.barplot(
    formula = x~y,
    data = data.bars,
    ylimits = c(0,35),
    ylab.label = '',
    sample.order = 'increasing',
    border.lwd = 0,
    yaxis.lab = seq(5,35,5),
    yat = seq(5,35,5),
    yaxis.tck = c(0,0),
    xlab.label = ''
    );

# Make covariate bars out of heatmaps
cov.1 <- create.heatmap(
    x = as.matrix(data.bars$y),
    clustering.method = 'none',
    scale.data = FALSE,
    colour.scheme = default.colours(4),
    grid.col = TRUE,
    col.colour = 'black',
    # col.lwd = 10,
    total.col = 5,
    print.colour.key = FALSE,
    yaxis.tck = 0,
    axes.lwd = 0
    );

cov.2 <- create.heatmap(
    x = as.matrix(data.cov$y),
    clustering.method = 'none',
    scale.data = FALSE,
    colour.scheme = c("lightblue","dodgerblue2", "dodgerblue4"),
    grid.col = TRUE,
    col.colour = 'black',
    # col.lwd = 10,
    total.col = 4,
    print.colour.key = FALSE,
    yaxis.tck = 0
    );

cov.3 <- create.heatmap(
    x = as.matrix(data.cov$z),
    clustering.method = 'none',
    scale.data = FALSE,
    colour.scheme = c("grey","coral1"),
    grid.col = TRUE,
    col.colour = 'black',
    # col.lwd = 10,
    total.col = 3,
    print.colour.key = FALSE,
    yaxis.tck = 0
    );


legendG <- legend.grob(
    list(
        legend = list(
            colours = default.colours(4),
            title = "Batch",
            labels = LETTERS[1:4],
            size = 3,
            title.cex = 1,
            label.cex = 1,
            border = 'black'
            ),
        legend = list(
            colours = c("lightblue","dodgerblue2","dodgerblue4"),
            title = "Grade",
            labels = c("Low","Normal","High"),
            size = 3,
            title.cex = 1,
            label.cex = 1,
            border = 'black'
            ),
        legend = list(
            colours = c("grey","coral1"),
            title = "Biomarker",
            labels = c("Not present","Present"),
            size = 3,
            title.cex = 1,
            label.cex = 1,
            border = 'black'
            )
        ),
        label.cex = 1.25,
    	title.cex = 1.25,
    	title.just = 'left',
   	title.fontface = 'bold.italic',
   	size = 3,
   	layout = c(1,3)
    	);

create.multipanelplot(
    # filename = tempfile(pattern = 'Multipanelplot_Barchart', fileext = '.tiff'),
    plot.objects = list(bars, cov.3, cov.2, cov.1 ), 	
    plot.objects.heights = c(1, 0.1,0.1,0.1), 
    legend = list(right = list(fun = legendG)), 
    ylab.label = 'Response to Treatment', 
    main = 'Bar Chart',
    x.spacing = 0, 
    y.spacing = 0.1
    );

# Set up plots for complex example

# Dotmap
spot.sizes <- function(x) { 0.5 * abs(x); }
dotmap.dot.colours <- c('red','blue');
spot.colours <- function(x) {
    colours <- rep('white', length(x));
    colours[sign(x) == -1] <- dotmap.dot.colours[1];
    colours[sign(x) ==  1] <- dotmap.dot.colours[2];
    return(colours);
    };

# Dotmap colours
orange <- rgb(249/255, 179/255, 142/255);
blue <- rgb(154/255, 163/255, 242/255);
green <- rgb(177/255, 213/255, 181/255);
bg.colours <- c(green, orange, blue, 'gold', 'skyblue', 'plum');

dotmap <- create.dotmap(
    x = CNA[1:15,1:58],
    bg.data = SNV[1:15,1:58],
    # Set the colour scheme
    colour.scheme = bg.colours,
    # Set the breakpoints for the colour scheme (determined from the data)
    at = c(0,1,2,4,6,7,8),
    # Specify the total number of colours (+1 for the fill colour)
    total.colours = 7,
    col.colour = 'white',
    row.colour = 'white',
    bg.alpha = 1,
    yaxis.tck = c(1,0),
    fill.colour = 'grey95',
    spot.size.function = spot.sizes,
    spot.colour.function = spot.colours,
    xaxis.tck = 0,
    xaxis.lab = c(rep('',100)),
    bottom.padding = 0, 
    top.padding = 0,
    left.padding = 0,
    right.padding = 0,
    yaxis.cex = 1
    );

# Dotmap legend
dotmap.legend <- list(
    legend = list(
        colours = bg.colours,
        labels = c('Nonsynonymous','Stop Gain','Frameshift deletion', 
            'Nonframeshift deletion', 'Splicing', 'Unknown'),
        border = 'white',
        title = 'SNV',
        pch = 15
        ),
    legend = list(
        colours = dotmap.dot.colours,
        labels = c('Gain','Loss'),
        border = 'white',
        title = 'CNA',
        pch = 19
        )
    );

dotmap.legend.grob <- legend.grob(
    legends = dotmap.legend,
    title.just = 'left',
    label.cex = 0.7,
    title.cex = 0.7
    );

# Covariates
cov.colours <- c(
    c('dodgerblue','pink'),
    c('grey','darkseagreen1','seagreen2','springgreen3','springgreen4'),
    c('peachpuff','tan4')
    );

# the heatmap expects numeric data
cov.data <- patient[-c(4:9)];
cov.data[cov.data == 'male'] <- 1;
cov.data[cov.data == 'female'] <- 2;
cov.data[is.na(cov.data)] <- 3;
cov.data[cov.data == 'I'] <- 4;
cov.data[cov.data == 'II'] <- 5;
cov.data[cov.data == 'III'] <- 6;
cov.data[cov.data == 'IV'] <- 7;
cov.data[cov.data == 'MSS'] <- 8;
cov.data[cov.data == 'MSI-High'] <- 9;
cov.data$sex <- as.numeric(cov.data$sex);
cov.data$stage <- as.numeric(cov.data$stage);
cov.data$msi <- as.numeric(cov.data$msi);

covariates <- create.heatmap(
    x = cov.data,
    clustering.method = 'none',
    colour.scheme = as.vector(cov.colours),
    total.colours = 10,
    row.colour = 'white',
    col.colour = 'white',
    grid.row = TRUE,
    grid.col = TRUE,
    xaxis.lab = c(rep('',100)),
    yaxis.lab = c('Sex','Stage','MSI'),
    yaxis.tck = c(0,0),
    xaxis.tck = c(0,0),
    xat = c(1:100),
    print.colour.key = FALSE,
    yaxis.cex = 1,
    bottom.padding = 0, 
    top.padding = 0,
    left.padding = 0,
    right.padding = 0
    );

## Warning: number of columns exceeded limit (50), column lines are 
## turned off. Please set "force.grid.col" to TRUE to override this

# Coviate Legends
cov.legends <- list(
    legend = list(
        colours = cov.colours[8:9],
        labels = c('MSS','MSI-High'),
        border = 'white',
        title = 'MSI'
        ),
    legend = list(
        colours = cov.colours[3:7], 
        labels = c('NA', 'I','II','III','IV'),
        border = 'white',
        title = 'Stage'
        ),
    legend = list(
        colours = cov.colours[1:2],
        labels = c('Male','Female'),
        border = 'white',
        title = 'Sex'
        )
    );

cov.legend.grob <- legend.grob(
    legends = cov.legends,
    title.just = 'left',
    label.cex = 0.7,
    title.cex = 0.7,
    layout = c(3,1)
    );


create.multipanelplot(
    # filename = tempfile(pattern = 'Multipanelplot_with_heatmap', fileext = '.tiff'),
    plot.objects = list(dotmap,covariates), 
    plot.objects.heights = c(1,0.2), 
    y.spacing = -0.8, 
    main = 'Dotmap', 
    top.padding = 2,
    layout.height = 2,
    legend = list(
        bottom = list(
            x = 0.10,
            y = 0.50,
            fun = cov.legend.grob
            ),
        right = list(
            x = 0.10,
            y = 0.50,
            fun = dotmap.legend.grob
            )
        ),
    resolution = 300
    );

# Add more plots, using more complex layout
# grouped barplot
groupedbar.colours <- c('indianred1','indianred4');

count.SNV <- apply(SNV[1:15,], 2, function(x){length(which(!is.na(x)))});
count.CNA <- apply(CNA[1:15,], 2, function(x){length(which(!(x==0)))});

grouped.data <- data.frame(
    values = c(count.SNV, count.CNA),
    samples = rep(colnames(SNV),2),
    group = rep(c('SNV','CNA'), each = 58)
    );

grouped.barplot <- create.barplot(
    formula = values ~ samples,
    data = grouped.data,
    groups = grouped.data$group,
    col = groupedbar.colours,
    top.padding = 0,
    bottom.padding = 0,
    left.padding = 0,
    right.padding = 0,
    border.col = 'white',
    xlab.label = '',
    ylab.label = 'Mutation',
    yaxis.lab = c(0,5,10,15),
    yat = c(0,5,10,15),
    xaxis.lab = c(rep('',100)),
    yaxis.tck = c(0,0),
    xaxis.tck = c(0,0),
    ylab.cex = 1.5,
    yaxis.cex = 1,
    axes.lwd = 2
    );

# stacked barplot
col.one <- rgb(255/255, 225/255, 238/255);
col.two <- rgb(244/255, 224/255, 166/255);
col.thr <- rgb(177/255, 211/255, 154/255);
col.fou <- rgb(101/255, 180/255, 162/255);
col.fiv <- rgb(51/255, 106/255, 144/255);
stackedbar.colours <- c(col.one, col.two, col.thr, col.fou, col.fiv, 'orchid4');
stacked.data.labels <- c('C>A/G>T','C>T/G>A','C>G/G>C','T>A/A>T','T>G/A>C', 'T>C/A>G');

stacked.data <- data.frame(
    values = c(patient$prop.CAGT, patient$prop.CTGA, patient$prop.CGGC, patient$prop.TAAT, 
        patient$prop.TGAC, patient$prop.TCAG), 
    divisions = rep(rownames(patient), 6),
    group = rep(stacked.data.labels, each = 58)
    );

# Generate stacked barplot
stacked.barplot <- create.barplot(
    formula = values ~ divisions,
    data = stacked.data,
    groups = stacked.data$group,
    stack = TRUE,
    col = stackedbar.colours,
    border.col = 'white',
    main = '',
    xlab.label = '',
    ylab.label = 'Proportion',
    yaxis.lab = c(0,0.4,0.8),
    yat = c(0,0.4,0.8),
    xaxis.lab = c(rep('',100)),
	yaxis.tck = c(0,0),
    xaxis.tck = c(0,0),
    ylab.cex = 1.5,
    yaxis.cex = 1,
    axes.lwd = 2
    );

# barchart legends
stackedbar.legend <- list(
    legend = list(
        colours = rev(stackedbar.colours),
        labels = rev(stacked.data.labels),
        border = 'white'
        )
    );

groupedbar.legend <- list(
    legend = list(
        colours = groupedbar.colours,
        labels = c('CNA','SNV'),
        border = 'white'
        )
    );

groupedbar.legend.grob <- legend.grob(
    legends = groupedbar.legend,
    title.just = 'left',
    label.cex = 0.7,
    title.cex = 0.7
    );

stackedbar.legend.grob <- legend.grob(
    legends = stackedbar.legend,
    title.just = 'left',
    label.cex = 0.7,
    title.cex = 0.7
    );

# Expression change Segplot
# locate matching genes
rows.to.keep <- which(match(rownames(microarray), rownames(SNV)[1:15], nomatch = 0) > 0);

segplot.data <- data.frame(
    min = apply(microarray[rows.to.keep,1:58], 1, min),
    max = apply(microarray[rows.to.keep,1:58], 1, max),
    median = apply(microarray[rows.to.keep,1:58], 1, median),
    order = seq(1,15,1)
    );

segplot <- create.segplot(
    formula = order ~ min + max,
    data = segplot.data,
    main = '',
    xlab.label = '',
    ylab.label = '',
    centers = segplot.data$median,
    yaxis.lab = c('','','','','',''),
    xaxis.lab = c('0','2','4','6','8'),
    xat = c(0,2,4,6,8),
    yaxis.tck = c(0,0),
    xaxis.tck = c(1,0),
    axes.lwd = 2,
	
    top.padding = 0,
    left.padding = 0,
    right.padding = 0,
    bottom.padding = 0
    );
# Create multiplot

plots <- list(grouped.barplot,stacked.barplot,dotmap, segplot,covariates);
create.multipanelplot(
    main.x = 0.47,
    main.y = 0.5,
    plot.objects = plots,
    plot.objects.heights = c(0.3, 0.3, 1, 0.15),
    plot.objects.widths = c(1,0.2),
    # filename = tempfile(pattern = 'Multipanelplot_Complex', fileext = '.tiff'),
    layout.height = 4,
    layout.width = 2,
    x.spacing = 0.2,
    left.padding = 0,
    layout.skip = c(FALSE,TRUE,FALSE,TRUE,FALSE,FALSE,FALSE,TRUE),
    y.spacing = c(-1.35,-1.35,-1.5),
    ylab.axis.padding = c(1,0),
    legend = list(
        left = list(
            fun = dotmap.legend.grob,
            args = list(
                key = list(
                    points = list(
                        pch = c(15,15,19,19)
                        )
                    )
                )
            )
    ),
    height = 12,
    width = 12,
    main = 'Complex', 
    top.padding = 2
    );
# Create a multiplot with a heatmap, key like legend and barplot

# First create a heatmap object
simple.heatmap <- create.heatmap(patient[, 4:6],
   clustering.method = 'none',
   print.colour.key = FALSE,
   same.as.matrix = FALSE,
   colour.scheme = c('gray0','grey100'),
   fill.colour = 'grey95',
   xaxis.lab = c(rep('',100)),
   xat = c(0,1,2,3,4,5,6,7,8),
   yaxis.lab = c('','',''),
   yat = c(0,1,2),
   xlab.label = ''
);


# and a simple bar plot
pvals <- data.frame(
    order = c(1:3),
    pvalue = -log10(c(0.0004, 0.045, 0.0001)),
    stringsAsFactors = FALSE
        )
#create bar plot
simple.bar <- create.barplot(
    formula = order ~ rev(pvalue),
    data = pvals,
    xlimits = c(0,5),
    plot.horizontal=TRUE,
    xlab.label = '',
    ylab.label = '',
    yaxis.lab = c(1,2,3)
    );

# then the covariates heatmap
cov.colours <- c(
    c('dodgerblue','pink'),
    c('grey','darkseagreen1','seagreen2','springgreen3','springgreen4'),
    c('peachpuff','tan4')
    );

# the heatmap expects numeric data
cov.data <- patient[-c(4:9)];
cov.data[cov.data == 'male'] <- 1;
cov.data[cov.data == 'female'] <- 2;
cov.data[is.na(cov.data)] <- 3;
cov.data[cov.data == 'I'] <- 4;
cov.data[cov.data == 'II'] <- 5;
cov.data[cov.data == 'III'] <- 6;
cov.data[cov.data == 'IV'] <- 7;
cov.data[cov.data == 'MSS'] <- 8;
cov.data[cov.data == 'MSI-High'] <- 9;
cov.data$sex <- as.numeric(cov.data$sex);
cov.data$stage <- as.numeric(cov.data$stage);
cov.data$msi <- as.numeric(cov.data$msi);

covariates <- create.heatmap(
    x = cov.data,
    clustering.method = 'none',
    colour.scheme = as.vector(cov.colours),
    total.colours = 10,
    row.colour = 'white',
    col.colour = 'white',
    grid.row = TRUE,
    grid.col = TRUE,
    yaxis.tck = 0,
    print.colour.key = FALSE,
    xaxis.lab = c('','',''),
    xlab.label = '',
    xat = c(1,2,3)
    );

## Warning: number of columns exceeded limit (50), column 
## lines are turned off. Please set "force.grid.col" to TRUE to override this

covariates2 <- create.heatmap(
    x = patient[4],
    clustering.method = 'none',
    colour.scheme = c("#00007F", "#007FFF"),
    row.colour = 'white',
    col.colour = 'white',
    grid.row = TRUE,
    grid.col = TRUE,
    yaxis.tck = 0,
    print.colour.key = FALSE,
    xaxis.lab = c('','',''),
    xlab.label = '',
    xat = c(1,2,3)
    );

## Warning: number of rows exceeded limit (50), row 
## lines are turned off. Please set "force.grid.row" to TRUE to override this


cov.legends <- list(
    legend = list(
        colours = c("white", "black"),
        labels = c('0','2'),
        border = 'grey',
        title = 'Tumour Mass (kg)',
        continuous = TRUE,
        height = 3
        ),
    legend = list(
        colours = cov.colours[8:9],
        labels = c('MSS','MSI-High'),
        border = 'white',
        title = 'MSI'
        ),
    legend = list(
        colours = cov.colours[3:7],
        labels = c('NA', 'I','II','III','IV'),
        border = 'white',
        title = 'Stage'
        ),
    legend = list(
        colours = cov.colours[1:2],
        labels = c('Male','Female'),
        border = 'white',
        title = 'Sex'
        ),
    legend = list(
        colours = c("#00007F", "#007FFF"),
        labels = c('0.09','0.72'),
        border = 'grey',
        title = 'CAGT',
        continuous = TRUE,
        height = 2,
        width = 3,
        angle = -90,
        tck = 1,
        tck.number = 2,
        at = c(0,100)
        )
    );

cov.legend.grob <- legend.grob(
    legends = cov.legends,
    title.just = 'left',
    label.cex = 0.7,
    title.cex = 0.7
    );

# Now bring it was together using multiplot
create.multipanelplot(
    # filename = tempfile(pattern = 'Multipanelplot_continousLegend', fileext = '.tiff'),
    plot.objects = list(simple.heatmap, simple.bar,covariates2,covariates),
    plot.objects.heights = c(1,0.1,0.35),
    plot.objects.widths = c(1,0.25),
    layout.height = 3,
    layout.width = 2,
    layout.skip = c(FALSE, FALSE,FALSE,TRUE,FALSE,TRUE),
    y.spacing = -0.1,
    x.spacing = 0.5,
    legend = list(
        left = list(
            fun = cov.legend.grob
            )
        ),
    main = 'Continous Legend', 
    top.legend.padding = 4,
    top.padding = -2,
    left.padding = 1 
    # This parameter must be set for the legend to appear
    );

 create.multipanelplot(
    # filename = tempfile(pattern = 'Multipanelplot_manyPlots', fileext = '.tiff'),
    main = 'Large Scale',
    plot.objects = list(
	simple.boxplot,
	simple.heatmap,
	simple.bar,
	barplot.formatted,
	dotmap,
	grouped.barplot,
	stacked.barplot,
	covariates,
	covariates2,
	heatmap.formatted
	),
    plot.objects.heights = c(1,1,1,1),
    plot.objects.widths = c(1,1, 1,1),
    layout.height = 4,
    layout.width = 4,
    top.legend.padding = 3,
    layout.skip = c(FALSE, FALSE,FALSE,FALSE,FALSE,TRUE,
		TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,TRUE),
    y.spacing = c(-1,-1,-1),
    x.spacing = c(1,2,3),
    legend = list(
        left = list(
            fun = cov.legend.grob
            )
        ),
    height = 12,
    width = 12
    # This parameter must be set for the legend to appear
    );

Joins plots together

Description

Merges together multiple plots in the specified layout

Usage

create.multiplot(
	plot.objects, 
	filename = NULL, 
	panel.heights = c(1,1), 
	panel.widths = 1, 
	main = NULL, 
	main.just = "center", 
	main.x = 0.5, 
	main.y = 0.5, 
	main.cex = 3, 
	main.key.padding = 1, 
	ylab.padding = 5, 
	xlab.padding = 5, 
	xlab.to.xaxis.padding = 2, 
	right.padding = 1, 
	left.padding = 1, 
	top.padding = 0.5, 
	bottom.padding = 0.5, 
	xlab.label = NULL, 
	ylab.label = NULL, 
	xlab.cex = 2, 
	ylab.cex = 2, 
	xlab.top.label = NULL, 
	xaxis.top.tck.lab = NULL,
	xat.top = TRUE,
	xlab.top.cex = 2, 
	xaxis.top.idx = NULL,
	xlab.top.col = 'black',
	xlab.top.just = "center", 
	xlab.top.x = 0.5, 
	xlab.top.y = 0, 
	xaxis.cex = 1.5, 
	yaxis.cex = 1.5, 
	xaxis.labels = TRUE, 
	yaxis.labels = TRUE, 
	xaxis.alternating = 1, 
	yaxis.alternating = 1, 
	xat = TRUE, 
	yat = TRUE, 
	xlimits = NULL, 
	ylimits = NULL, 
	xaxis.rot = 0,
	xaxis.rot.top = 0,
	xaxis.fontface = 'bold',
	y.tck.dist=0.5,
	x.tck.dist=0.5, 
	yaxis.fontface = 'bold',
	x.spacing = 1, 
	y.spacing = 1, 
	x.relation = 'same', 
	y.relation = 'same', 
	xaxis.tck = c(0.75,0.75), 
	yaxis.tck = c(0.75,0.75), 
	axes.lwd = 1.5, 
	key.right.padding = 1, 
	key.left.padding = 1, 
	key.bottom.padding = 1, 
	xlab.key.padding = 0.5,
	height = 6, 
	width = 6, 
	size.units = 'in', 
	resolution = 1600, 
	enable.warnings = FALSE, 
	key = list(text = list(lab = c(''))), 
	legend =  NULL, 
	print.new.legend = FALSE, 
	merge.legends = FALSE, 
	plot.layout = c(1,length(plot.objects)), 
	layout.skip=rep(FALSE,length(plot.objects)), 
	description = 'Created with BoutrosLab.plotting.general', 
	plot.labels.to.retrieve = NULL,
	style = 'BoutrosLab', 
	remove.all.border.lines = FALSE,
	preload.default = 'custom',
	plot.for.carry.over.when.same = 1,
	get.dendrogram.from = NULL, 
	dendrogram.right.size = NULL, 
	dendrogram.right.x = NULL, 
	dendrogram.right.y = NULL, 
        dendrogram.top.size = NULL, 
        dendrogram.top.x = NULL, 
        dendrogram.top.y = NULL,
        use.legacy.settings = FALSE
);

Arguments

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 histogram. In particular, if a script that uses such a call of create histogram 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
    

Author(s)

Ken Chu and Denise Mak

Examples

set.seed(12345);

# begin by creating the individual plots which will be combined into a multiplot
dist <- data.frame(
    a = rnorm(100, 1), 
    b = rnorm(100, 3), 
    c = rnorm(100, 5)
    );

simple.data <- data.frame(
    x = c(dist$a, dist$b, dist$c),
    y = rep(LETTERS[1:3], each = 100)
    );

fill.squares <- matrix(c(1, 0, 0, 0, 1, 0, 0, 0, 1), ncol = 3, byrow = TRUE);
rownames(fill.squares) <- c("Drug I only", "Drug II only" , "Drugs I & II");
colnames(fill.squares) <- levels(factor(simple.data$y));

# Create plot # 1
simple.boxplot <- create.boxplot(
    formula = x ~ y,
    data = simple.data,
    col = 'lightgrey'
    );

# Create plot # 2
simple.heatmap <- create.heatmap(
        x = t(fill.squares),
        clustering.method = 'none',
        shrink = 0.8,
        colour.scheme = c("white", "grey20"),
        fill.colour = "white",
        print.colour.key = FALSE
        );

# Simple example of multiplot
# This example uses the defaults set in simple.heatmap and simple.boxplot 
create.multiplot(
    # filename = tempfile(pattern = 'Multiplot_Simple', fileext = '.tiff'),
        plot.objects = list(simple.heatmap, simple.boxplot),
        main = "Simple",
        xlab.label = c("Patient Group"),
        # The plotting function throws an error if this is not included
        ylab.label = c("Sugar Level", "Drug Regimen"),
        ylab.padding = 7,
        # Parameters set in the multiplot will override settings in individual plots
        xaxis.cex = 0.7,
        yaxis.cex = 0.7,
	resolution = 100
        );

# Simple example of multiplot with adjusted plot sizes
create.multiplot(
    # filename = tempfile(pattern = 'Multiplot_Simple_Plot_Sizes', fileext = '.tiff'),
        plot.objects = list(simple.heatmap, simple.boxplot),
        main = "Simple plot sizes",
        xlab.label = c("Patient Group"),
        # y-axis labels must be spaced with tabs or spaces to properly align
        ylab.label = c("", "Sugar Level", "", "Drug Regimen"),
        ylab.padding = 7,
        xaxis.cex = 0.7,
        yaxis.cex = 0.7,
        # Set the relative heights of the plots
        panel.heights = c(3,1),
	resolution = 100
        );

simple.violin <- create.violinplot(
    formula = x ~ y,
    data = simple.data,
    col = 'lightgrey'
    );

# Simple example of multiplot with custom layout
create.multiplot(
    # filename = tempfile(pattern = 'Multiplot_Simple_Layout', fileext = '.tiff'),
    plot.objects = list(simple.heatmap, simple.boxplot, simple.violin),
    main = "Simple layout",
    xlab.label = c("Patient Group"),
    ylab.label = c("", "Sugar Level", "", "Drug Regimen"),
    ylab.padding = 7,
    xaxis.cex = 0.7,
    yaxis.cex = 0.7,
    panel.heights = c(3,1),
    # Set how many rows & columns are in the layout
    plot.layout = c(2,2),
    # Set whether to plot or not in the space (fills from bottom left to top right)
    layout.skip = c(FALSE, TRUE, FALSE, FALSE),
    # Move plots closer together
    x.spacing = 0,
    # Remove doubled internal axis
    yat = list(
        seq(1,3,1),
        seq(-2, 8, 2),
        c()
        ),
    resolution = 100
    );

# Example of how to take parameter values from individual plots
# This programming structure allows for including the individual customization 
# of plots to the final multiplot
all_data <- data.frame(
    a = rnorm(n = 25, mean = 0, sd = 0.75),
    b = rnorm(n = 25, mean = 0, sd = 0.75),
    c = rnorm(n = 25, mean = 0, sd = 0.75),
    d = rnorm(n = 25, mean = 0, sd = 0.75),
    e = rnorm(n = 25, mean = 0, sd = 0.75),
    f = rnorm(n = 25, mean = 0, sd = 0.75),
    x = rnorm(n = 25, mean = 5),
    y = seq(1, 25, 1)
    );

plot.heatmap <- function(all_data){
    # save the parameter values that will be reused in the multiplot
    multiplot_visuals <- list(
        xlab.label = '',
        xaxis.labels = NULL,
        xat = NULL,
        ylab.label = 'Genes of Interest',
        yaxis.labels = c("BRCA1", "BRCA2", "APC", "TIN", "ARG", "FOO"),
        yat = c(1,2,3,4,5,6)
        );

    # create the plot -- this allows for previewing of the individual plot
    heatmap.formatted <- create.heatmap(
        x = all_data[,1:6],
        clustering.method = 'none',
        colour.scheme = c('magenta','white','green'),
        print.colour.key = FALSE,
        xlab.label = multiplot_visuals$xlab.label,
        xaxis.lab = multiplot_visuals$xaxis.labels,
        xat = multiplot_visuals$xat,
        ylab.label = multiplot_visuals$ylab.label,
        yaxis.lab = multiplot_visuals$yaxis.labels,
        yat = multiplot_visuals$yat 
        ); 

    # return both the plot and the relevant parameter values
    return(
        list(
            the_plot = heatmap.formatted,
            visuals = multiplot_visuals
            )
        )
}

plot.barplot <- function(all_data) {

    # save the parameter values that will be reused in the multiplot
    multiplot_visuals <- list(
        xlab.label = '',
        xaxis.labels = NULL,
        xat = NULL,
        ylab.label = 'Importance',
        yaxis.labels = seq(1, ceiling(max(all_data$x)), 1),
        yat = seq(1, ceiling(max(all_data$x)), 1)
        );

    # create the plot -- this allows for previewing of the individual plot
    barplot.formatted <- create.barplot(
        formula = x ~ y,
        data = all_data[,7:8],
        border.lwd = 0,
        col = 'grey',
        xlab.label = multiplot_visuals$xlab.label,
        xaxis.lab = multiplot_visuals$xaxis.labels,
        xat = multiplot_visuals$xat,
        ylab.label = multiplot_visuals$ylab.label,
        yaxis.lab = multiplot_visuals$yaxis.labels,
        yat = multiplot_visuals$yat
        );

    # return both the plot and the relevant parameter values
    return(
        list(
            the_plot = barplot.formatted,
            visuals = multiplot_visuals
            )
        )
    }

plot_functions <- c('plot.heatmap', 'plot.barplot');

# run the functions
all_plots <- lapply(
    plot_functions, 
    function(funName){
        eval(parse(text = paste0(funName, '(all_data)')))
        }
    );

create.multiplot(
    # filename = tempfile(pattern = 'Multiplot_Formatting', fileext = '.tiff'),
    main = "Formatting",
    plot.objects = lapply(all_plots, function(aPlot) aPlot$the_plot),
    panel.heights = c(1,3),
    xaxis.cex = 1,
    yaxis.cex = 1,
    ylab.padding = 8,
    yat = lapply(all_plots,function(aPlot) aPlot$visuals$yat),
    xlab.label = lapply(all_plots,function(aPlot) aPlot$visuals$xlab.label),
    ylab.label = rev(lapply(all_plots,function(aPlot) aPlot$visuals$ylab.label)),
    yaxis.labels = lapply(all_plots,function(aPlot) aPlot$visuals$yaxis.labels),
    resolution = 100
    );

data_bars <- data.frame(
    x = sample(x = 5:35, size = 10),
    y = seq(1,10,1)
    );

data_cov <- data.frame(
    x = rnorm(n = 10, mean = 0, sd = 0.75),
    y = rnorm(n = 10, mean = 0, sd = 0.75),
    z = rnorm(n = 10, mean = 0, sd = 0.75)
    );

# Create main barplot
bars <- create.barplot(
    formula = x~y,
    data = data_bars,
    ylimits = c(0,35),
    sample.order = 'increasing',
    border.lwd = 0
    );

# Make covariate bars out of heatmaps
cov_1 <- create.heatmap(
    x = as.matrix(data_bars$y),
    clustering.method = 'none',
    scale.data = FALSE,
    colour.scheme = default.colours(4),
    grid.col = TRUE,
    col.colour = 'black',
    # col.lwd = 10,
    total.col = 5,
    print.colour.key = FALSE,
    yaxis.tck = 0,
    axes.lwd = 0
    );

cov_2 <- create.heatmap(
    x = as.matrix(data_cov$y),
    clustering.method = 'none',
    scale.data = FALSE,
    colour.scheme = c("lightblue","dodgerblue2", "dodgerblue4"),
    grid.col = TRUE,
    col.colour = 'black',
    # col.lwd = 10,
    total.col = 4,
    print.colour.key = FALSE,
    yaxis.tck = 0
    );

cov_3 <- create.heatmap(
    x = as.matrix(data_cov$z),
    clustering.method = 'none',
    scale.data = FALSE,
    colour.scheme = c("grey","coral1"),
    grid.col = TRUE,
    col.colour = 'black',
    # col.lwd = 10,
    total.col = 3,
    print.colour.key = FALSE,
    yaxis.tck = 0
    );

# Generate legends outside of individual functions
legend <- legend.grob(
    list(
        legend = list(
            colours = default.colours(4),
            title = "Batch",
            labels = LETTERS[1:4],
            size = 3,
            title.cex = 1,
            label.cex = 1,
            border = 'black'
            ),
        legend = list(
            colours = c("lightblue","dodgerblue2","dodgerblue4"),
            title = "Grade",
            labels = c("Low","Normal","High"),
            size = 3,
            title.cex = 1,
            label.cex = 1,
            border = 'black'
            ),
        legend = list(
            colours = c("grey","coral1"),
            title = "Biomarker",
            labels = c("Not present","Present"),
            size = 3,
            title.cex = 1,
            label.cex = 1,
            border = 'black'
            )
        ),
    title.just = 'left'
    );

# Assemble plot using multiplot function
create.multiplot(
    # filename = tempfile(pattern = 'Multiplot_Barchart', fileext = '.tiff'),
    main = 'Multiplot with bar chart',
    plot.objects = list(cov_3, cov_2, cov_1, bars),
    ylab.label = c("\t", "Response to treatment","\t"),
    xlab.label = "Sample characteristics",
    panel.heights = c(1, 0.05,0.05,0.05),
    y.spacing = c(-1, -1, -1, 0),
    xaxis.lab = NULL,
    yaxis.lab = list(NULL, NULL, NULL, seq(0, 350, 50)),
    legend = list(right = list(fun = legend)),
    print.new.legend = TRUE,
    xaxis.alternating = 0,
    main.cex = 1,
    ylab.cex = 1,
    xlab.cex = 1,
    xlab.to.xaxis.padding = -2,
    yaxis.cex = 1,
    description = "Multiplot example created by BoutrosLab.plotting.general",
    resolution = 200
    );


gene_data <- data.frame(
    x = rnorm(n = 25, mean = 0, sd = 0.75),
    y = rnorm(n = 25, mean = 0, sd = 0.75),
    z = rnorm(n = 25, mean = 0, sd = 0.75),
    v = rnorm(n = 25, mean = 0, sd = 0.75),
    w = rnorm(n = 25, mean = 0, sd = 0.75),
    a = rnorm(n = 25, mean = 0, sd = 0.75),
    b = rnorm(n = 25, mean = 0, sd = 0.75),
    c = rnorm(n = 25, mean = 0, sd = 0.75)
    );

# main heatmap
main <- create.heatmap(
    x = gene_data,
    xaxis.tck = 0,
    yaxis.tck = 0,
    colourkey.cex = 1,
    clustering.method = 'none',
    axes.lwd = 1,
    ylab.label = 'y',
    xlab.label = 'x',
    yaxis.fontface = 1,
    xaxis.fontface = 1,
    xlab.cex = 1,
    ylab.cex = 1,
    main.cex = 1,
    colour.scheme = c('red','white','turquoise')
    );

key_data <- data.frame(
    x <- seq(-50,50,1)
    );

# colour key for heatmap
key <- create.heatmap(
    x = key_data,
    clustering.method = 'none',
    scale.data = FALSE,
    colour.scheme = c('turquoise','white','red'),
    print.colour.key = FALSE,
    yaxis.tck = 0,
    xat = c(10,90),
    xaxis.lab = c('low', 'high')
    );

top_data <- data.frame(
    x = rnorm(n = 25, mean = 0, sd = 0.75),
    y = seq(1,25,1)
    );

# top barplot
top <- create.barplot(
    formula = x~y,
    data = top_data,
    border.lwd = 0
    );

side_data <- data.frame(
    x = rnorm(n = 8, mean = 0, sd = 0.75),
    y = seq(1,8,1)
    );

# side barplot
side <- create.barplot(
    formula = x~y,
    data = side_data,
    border.lwd = 0,
    sample.order = 'decreasing',
    plot.horizontal = TRUE
    );

# assembling final figure
create.multiplot(
    # filename = tempfile(pattern = 'Multiplot_with_heatmap', fileext = '.tiff'),
    main = 'Multiplot with heatmap',
    plot.objects = list(key, main, side, top),
    panel.heights = c(0.25, 1, 0.05),
    panel.widths = c(1, 0.25),
    plot.layout = c(2, 3),
    layout.skip = c(FALSE, TRUE, FALSE, FALSE, FALSE, FALSE),
    xaxis.alternating = 0,
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlab.cex = 1,
    ylab.cex = 1,
    xlab.label = c('\t', 'Samples', '\t', '    Importance'),
    ylab.label = c( 'Amount (g)', '\t', '\t', 'Genes', '\t', '\t'),
    ylab.padding = 6,
    xlab.to.xaxis.padding = 0,
    xaxis.lab = list(
        c("",'low',"", "",'high', ""),
        LETTERS[1:25],
        seq(0,5,1),
        NULL
        ),
    yaxis.lab = list(
        NULL,
        replicate(8, paste(sample(LETTERS, 4, replace = TRUE), collapse = "")),
        NULL,
        seq(0,4,0.05)
        ),
    x.spacing = -0.5,
    y.spacing = c(0,-1),
    xaxis.fontface = 1,
    yaxis.fontface = 1
    );

# Set up plots for complex example

# Dotmap
spot_sizes <- function(x) { 0.5 * abs(x); }
dotmap_dot_colours <- c('red','blue');
spot_colours <- function(x) {
    colours <- rep('white', length(x));
    colours[sign(x) == -1] <- dotmap_dot_colours[1];
    colours[sign(x) ==  1] <- dotmap_dot_colours[2];
    return(colours);
    };

# Dotmap colours
orange <- rgb(249/255, 179/255, 142/255);
blue <- rgb(154/255, 163/255, 242/255);
green <- rgb(177/255, 213/255, 181/255);
bg.colours <- c(green, orange, blue, 'gold', 'skyblue', 'plum');

dotmap <- create.dotmap(
    x = CNA[1:15,1:58],
    bg.data = SNV[1:15,1:58],
    # Set the colour scheme
    colour.scheme = bg.colours,
    # Set the breakpoints for the colour scheme (determined from the data)
    at = c(0,1,2,4,6,7,8),
    # Specify the total number of colours (+1 for the fill colour)
    total.colours = 7,
    col.colour = 'white',
    row.colour = 'white',
    bg.alpha = 1,
    fill.colour = 'grey95',
    spot.size.function = spot_sizes,
    spot.colour.function = spot_colours,
    xaxis.tck = 0,
    xaxis.cex = 0.7,
    yaxis.cex = 0.7,
    xaxis.rot = 90
    );

# Dotmap legend
dotmap_legend <- list(
    legend = list(
        colours = bg.colours,
        labels = c('Nonsynonymous','Stop Gain','Frameshift deletion', 
            'Nonframeshift deletion', 'Splicing', 'Unknown'),
        border = 'white',
        title = 'SNV',
        pch = 15
        ),
    legend = list(
        colours = dotmap_dot_colours,
        labels = c('Gain','Loss'),
        border = 'white',
        title = 'CNA',
        pch = 19
        )
    );

dotmap_legend.grob <- legend.grob(
    legends = dotmap_legend,
    title.just = 'left',
    label.cex = 0.7,
    title.cex = 0.7
    );

# Covariates
cov.colours <- c(
    c('dodgerblue','pink'),
    c('grey','darkseagreen1','seagreen2','springgreen3','springgreen4'),
    c('peachpuff','tan4')
    );

# the heatmap expects numeric data
cov.data <- patient[-c(4:9)];
cov.data[cov.data == 'male'] <- 1;
cov.data[cov.data == 'female'] <- 2;
cov.data[is.na(cov.data)] <- 3;
cov.data[cov.data == 'I'] <- 4;
cov.data[cov.data == 'II'] <- 5;
cov.data[cov.data == 'III'] <- 6;
cov.data[cov.data == 'IV'] <- 7;
cov.data[cov.data == 'MSS'] <- 8;
cov.data[cov.data == 'MSI-High'] <- 9;
cov.data$sex <- as.numeric(cov.data$sex);
cov.data$stage <- as.numeric(cov.data$stage);
cov.data$msi <- as.numeric(cov.data$msi);

covariates <- create.heatmap(
    x = cov.data,
    clustering.method = 'none',
    colour.scheme = as.vector(cov.colours),
    total.colours = 10,
    row.colour = 'white',
    col.colour = 'white',
    grid.row = TRUE,
    grid.col = TRUE,
    yaxis.tck = 0,
    print.colour.key = FALSE
    );

# Coviate Legends
cov_legends <- list(
    legend = list(
        colours = cov.colours[8:9],
        labels = c('MSS','MSI-High'),
        border = 'white',
        title = 'MSI'
        ),
    legend = list(
        colours = cov.colours[3:7], 
        labels = c('NA', 'I','II','III','IV'),
        border = 'white',
        title = 'Stage'
        ),
    legend = list(
        colours = cov.colours[1:2],
        labels = c('Male','Female'),
        border = 'white',
        title = 'Sex'
        )
    );

cov_legend.grob <- legend.grob(
    legends = cov_legends,
    title.just = 'left',
    label.cex = 0.7,
    title.cex = 0.7,
    layout = c(3,1)
    );

# Multiplot of dotmap and covariates
create.multiplot(
    # filename = tempfile(pattern = 'Multiplot_Dotmap_Cov', fileext = '.tiff'),
    plot.objects = list(covariates, dotmap),
    main = 'Dotmap & covariates',
    panel.heights = c(1,0.1),
    # Set some of the yat to NULL to let R figure it out
    yat = c(seq(1,15,1), NULL),
    xat = NULL,
    yaxis.lab = list(
        c('Sex','Stage','MSI'),
        rev(rownames(SNV)[1:15])
        ),
    yaxis.cex = 0.7,
    y.spacing = -1,
    legend = list(
        bottom = list(
            x = 0.10,
            y = 0.50,
            fun = cov_legend.grob
            ),
        right = list(
            x = 0.10,
            y = 0.50,
            fun = dotmap_legend.grob
            )
        ),
    # This parameter must be set for the legend to appear
    print.new.legend = TRUE,
    # Adding spacing for the legend
    bottom.padding = 5
    );

# Add more plots, using more complex layout
# grouped barplot
groupedbar_colours <- c('indianred1','indianred4');

count.SNV <- apply(SNV[1:15,], 2, function(x){length(which(!is.na(x)))});
count.CNA <- apply(CNA[1:15,], 2, function(x){length(which(!(x==0)))});

grouped_data <- data.frame(
    values = c(count.SNV, count.CNA),
    samples = rep(colnames(SNV),2),
    group = rep(c('SNV','CNA'), each = 58)
    );

grouped_barplot <- create.barplot(
    formula = values ~ samples,
    data = grouped_data,
    groups = grouped_data$group,
    col = groupedbar_colours,
    border.col = 'white'
    );

# stacked barplot
col_one <- rgb(255/255, 225/255, 238/255);
col_two <- rgb(244/255, 224/255, 166/255);
col_thr <- rgb(177/255, 211/255, 154/255);
col_fou <- rgb(101/255, 180/255, 162/255);
col_fiv <- rgb(51/255, 106/255, 144/255);
stackedbar_colours <- c(col_one, col_two, col_thr, col_fou, col_fiv, 'orchid4');
stacked_data_labels <- c('C>A/G>T','C>T/G>A','C>G/G>C','T>A/A>T','T>G/A>C', 'T>C/A>G');

stacked_data <- data.frame(
    values = c(patient$prop.CAGT, patient$prop.CTGA, patient$prop.CGGC, patient$prop.TAAT, 
        patient$prop.TGAC, patient$prop.TCAG), 
    divisions = rep(rownames(patient), 6),
    group = rep(stacked_data_labels, each = 58)
    );

# Generate stacked barplot
stacked_barplot <- create.barplot(
    formula = values ~ divisions,
    data = stacked_data,
    groups = stacked_data$group,
    stack = TRUE,
    col = stackedbar_colours,
    border.col = 'white'
    );

# barchart legends
stackedbar_legend <- list(
    legend = list(
        colours = rev(stackedbar_colours),
        labels = rev(stacked_data_labels),
        border = 'white'
        )
    );

groupedbar_legend <- list(
    legend = list(
        colours = groupedbar_colours,
        labels = c('CNA','SNV'),
        border = 'white'
        )
    );

groupedbar_legend.grob <- legend.grob(
    legends = groupedbar_legend,
    title.just = 'left',
    label.cex = 0.7,
    title.cex = 0.7
    );

stackedbar_legend.grob <- legend.grob(
    legends = stackedbar_legend,
    title.just = 'left',
    label.cex = 0.7,
    title.cex = 0.7
    );

# Expression change Segplot
# locate matching genes
rows.to.keep <- which(match(rownames(microarray), rownames(SNV)[1:15], nomatch = 0) > 0);

segplot.data <- data.frame(
    min = apply(microarray[rows.to.keep,1:58], 1, min),
    max = apply(microarray[rows.to.keep,1:58], 1, max),
    median = apply(microarray[rows.to.keep,1:58], 1, median),
    order = seq(1,15,1)
    );

segplot <- create.segplot(
    formula = order ~ min + max,
    data = segplot.data,
    main = 'Medians',
    centers = segplot.data$median,
    pch = 15
    );

# Create multiplot
plots <- list(covariates, dotmap, segplot, stacked_barplot, grouped_barplot);

create.multiplot(
    # filename = tempfile(pattern = 'Multiplot_Complex', fileext = '.tiff'),
    main = 'Complex',
    # These dimensions make the plot look much more proportional
    width = 12,
    height = 8,
    plot.objects = plots,
    panel.heights = c(0.2, 0.2, 1, 0.1),
    panel.widths = c(1,0.1),
    plot.layout = c(2, 4),
    layout.skip = c(FALSE,TRUE,FALSE,FALSE,FALSE,TRUE,FALSE,TRUE),
    xaxis.lab = list(
        NULL,
        NULL,
        seq(0,14,2),
        NULL,
        NULL),
    yaxis.lab = list(
        c('Sex','Stage','MSI'),
        rownames(SNV)[1:15],
        NULL,
        seq(0.0,1.0,0.2),
        seq(0,16,4)
        ),
    x.spacing = -0.5,
    y.spacing = -1.5,
    xaxis.cex = 0.7,
    yaxis.cex = 0.7,
    xat = list(
        NULL,
        NULL,
        seq(0,10,2.5),
        NULL,
        NULL
        ),
    yat = list(
        seq(1,3,1),
        seq(1,15,1),
        NULL,
        seq(0.0,1.0,0.2),
        seq(0,16,4)
        ),
    ylab.label = c( 'Mutation', 'Proportion','\t','\t','\t','\t','\t'),
    ylab.cex = 0.7,
    xlab.cex = 0.7,
    xlab.to.xaxis.padding = 2,
    key.bottom.padding = 5,
    bottom.padding = 5,
    right.padding = 8,
    legend = list(
        bottom = list(
            x = 0.10,
            y = 0.50,
            fun = cov_legend.grob
            ),
        inside = list(
            x = 0.91,
            y = 0.96,
            fun = groupedbar_legend.grob
            ),
        inside = list(
            x = 0.91,
            y = 0.86,
            fun = stackedbar_legend.grob
            ),
        left = list(
            fun = dotmap_legend.grob,
            args = list(
                key = list(
                    points = list(
                        pch = c(15,15,19,19)
                        )
                    )
                )
            )
        ),
    print.new.legend = TRUE,
    resolution = 200
    );

# Nature style
create.multiplot(
    # filename = tempfile(pattern = 'Multiplot_Nature_style', fileext = '.tiff'),
    plot.objects = list(simple.heatmap, simple.boxplot),
    main = "Nature style",
    ylab.padding = 7,
    xaxis.cex = 0.7,
    yaxis.cex = 0.7,

    # set style to Nature 
    style = 'Nature',
    
    # demonstrating how to italicize character variables
    ylab.label = c(expression(paste('italicized ', italic('a'))), 
    expression(paste('italicized ', italic('b')))),
  
    # demonstrating how to create en-dashes
    xlab.label = c(expression(paste('en dashs: 1','\u2013', '10'^'\u2013', ''^3))),
    resolution = 200
    );

# Create a multiplot with a heatmap, key like legend and barplot

# First create a heatmap object
simple.heatmap <- create.heatmap(patient[, 4:6],
    clustering.method = 'none',
    print.colour.key = FALSE,
    scale=TRUE,
    same.as.matrix = FALSE,
    colour.scheme = c('gray0','grey100'),
    fill.colour = 'grey95'
);

 
# and a simple bar plot
pvals <- data.frame(
        order = c(1:3),
        pvalue = -log10(c(0.0004, 0.045, 0.0001)),
        stringsAsFactors = FALSE
        )
#create bar plot
simple.bar <- create.barplot(
        formula = order ~ rev(pvalue),
        data = pvals,
        xlimits = c(0,5),
        plot.horizontal=TRUE
        );

# then the covariates heatmap
cov.colours <- c(
    c('dodgerblue','pink'),
    c('grey','darkseagreen1','seagreen2','springgreen3','springgreen4'),
    c('peachpuff','tan4')
    );

# the heatmap expects numeric data
cov.data <- patient[-c(4:9)];
cov.data[cov.data == 'male'] <- 1;
cov.data[cov.data == 'female'] <- 2;
cov.data[is.na(cov.data)] <- 3;
cov.data[cov.data == 'I'] <- 4;
cov.data[cov.data == 'II'] <- 5;
cov.data[cov.data == 'III'] <- 6;
cov.data[cov.data == 'IV'] <- 7;
cov.data[cov.data == 'MSS'] <- 8;
cov.data[cov.data == 'MSI-High'] <- 9;
cov.data$sex <- as.numeric(cov.data$sex);
cov.data$stage <- as.numeric(cov.data$stage);
cov.data$msi <- as.numeric(cov.data$msi);

covariates <- create.heatmap(
    x = cov.data,
    clustering.method = 'none',
    colour.scheme = as.vector(cov.colours),
    total.colours = 10,
    row.colour = 'white',
    col.colour = 'white',
    grid.row = TRUE,
    grid.col = TRUE,
    yaxis.tck = 0,
    print.colour.key = FALSE
    );
covariates2 <- create.heatmap(
    x = patient[4],
    clustering.method = 'none',
    colour.scheme = c("#00007F", "#007FFF"),
    row.colour = 'white',
    col.colour = 'white',
    grid.row = TRUE,
    grid.col = TRUE,
    yaxis.tck = 0,
    print.colour.key = FALSE
    );

cov_legends <- list(
    legend = list(
        colours = c("white", "black"),
        labels = c('0','2'),
        border = 'grey',
        title = 'Tumour Mass (kg)',
        continuous = TRUE,
	height = 3
        ),
    legend = list(
        colours = cov.colours[8:9],
        labels = c('MSS','MSI-High'),
        border = 'white',
        title = 'MSI'
        ),
    legend = list(
        colours = cov.colours[3:7],
        labels = c('NA', 'I','II','III','IV'),
        border = 'white',
        title = 'Stage'
        ),
    legend = list(
        colours = cov.colours[1:2],
        labels = c('Male','Female'),
        border = 'white',
        title = 'Sex'
        ),
    legend = list(
        colours = c("#00007F", "#007FFF"),
        labels = c('0.09','0.72'),
        border = 'grey',
        title = 'CAGT',
	continuous = TRUE,
	height = 2,
        width = 3,
	angle = -90,
	tck = 1,
	tck.number = 2,
	at = c(0,100)
        )
    );

cov_legend.grob <- legend.grob(
    legends = cov_legends,
    title.just = 'left',
    label.cex = 0.7,
    title.cex = 0.7
    );

# Now bring it was together using multiplot
create.multiplot(
    main = 'multiplot with colour key legend',
    main.cex = 1,
    # filename = tempfile(pattern = 'MultiPlot_With_ColorKey_Legend', fileext = '.tiff'),
    plot.objects = list(covariates, covariates2, simple.heatmap, simple.bar),
    panel.heights = c(1,0.1,0.35),
    panel.widths = c(1,0.25),
    plot.layout = c(2,3),
    layout.skip = c(FALSE, TRUE, FALSE, TRUE,FALSE,FALSE),
    xaxis.alternating = 1,
    # Set some of the yat to NULL to let R figure it out
    yaxis.lab = list(
        c('Sex','Stage','MSI'),
        NULL,
	c('one','two','three'),
        NULL
        ),
    xaxis.lab = list(
        NULL,
        NULL,
	NULL,
        seq(0,5,1)
    ),
    xat = list(
        NULL,
        NULL,
	NULL,
        seq(0,5,1)
    ),
    yaxis.tck = 0,
    xlab.to.xaxis.padding = 0,
    yaxis.cex = 0.5,
    xaxis.cex = 0.5,
    xlab.cex = 0.75,
    ylab.cex = 0.75,
    xlab.label = c('\t', 'samples', '\t', '    -log10 pval'),
    ylab.label = c("", "Test", "","CAGT",  "covariates"),
    y.spacing = 0,
    x.spacing = 0,
    legend = list(
        left = list(
            x = 0.10,
            y = 0.50,
            fun = cov_legend.grob
            )
        ),
    left.padding = 2.5,
    # This parameter must be set for the legend to appear
    print.new.legend = TRUE
    );

BarPlotDataRetLabels <- data.frame(x = c("test1","test2","test3","test4"), 
				   y = c(10000,13000,12000,6700))
HeatMapDataRetLabels <- matrix(nrow = 4, ncol = 4, data = rnorm(16,1,1))

bpRet <- create.barplot(
	formula = y~x, 
	data = BarPlotDataRetLabels, 
	xaxis.lab = NULL, 
	xat = 0
	);
hmRet <- create.heatmap(
	x= HeatMapDataRetLabels, 
	yaxis.lab = c("Gene 1", "Gene 2", "Gene 3", "Gene 4"), 
	yat = c(1,2,3,4), 
	clustering.method = 'none'
	);

create.multiplot(
	# filename = tempfile(pattern = 'Multiplot_RetrievePlotLabels', fileext = '.tiff'),
	plot.objects = list(hmRet,bpRet,bpRet), 
	print.new.legend = TRUE,
	xlab.label = c('Samples'),
	ylab.padding = 12,
	y.spacing = c(0,0),
	panel.heights = c(0.25,1,0.25),
	plot.labels.to.retrieve = c(1,2,3)
	);

create.multiplot(
    # filename = tempfile(pattern = 'Multiplot_Retrieve_Specefic_Labels', fileext = '.tiff'),
        plot.objects = list(simple.heatmap, simple.boxplot),
        main = "Simple",
        xlab.label = c("Patient Group"),
	xaxis.labels = c("1","Drug Regimen"),
        # The plotting function throws an error if this is not included
        ylab.label = c("Sugar Level", "Drug Regimen"),
        ylab.padding = 7,
        # Parameters set in the multiplot will override settings in individual plots
        xaxis.cex = 0.7,
        yaxis.cex = 0.7,
	yaxis.labels = c(NA,NA),
	xat = list(TRUE,TRUE),
	yat = list(TRUE,TRUE),
	plot.labels.to.retrieve = c(1),
	xlimits = list(NULL,c("A","B","C")),
	ylimits = list(NULL,c(-3,10))
        );

# Dendrogram provided
dist <- data.frame(
    a = rnorm(100, 1), 
    b = rnorm(100, 3), 
    c = rnorm(100, 5)
    );

simple.data <- data.frame(
    x = c(dist$a, dist$b, dist$c),
    y = rep(LETTERS[1:3], each = 100)
    );
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'
    );

simple.boxplot <- create.boxplot(
    formula = x ~ y,
    data = simple.data,
    col = 'lightgrey'
    );

simple.heatmap <- create.heatmap(
    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'
    );
    
legend <- legend.grob(
    list(
        legend = list(
            colours = default.colours(4),
            title = "Batch",
            labels = LETTERS[1:4],
            size = 3,
            title.cex = 1,
            label.cex = 1,
            border = 'black'
            ),
        legend = list(
            colours = c("lightblue","dodgerblue2","dodgerblue4"),
            title = "Grade",
            labels = c("Low","Normal","High"),
            size = 3,
            title.cex = 1,
            label.cex = 1,
            border = 'black'
            ),
        legend = list(
            colours = c("grey","coral1"),
            title = "Biomarker",
            labels = c("Not present","Present"),
            size = 3,
            title.cex = 1,
            label.cex = 1,
            border = 'black'
            )
        ),
    title.just = 'left'
    );
create.multiplot(
        # filename = tempfile(pattern = 'MultiPlot_getDendrograms', fileext = '.tiff'),
        plot.objects = list(simple.heatmap, simple.boxplot),
        main = "Simple",
        xlab.label = c("Patient Group"),
	y.spacing = 3,
        # The plotting function throws an error if this is not included
        ylab.label = c("Sugar Level", "Drug Regimen"),
        ylab.padding = 7,
        # Parameters set in the multiplot will override settings in individual plots
        xaxis.cex = 0.7,
        yaxis.cex = 0.7,
	yaxis.lab = list(
		c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20),
		c(-2,-1,0,1,2,3,4,5)
		),
	xaxis.lab = list(c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15, 16,17,18,19,20),c(1,2,3)),
	xaxis.rot = 45,
	xaxis.rot.top = 90,
    	legend = list(right = list(fun = legend)),
	print.new.legend = TRUE,
	get.dendrogram.from = 1, 
	dendrogram.right.size = 0.40, dendrogram.right.x = 29, dendrogram.right.y = 67,
	dendrogram.top.size = 1, dendrogram.top.x = 110, dendrogram.top.y = -180
        );

Make a polygonplot

Description

Takes a data.frame and creates a polygon

Usage

create.polygonplot(
	formula,
	data,
	filename = NULL,
	groups = NULL,
	main = NULL,
	main.just = 'center',
	main.x = 0.5,
	main.y = 0.5,
	main.cex = 3,
	max,
	min,
	col = 'white',
	alpha = 0.5,
	border.col = 'black',
	strip.col = 'white',
	strip.cex = 1,
	type = 'p',
	cex = 0.75,
	pch = 19,
	lwd = 1,
	lty = 1,
	axes.lwd = 1,
	xlab.label = tail(sub('~', '', formula[-2]), 1),
	ylab.label = tail(sub('~', '', formula[-3]), 1),
	xlab.cex = 2,
	ylab.cex = 2,
	xlab.col = 'black',
	ylab.col = 'black',
	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,
	xaxis.lab = TRUE,
	yaxis.lab = TRUE,
	xaxis.cex = 1.5,
	yaxis.cex = 1.5,
	xaxis.rot = 0,
	yaxis.rot = 0,
	xaxis.log = FALSE,
	yaxis.log = FALSE,
	xaxis.fontface = 'bold',
	yaxis.fontface = 'bold',
	xaxis.col = 'black',
	yaxis.col = 'black',
	xaxis.tck = 1,
	yaxis.tck = 1,
	xlimits = NULL,
	ylimits = NULL,
	xat = TRUE,
	yat = TRUE,
	layout = NULL,
	as.table = FALSE,
	x.spacing = 0,
	y.spacing = 0,
	x.relation = 'same',
	y.relation = 'same',
	top.padding = 0.5,
	bottom.padding = 2,
	right.padding = 1,
	left.padding = 2,
	ylab.axis.padding = 0,
	add.border = FALSE,
	add.xy.border = NULL,
	add.median = FALSE,
	median.lty = 3,
	median.lwd = 1.5,
	use.loess.border = FALSE,
	use.loess.median = FALSE,
	median = NULL,
	median.col = 'black',
	extra.points = NULL,
	extra.points.pch = 21,
	extra.points.type = 'p',
	extra.points.col = 'black',
	extra.points.fill = 'white',
	extra.points.cex = 1,
	add.rectangle = FALSE,
	xleft.rectangle = NULL,
	ybottom.rectangle = NULL,
	xright.rectangle = NULL,
	ytop.rectangle = NULL,
	col.rectangle = 'transparent',
	alpha.rectangle = 1,
	xgrid.at = xat,
	ygrid.at = yat,
	grid.lty = 1,
	grid.col = 'grey',
	grid.lwd = 0.3,
	add.xyline = FALSE,
	xyline.col = 'black',
	xyline.lwd = 1,
	xyline.lty = 1,
	abline.h = NULL,
	abline.v = NULL,
	abline.col = 'black',
	abline.lwd = 1,
	abline.lty = 1,
	add.text = FALSE,
	text.labels = NULL,
	text.x = NULL,
	text.y = NULL,
	text.col = 'black',
	text.cex = 1,
	text.fontface = 'bold',
	key = NULL,
	legend = NULL,
	height = 6,
	width = 6,
	size.units = 'in',
	resolution = 1600,
	enable.warnings = FALSE,
	description = 'Created with BoutrosLab.plotting.general',
	style = 'BoutrosLab',
	preload.default = 'custom',
	use.legacy.settings = FALSE,
	inside.legend.auto = FALSE
	);

Arguments

.

,

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 histogram. In particular, if a script that uses such a call of create histogram 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
    

Author(s)

Denise Mak

See Also

xyplot, lattice or the Lattice book for an overview of the package.

Examples

set.seed(12345);

temp  <- matrix(runif(1010), ncol = 10) + sort(runif(101));

simple.data <- data.frame(
    x = 0:100,
    max = apply(temp, 1, max),
    min = apply(temp, 1, min)
    );

create.polygonplot(
    # filename = tempfile(pattern = 'Polygon_Simple', fileext = '.tiff'),
    formula = NA ~ x,
    data = simple.data,
    max = simple.data$max,
    min = simple.data$min,
    main = 'Simple',
    xlimits = c(0,100),
    ylimits = c (0,2),
    col = default.colours(1),
    description = 'Polygon created by BoutrosLab.plotting.general',
    resolution = 100
    );

# Compare two genes across increasing numbers of samples
data1 <- microarray[1,1:58];
data2 <- microarray[2,1:58];

gene1 <- as.data.frame(matrix(nrow = 58, ncol = 58));
gene2 <- as.data.frame(matrix(nrow = 58, ncol = 58));

fill.matrix <- function(x, gene, data){
    for(i in x){
        gene[i, 1:i] <- rep(NA, i);
        gene[i, i:58] <- rep(as.numeric(data[i]), 58-i+1);
        }
    return(gene);
    };

gene1 <- fill.matrix(1:58, gene1, data1);
gene1 <- t(matrix(unlist(gene1), ncol = 58, byrow = TRUE));

gene2 <- fill.matrix(1:58, gene2, data2);
gene2 <- t(matrix(unlist(gene2), ncol = 58, byrow = TRUE));

# Set up the data
polygon.data.gene1 <- data.frame(
    x = 1:58,
    max = apply(gene1, 2, function(x) {max(x, na.rm = TRUE)}),
    median = apply(gene1, 2, function(x) {median(x, na.rm = TRUE)}),
    min = apply(gene1, 2, function(x) {min(x, na.rm = TRUE)}),
    set = rownames(microarray[1,]),
    extra = apply(microarray[1:58], 2, function(x) {median(x)})
    );

polygon.data.two.genes <- rbind(
    polygon.data.gene1,
    data.frame(
        x = 1:58,
        max = apply(gene2, 2, function(x) {max(x, na.rm = TRUE)}),
        median = apply(gene2, 2, function(x) {median(x, na.rm = TRUE)}),
        min = apply(gene2, 2, function(x) {min(x, na.rm = TRUE)}),
        set = rownames(microarray[2,]),
        extra = apply(microarray[1:58], 2, function(x) {median(x)})
        )
    )

# Minimal Input
create.polygonplot(
    # filename = tempfile(pattern = 'Polygon_Minimal_Input', fileext = '.tiff'),
    formula = NA ~ x,
    data = polygon.data.gene1,
    max = polygon.data.gene1$max,
    min = polygon.data.gene1$min,
    main = 'Minimal input',
    xlimits = c(0,58),
    ylimits = c (2,5),
    description = 'Polygon created by BoutrosLab.plotting.general',
    resolution = 100
    );

# Axes & Labels
create.polygonplot(
    # filename = tempfile(pattern = 'Polygon_Axes_Labels', fileext = '.tiff'),
    formula = NA ~ x,
    data = polygon.data.gene1,
    max = polygon.data.gene1$max,
    min = polygon.data.gene1$min,
    main = 'Axes & labels',
    xlimits = c(0,58),
    ylimits = c (0,10),
    # Axes & Labels
    xlab.label = 'Samples',
    ylab.label = 'Value',
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    xat = seq(0, 58, 5),
    yat = seq(0, 10, 2),
    description = 'Polygon created by BoutrosLab.plotting.general',
    resolution = 100
    );

# Colour
create.polygonplot(
    # filename = tempfile(pattern = 'Polygon_Colour', fileext = '.tiff'),
    formula = NA ~ x,
    data = polygon.data.gene1,
    max = polygon.data.gene1$max,
    min = polygon.data.gene1$min,
    main = 'Colour',
    xlimits = c(0,58),
    ylimits = c (0,10),
    xlab.label = 'Samples',
    ylab.label = 'Value',
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    xat = seq(0, 58, 5),
    yat = seq(0, 10, 2),
    # Colour
    col = default.colours(1),
    description = 'Polygon created by BoutrosLab.plotting.general',
    resolution = 100
    );

# Add median line and points
create.polygonplot(
    # filename = tempfile(pattern = 'Polygon_Median_Points', fileext = '.tiff'),
    formula = NA ~ x,
    data = polygon.data.gene1,
    max = polygon.data.gene1$max,
    min = polygon.data.gene1$min,
    # Median
    median = polygon.data.gene1$median,
    add.median = TRUE,
    main = 'Plotting character',
    xlimits = c(0,58),
    ylimits = c (0,10),
    xlab.label = 'Samples',
    ylab.label = 'Value',
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    xat = seq(0, 58, 5),
    yat = seq(0, 10, 2),
    col = default.colours(1),
    # border points
    add.border = TRUE,
    description = 'Polygon created by BoutrosLab.plotting.general',
    resolution = 100
    );


# Additional Data
create.polygonplot(
    # filename = tempfile(pattern = 'Polygon_Extra_Data', fileext = '.tiff'),
    formula = NA ~ x,
    # divide data
    groups = set,
    data = polygon.data.two.genes,
    max = polygon.data.two.genes$max,
    min = polygon.data.two.genes$min,
    main = 'Two data sets',
    median = polygon.data.two.genes$median,
    add.median = TRUE,
    xlimits = c(0,58),
    ylimits = c (0,15),
    xlab.label = 'Samples',
    ylab.label = 'Value',
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    xat = seq(0, 58, 5),
    yat = seq(0, 14, 2),
    # Increasing number of colours
    col = default.colours(2),
    description = 'Polygon created by BoutrosLab.plotting.general',
    resolution = 100
    );

# Legend
create.polygonplot(
    # filename = tempfile(pattern = 'Polygon_Legend', fileext = '.tiff'),
    formula = NA ~ x,
    groups = set,
    data = polygon.data.two.genes,
    max = polygon.data.two.genes$max,
    min = polygon.data.two.genes$min,
    main = 'Legend',
    median = polygon.data.two.genes$median,
    add.median = TRUE,
    xlimits = c(0,58),
    ylimits = c (0,15),
    xlab.label = 'Samples',
    ylab.label = 'Value',
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    xat = seq(0, 58, 5),
    yat = seq(0, 14, 2),
    col = default.colours(2),
    # Adding legend
    key = list(
        text = list(
            lab = rownames(microarray[1:2,]),
            cex = 0.8,
            col = 'black'
            ),
        points = list(
            pch = 15,
            col = default.colours(2),
            cex = 2
            ),
        x = 0.04,
        y = 0.93,
        padding.text = 3,
        columns = 1
        ),
    description = 'Polygon created by BoutrosLab.plotting.general',
    resolution = 200
    );

# Panel Organiation
create.polygonplot(
    # filename = tempfile(pattern = 'Polygon_Panel', fileext = '.tiff'),
    # divide data
    formula = NA ~ x | set,
    data = polygon.data.two.genes,
    max = polygon.data.two.genes$max,
    min = polygon.data.two.genes$min,
    main = 'Panel',
    median = polygon.data.two.genes$median,
    add.median = TRUE,
    xlimits = c(0,58),
    ylimits = c (0,15),
    xlab.label = 'Samples',
    ylab.label = 'Value',
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    xat = seq(0, 58, 5),
    yat = seq(0, 14, 2),
    col = default.colours(1),
    description = 'Polygon created by BoutrosLab.plotting.general',
    resolution = 200
    );

# Extra Points
create.polygonplot(
    # filename = tempfile(pattern = 'Polygon_Extra_Points', fileext = '.tiff'),
    formula = NA ~ x,
    groups = set,
    data = polygon.data.two.genes,
    max = polygon.data.two.genes$max,
    min = polygon.data.two.genes$min,
    main = 'Extra points',
    median = polygon.data.two.genes$median,
    add.median = TRUE,
    xlimits = c(0,58),
    ylimits = c (0,15),
    xlab.label = 'Samples',
    ylab.label = 'Value',
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    xat = seq(0, 58, 5),
    yat = seq(0, 14, 2),
    col = default.colours(2),
    # Add to legend
    key = list(
        text = list(
            lab = c(rownames(microarray[1:2,]), 'All genes'),
            cex = 0.8,
            col = 'black'
            ),
        points = list(
            pch = c(15, 15, 3),
            col = c(default.colours(2), 'red'),
            cex = c(2, 2, 0.7)
            ),
        x = 0.04,
        y = 0.93,
        padding.text = 3,
        columns = 1
        ),
    # Extra points
    extra.points = list(
        x = polygon.data.two.genes$x,
        y = polygon.data.two.genes$extra
        ),
    extra.points.col = 'red',
    extra.points.pch = 3,
    extra.points.type = c('p', 'l'),
    extra.points.cex = 0.7,
    description = 'Polygon created by BoutrosLab.plotting.general',
    resolution = 200
    );

# Nature style
create.polygonplot(
    # filename = tempfile(pattern = 'Polygon_Nature_style', fileext = '.tiff'),
    formula = NA ~ x,
    groups = set,
    data = polygon.data.two.genes,
    max = polygon.data.two.genes$max,
    min = polygon.data.two.genes$min,
    main = 'Nature style',
    median = polygon.data.two.genes$median,
    add.median = TRUE,
    xlimits = c(0,58),
    ylimits = c (0,15),
    xaxis.cex = 1,
    yaxis.cex = 1,
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    xat = seq(0, 58, 5),
    yat = seq(0, 14, 2),
    col = default.colours(2),
    # Adding legend
    key = list(
        text = list(
            lab = rownames(microarray[1:2,]),
            cex = 0.8,
            col = 'black'
            ),
        points = list(
            pch = 15,
            col = default.colours(2),
            cex = 2
            ),
        x = 0.04,
        y = 0.93,
        padding.text = 3,
        columns = 1
        ),

    # 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 = 'Polygon created by BoutrosLab.plotting.general',
    resolution = 1200
    );

Make a quantile-quantile plot of two samples

Description

Takes two samples and creates a qq plot for comparing two distributions, possibly conditioned on other variables

Usage

create.qqplot.comparison(
	x,
	data = NULL,
	filename = NULL,
	groups = NULL,
	main = NULL,
	main.just = 'center',
	main.x = 0.5,
	main.y = 0.5,
	main.cex = 3,
	aspect = 'fill',
	prepanel = NULL,
	xlab.label = NULL,
	ylab.label = NULL,
	xlab.cex = 2,
	ylab.cex = 2,
	xlab.col = 'black',
	ylab.col = 'black',
	xlimits = NULL,
	ylimits = NULL,
	xat = TRUE,
	yat = TRUE,
	xaxis.lab = NA,
	yaxis.lab = NA,
	xaxis.cex = 1.5,
	yaxis.cex = 1.5,
	xaxis.fontface = 'bold',
	yaxis.fontface = 'bold',
	xaxis.log = FALSE,
	yaxis.log = FALSE,
	xaxis.rot = 0,
	yaxis.rot = 0,
	xaxis.col = 'black',
	yaxis.col = 'black',
	xaxis.tck = 1,
	yaxis.tck = 1,
	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,
	add.grid = FALSE,
	xgrid.at = xat,
	ygrid.at = yat,
	type = 'p',
	cex = 0.75,
	pch = 19,
	col = 'black',
	lwd = 1,
	lty = 1,
	axes.lwd = 2.25,
	key = list(text = list(lab = c(''))),
	legend = NULL,
	add.rectangle = FALSE,
	xleft.rectangle = NULL,
	ybottom.rectangle = NULL,
	xright.rectangle = NULL,
	ytop.rectangle = NULL,
	col.rectangle = 'transparent',
	alpha.rectangle = 1,
	top.padding = 3,
	bottom.padding = 0.7,
	left.padding = 0.5,
	right.padding = 0.1,
	height = 6,
	width = 6,
	size.units = 'in',
	resolution = 1600,
	enable.warnings = FALSE,
	description = 'Created with BoutrosLab.plotting.general',
	style = 'BoutrosLab',
	preload.default = 'custom',
	use.legacy.settings = FALSE,
	inside.legend.auto = FALSE
	);

Arguments

Value

If filename is NULL then returns the trellis object, otherwise creates a plot and returns a 0/1 success code.

Author(s)

Ying Wu

See Also

qq, lattice or the Lattice book for an overview of the package.

Examples

set.seed(12345);

create.qqplot.comparison(
    # filename = tempfile(pattern = 'QQcomparison_Simple', fileext = '.tiff'),
    x = list(rnorm(100), rnorm(100)),
    resolution = 50
    );

# Minimal Input
create.qqplot.comparison(
    # filename = tempfile(pattern = 'QQcomparison_Minimal_Input', fileext = '.tiff'),
    x = list(microarray[1:500,2], microarray[1:500,2]),
    main = 'Minimal input',
    description = 'QQplot comparison created by BoutrosLab.plotting.general',
    resolution = 50
    );

# Axes & Labels
create.qqplot.comparison(
    # filename = tempfile(pattern = 'QQcomparison_Axes_Labels', fileext = '.tiff'),
    x = list(microarray[1:500,2], microarray[1:500,2]),
    main = 'Axes & labels',
    # adding axes and labels
    xlab.label = 'Sample 1',
    ylab.label = 'Sample 2',
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    # adding grid for good measure
    add.grid = TRUE,
    description = 'QQplot comparison created by BoutrosLab.plotting.general',
    resolution = 100
    );

# Formula input

# 'Formula' format of data
chr.locations <- microarray$Chr[1:500];
chr.locations <- replace(chr.locations, which(chr.locations == 1), 'Chromosome 1');
chr.locations <- replace(chr.locations, which(chr.locations == 2), 'Chromosome 2');

qqplot.data <- data.frame(
    sample = c(rep('Sample 1', 500), rep('Sample 2', 500)),
    value = c(microarray[1:500,1], microarray[1:500,2]),
    chr = chr.locations
    );


create.qqplot.comparison(
    # filename = tempfile(pattern = 'QQcomparison_Formula', fileext = '.tiff'),
    # Using a different input method
    x = sample ~ value,
    data = qqplot.data,
    main = 'Formula input',
    xlab.label = 'Sample 1',
    ylab.label = 'Sample 2',
    xaxis.lab = seq(0, 15, 5),
    yaxis.lab = seq(0, 15, 5),
    xlimits = c(0, 17),
    ylimits = c(0, 17),
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    add.grid = TRUE,
    description = 'QQplot comparison created by BoutrosLab.plotting.general',
    resolution = 200
    );


# Groups & Legend
create.qqplot.comparison(
 # filename = tempfile(pattern = 'QQcomparison_Groups_Legend', fileext = '.tiff'),
    x = sample ~ value,
    data = qqplot.data,
    # Using fake grouping for the sake of illustration
    groups = qqplot.data$chr,
    # Set colours to differente the gruops
    col = default.colours(3),
    # Setting different plotting characters
    pch = c(15, 16),
    main = 'Groups & legend',
    xlab.label = 'Sample 1',
    ylab.label = 'Sample 2',
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    add.grid = TRUE,
    # Adding legend to explain groups
    key = list(
        text = list(
            lab = c('1','2'),
            cex = 1.5,
            col = 'black'
            ),
        points = list(
            pch = c(15, 16),
            col = default.colours(2),
            cex = 1
            ),
        x = 0.04,
        y = 0.95,
        padding.text = 2
        ),
    description = 'QQplot comparison created by BoutrosLab.plotting.general',
    resolution = 200
    );

# Multiple qq plots
create.qqplot.comparison(
    # filename = tempfile(pattern = 'QQcomparison_Multiple', fileext = '.tiff'),
    x = sample ~ value | chr,
    data = qqplot.data,
    main = 'Multiple plots',
    xlab.label = 'Sample 1',
    ylab.label = 'Sample 2',
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    add.grid = TRUE,
    description = 'QQplot comparison created by BoutrosLab.plotting.general',
    resolution = 200
    );

# Nature style
create.qqplot.comparison(
    # filename = tempfile(pattern = 'QQcomparison_Nature_style', fileext = '.tiff'),
    x = sample ~ value,
    data = qqplot.data,
    main = 'Nature style',
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    add.grid = TRUE,

   # 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 = 'QQplot comparison created by BoutrosLab.plotting.general',
    resolution = 200
    );

Make a quantile-quantile plot of a sample

Description

Takes a sample and creates a qq plot against a theoretical distribution, possibly conditioned on other variables.

Usage

create.qqplot.fit(
	x,
	data = NA,
	filename = NULL,
	groups = NULL,
	confidence.bands = FALSE,
	conf = 0.95,
        confidence.method = 'both',
	reference.line.method = 'quartiles',
	distribution = qnorm,
	aspect = 'fill',
        prepanel = NULL,
	main = NULL,
	main.just = 'center',
	main.x = 0.5,
	main.y = 0.5,
	main.cex = 3,
        xlab.label = NULL,
	ylab.label = NULL,
	xlab.cex = 2,
	ylab.cex = 2,
	xlab.col = 'black',
	ylab.col = 'black',
        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,
	xlimits = NULL,
	ylimits = NULL,
	xat = TRUE,
	yat = TRUE,
	xaxis.lab = NA,
	yaxis.lab = NA,
        xaxis.cex = 1.5,
	yaxis.cex = 1.5,
	xaxis.col = 'black',
	yaxis.col = 'black',
	xaxis.fontface = 'bold',
        yaxis.fontface = 'bold',
	xaxis.log = FALSE,
	yaxis.log = FALSE,
	xaxis.rot = 0,
	yaxis.rot = 0,
	xaxis.tck = 1,
        yaxis.tck = 1,
	add.grid = FALSE,
	xgrid.at = xat,
	ygrid.at = yat,
	type = 'p',
	cex = 0.75,
	pch = 19,
	col = 'black',
        col.line = 'grey',
	lwd = 2,
	lty = 1,
	axes.lwd = 2.25,
	key = list(text = list(lab = c(''))),
	legend = NULL,
        add.rectangle = FALSE,
	xleft.rectangle = NULL,
	ybottom.rectangle = NULL,
	xright.rectangle = NULL,
        ytop.rectangle = NULL,
	col.rectangle = 'transparent',
	alpha.rectangle = 1,
	top.padding = 3,
	bottom.padding = 0.7,
        left.padding = 0.5,
	right.padding = 0.1,
	height = 6,
	width = 6,
	size.units = 'in',
	resolution = 1600,
        enable.warnings = FALSE,
	description = 'Created with BoutrosLab.plotting.general',
        style = 'BoutrosLab',
	preload.default = 'custom',
	use.legacy.settings = FALSE,
	inside.legend.auto = FALSE
	);

Arguments

Value

If filename is NULL then returns the trellis object, otherwise creates a plot and returns a 0/1 success code.

Warning

Note that the confidence band only works for a single panel qq plot, not for grouped data and multi-qq plot. Why? What's missing?

Author(s)

Ying Wu

See Also

qqmath, lattice or the Lattice book for an overview of the package.

Examples

set.seed(12345);
create.qqplot.fit(
    # filename = tempfile(pattern = 'QQfit_Simple', fileext = '.tiff'),
    x = rnorm(300),
    # choosing to compare against a uniform distribution
    distribution = qunif,
    resolution = 100
    );

# Minimal Input
create.qqplot.fit(
    # filename = tempfile(pattern = 'QQfit_Minimal_Input', fileext = '.tiff'),
    x = microarray[1:500,1],
    # choosing to compare against a uniform distribution
    distribution = qunif,
    main = 'Minimal input',
    description = 'QQplot fit created by BoutrosLab.plotting.general',
    resolution = 100
    );

# Axes and Labels
create.qqplot.fit(
    # filename = tempfile(pattern = 'QQfit_Axes_Labels', fileext = '.tiff'),
    x = microarray[1:500,1],
    distribution = qunif,
    main = 'Axes & labels',
    # Adding axes labels
    xlab.label = 'qunif',
    ylab.label = 'sample values',
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    xaxis.cex = 1,
    yaxis.cex = 1,
    add.grid = TRUE,
    description = 'QQplot fit created by BoutrosLab.plotting.general',
    resolution = 100
    );

# Confidence bands 
create.qqplot.fit(
    # filename = tempfile(pattern = 'QQfit_Confidence_Bands', fileext = '.tiff'),
    x = microarray[1:500,1],
    distribution = qunif,
    main = 'Confidence bands',
    xlab.label = 'qunif',
    ylab.label = 'sample values',
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    xaxis.cex = 1,
    yaxis.cex = 1,
    add.grid = TRUE,
    # Adding confidence bands (auto-generates legend)
    confidence.bands = TRUE,
    confidence.method = 'both',
    description = 'QQplot fit created by BoutrosLab.plotting.general',
    resolution = 100
    );


# Multiple qq plot conditioned on a variable
# 'Formula' format of data
chr.locations <- microarray$Chr[1:500];
chr.locations <- replace(chr.locations, which(chr.locations == 1), 'Chromosome 1');
chr.locations <- replace(chr.locations, which(chr.locations == 2), 'Chromosome 2');

qqplot.data <- data.frame(
    value = microarray[1:500,1],
    chr = chr.locations
    );

create.qqplot.fit(
    # filename = tempfile(pattern = 'QQfit_Multiple', fileext = '.tiff'),
    x = ~ value | chr,
    data = qqplot.data,
    distribution = qunif,
    main = 'Multiple plots',
    xlab.label = 'qunif',
    ylab.label = 'sample values',
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    xaxis.cex = 1,
    yaxis.cex = 1,
    add.grid = TRUE,
    confidence.bands = TRUE,
    confidence.method = 'simultaneous',
    description = 'QQplot fit created by BoutrosLab.plotting.general',
    resolution = 100
    );

# Grouped qq plot
create.qqplot.fit(
    # filename = tempfile(pattern = 'QQfit_Grouped', fileext = '.tiff'),
    x = ~ value,
    data = qqplot.data,
    # Adding groups
    groups = qqplot.data$chr,
    # Colouring groups
    col = default.colours(2),
    # Setting different plotting characters
    pch = c(15, 19),
    distribution = qunif,
    main = 'Grouped & legend',
    xlab.label = 'qunif',
    ylab.label = 'sample values',
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    xaxis.cex = 1,
    yaxis.cex = 1,
    add.grid = TRUE,
    confidence.bands = TRUE,
    confidence.method = 'simultaneous',
    # Adding legend for groups
    key = list(
        text = list(
            lab = c('1','2'),
            cex = 1,
            col = 'black'
            ),
        points = list(
            pch = c(15, 19),
            col = default.colours(2),
            cex = 1
            ),
        x = 0.04,
        y = 0.95,
        padding.text = 2
        ),
    description = 'QQplot fit created by BoutrosLab.plotting.general',
    resolution = 100
    );

# Correlation Key
create.qqplot.fit(
    # filename = tempfile(pattern = 'QQfit_Correlation_Key', fileext = '.tiff'),
    x = ~ value,
    data = qqplot.data,
    groups = qqplot.data$chr,
    col = default.colours(2),
    pch = c(15, 19),
    distribution = qunif,
    main = 'Correlation key',
    xlab.label = 'qunif',
    ylab.label = 'sample values',
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    xaxis.cex = 1,
    yaxis.cex = 1,
    add.grid = TRUE,
    confidence.bands = TRUE,
    confidence.method = 'simultaneous',
    # Adjusting legend to contain multiple keys
    legend = list(
        inside = list(
            fun = draw.key,
            args = list(
                key = list(
                        text = list(
                            lab = c('1','2'),
                            cex = 1,
                            col = 'black'
                            ),
                        points = list(
                            pch = c(15, 19),
                            col = default.colours(2),
                            cex = 1
                            ),
                        x = 0.14,
                        y = 0.80,
                        padding.text = 2
                    )
                )
            ),
        inside = list(
            fun = draw.key,
            args = list(
                key = get.corr.key(
                    x = runif(500),
                    y = qqplot.data$value,
                    label.items = c('spearman', 'kendall','beta1'),
                    alpha.background = 0,
                    key.cex = 1
                    )
                ),
            x = 0.75,
            y = 0.20,
            corner = c(0,1)
            )
        ),
    description = 'QQplot fit created by BoutrosLab.plotting.general',
    resolution = 100
    );

# Nature style
create.qqplot.fit(
    # filename = tempfile(pattern = 'QQfit_Nature_style', fileext = '.tiff'),
    x = microarray[1:500,1],
    distribution = qunif,
    main = 'Nature style',
    xlab.cex = 1.5,
    ylab.cex = 1.5,
    xaxis.fontface = 1,
    yaxis.fontface = 1,
    xaxis.cex = 1,
    yaxis.cex = 1,
    add.grid = TRUE,
    confidence.bands = TRUE,
    confidence.method = 'both',

    # 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 = 'QQplot fit created by BoutrosLab.plotting.general',
    resolution = 1200
    );

Create the confidence bands for a one-sample qq plot

Description

Returns the values of constructing the confidence bands for a one-sample qq plot

Usage

create.qqplot.fit.confidence.interval(x, distribution = qnorm, conf = 0.95,
    conf.method = "both", reference.line.method = "quartiles");

Arguments

Value

Returns the values of creating the upper and lower bands for the qq plot.

Warning

Note that this function works only for a single panel qq plot, not for grouped data and multi-qq plot.

Author(s)

Ying Wu

Examples

tmp.x <- rnorm(100);

tmp.confidence.interval <- create.qqplot.fit.confidence.interval(tmp.x);

qqnorm(tmp.x);
qqline(tmp.x);
lines(tmp.confidence.interval$z, tmp.confidence.interval$upper.pw, lty = 2, col = "brown");
lines(tmp.confidence.interval$z, tmp.confidence.interval$lower.pw, lty = 2, col = "brown");
lines(tmp.confidence.interval$z[tmp.confidence.interval$u], 
    tmp.confidence.interval$upper.sim, lty = 2, col = "blue");
lines(tmp.confidence.interval$z[tmp.confidence.interval$l], 
    tmp.confidence.interval$lower.sim, lty = 2, col = "blue");

legend(1, -1.5, c("simultaneous", "pointwise"), col = c("blue", "brown"), lty = 2, bty = "n");

Make a scatterplot

Description

Takes a data.frame and creates a scatterplot

Usage

create.scatterplot(
	formula,
	data,
	filename = NULL,
	groups = NULL,
	main = NULL,
	main.just = 'center',
	main.x = 0.5,
        main.y = 0.5,
	main.cex = 3,
	xlab.label = tail(sub('~', '', formula[-2]), 1),
	ylab.label = tail(sub('~', '', formula[-3]), 1),
        xlab.cex = 2,
	ylab.cex = 2,
	xlab.col = 'black',
	ylab.col = 'black',
        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,
	xlimits = NULL,
	ylimits = NULL,
	xat = TRUE,
	yat = TRUE,
	xaxis.lab = NA,
	yaxis.lab = NA,
	xaxis.log = FALSE,
	yaxis.log = FALSE,
        xaxis.cex = 1.5,
	yaxis.cex = 1.5,
	xaxis.rot = 0,
	yaxis.rot = 0,
	xaxis.fontface = 'bold',
	yaxis.fontface = 'bold',
	xaxis.col = 'black',
	yaxis.col = 'black',
	xaxis.tck = c(1,1),
	yaxis.tck = c(1,1),
	add.grid = FALSE,
	xgrid.at = xat,
        ygrid.at = yat,
	grid.colour = NULL,
	horizontal = FALSE,
	type = 'p',
	cex = 0.75,
	pch = 19,
	col = 'black',
	col.border = 'black',
	lwd = 1,
	lty = 1,
	alpha = 1,
	axes.lwd = 1,
	strip.col = 'white',
	strip.cex = 1,
	strip.fontface = 'bold',
	y.error.up = NULL,
	y.error.down = y.error.up,
	x.error.right = NULL,
	x.error.left = x.error.right,
	y.error.bar.col = 'black',
	x.error.bar.col = y.error.bar.col,
	error.whisker.angle = 90,
	error.bar.lwd = 1,
	error.bar.length = 0.1,
	key = list(text = list(lab = c(''))),
	legend = NULL,
	top.padding = 0.1,
	bottom.padding = 0.7,
	right.padding = 0.1,
	left.padding = 0.5,
	key.top = 0.1,
	key.left.padding = 0,
	ylab.axis.padding = 1,
	axis.key.padding = 1,
	layout = NULL,
	as.table = FALSE,
	x.spacing = 0,
	y.spacing = 0,
	x.relation = 'same',
	y.relation = 'same',
	add.axes = FALSE,
	axes.lty = 'dashed',
	add.xyline = FALSE,
	xyline.col = 'black',
	xyline.lwd = 1,
	xyline.lty = 1,
	abline.h = NULL,
	abline.v = NULL,
	abline.col = 'black',
	abline.lwd = 1,
	abline.lty = 1,
	add.curves = FALSE,
	curves.exprs = NULL,
	curves.from = min(data, na.rm = TRUE),
	curves.to = max(data, na.rm = TRUE),
	curves.col = 'black',
	curves.lwd = 2,
	curves.lty = 1,
	add.rectangle = FALSE,
	xleft.rectangle = NULL,
	ybottom.rectangle = NULL,
	xright.rectangle = NULL,
	ytop.rectangle = NULL,
	col.rectangle = 'transparent',
	alpha.rectangle = 1,
	add.points = FALSE,
	points.x = NULL,
	points.y = NULL,
	points.pch = 19,
	points.col = 'black',
	points.col.border = 'black',
	points.cex = 1,
	add.line.segments = FALSE,
	line.start = NULL,
	line.end = NULL,
	line.col = 'black',
	line.lwd = 1,
	add.text = FALSE,
	text.labels = NULL,
	text.x = NULL,
	text.y = NULL,
	text.col = 'black',
	text.cex = 1,
	text.fontface = 'bold',
	text.guess.labels = FALSE,
	text.guess.skip.labels = TRUE,
	text.guess.ignore.radius = FALSE,
	text.guess.ignore.rectangle = FALSE,
	text.guess.radius.factor = 1,
	text.guess.buffer.factor = 1,
	text.guess.label.position = NULL,
	height = 6,
	width = 6,
	size.units = 'in',
	resolution = 1600,
	enable.warnings = FALSE,
	description = 'Created with BoutrosLab.plotting.general',
	style = 'BoutrosLab',
	preload.default = 'custom',
	group.specific.colouring = TRUE,
	use.legacy.settings = FALSE,
	inside.legend.auto = FALSE,
	regions.labels = c(),
        regions.start = c(),
	regions.stop = c(),
	regions.color = c("red"),
	regions.cex = 1,
	regions.alpha = 1,
        lollipop.bar.y = NULL,
	lollipop.bar.color = "gray",
	...
	);

Arguments