Package 'CancerEvolutionVisualization' reference manual

Package 'CancerEvolutionVisualization'

Title:	Publication Quality Phylogenetic Tree Plots
Description:	Generates tree plots with precise branch lengths, gene annotations, and cellular prevalence. The package handles complex tree structures (angles, lengths, etc.) and can be further refined as needed by the user.
Authors:	Paul Boutros [cre], Adriana Salcedo [aut], Dan Knight [aut], Helena Winata [aut], Selina Wu [ctb]
Maintainer:	Paul Boutros <[email protected]>
License:	GPL-2
Version:	3.0.0
Built:	2025-01-07 18:30:19 UTC
Source:	https://github.com/uclahs-cds/package-cancerevolutionvisualization

Title:

Publication Quality Phylogenetic Tree Plots

Description:

Generates tree plots with precise branch lengths, gene annotations, and cellular prevalence. The package handles complex tree structures (angles, lengths, etc.) and can be further refined as needed by the user.

Authors:

Paul Boutros [cre], Adriana Salcedo [aut], Dan Knight [aut], Helena Winata [aut], Selina Wu [ctb]

Maintainer:

Paul Boutros <[email protected]>

License:

GPL-2

Version:

3.0.0

Built:

2025-01-07 18:30:19 UTC

Source:

https://github.com/uclahs-cds/package-cancerevolutionvisualization

Help Index

Colour scheme vector

Description

Default colours used by CP polygons, etc.

Format

Character vector

CCF Distribution Heatmap

Description

Creates a heatmap of cancer cell fraction (CCF) distribution across tumour samples. The function is a wrapper around BoutrosLab.plotting.general::create.heatmap() with some changes in the default parameters. All parameter description are the same as in BoutrosLab.plotting.general::create.heatmap() except for ccf.thres.

Usage

create.ccf.heatmap(
    x,
    cluster.dimensions = 'both',
    clustering.method = 'complete',
    distance.method = 'euclidean',
    xaxis.lab = '',
    xlab.label = 'SNVs',
    print.colour.key = FALSE,
    colour.scheme = c('white', 'blue'),
    ...
    )
create.ccf.heatmap(
    x,
    cluster.dimensions = 'both',
    clustering.method = 'complete',
    distance.method = 'euclidean',
    xaxis.lab = '',
    xlab.label = 'SNVs',
    print.colour.key = FALSE,
    colour.scheme = c('white', 'blue'),
    ...
    )

Arguments

`x`	Either a data-frame or a matrix from which the heatmap is to created
`cluster.dimensions`	Defaults to “both”.
`clustering.method`	Defaults to “complete”.
`distance.method`	Defaults to “euclidean”.
`xaxis.lab`	Defaults to an empty string.
`xlab.label`	Defaults to “SNVs”.
`print.colour.key`	Defaults to `FALSE`.
`colour.scheme`	Defaults to `c('white', 'blue')`.
`...`	Pass through argument. See BoutrosLab.plotting.general::create.heatmap() for further details.

Value

A 'grob' object of the heatmap.

Author(s)

Helena Winata

Examples

    set.seed(123);
    arr <- matrix(rnorm(500), nrow = 100, ncol = 5);
    rownames(arr) <- paste0('SNV.', 1:100);
    colnames(arr) <- paste0('Sample.', 1:5);
    create.ccf.heatmap(arr);
set.seed(123);
    arr <- matrix(rnorm(500), nrow = 100, ncol = 5);
    rownames(arr) <- paste0('SNV.', 1:100);
    colnames(arr) <- paste0('Sample.', 1:5);
    create.ccf.heatmap(arr);

CCF Summary Heatmap

Description

Creates a heatmap of cancer cell fraction (CCF) distribution across tumour samples with clone IDs as a covariate beneath the heatmap. Subplot parameters controls the appearance of the heatmap and barplots. See BoutrosLab.plotting.general::create.barplot() or BoutrosLab.plotting.general::create.heatmap() for parameter description. Legend parameters are passed to BoutrosLab.plotting.general::legend.grob().

Usage

create.ccf.summary.heatmap(
    DF,
    ccf.limits = NULL,
    median.col = 'median.ccf.per.sample',
    clone.order = NULL,
    sample.order = NULL,
    colour.scheme = c('white', 'blue'),
    clone.colours = NULL,
    subplot.xlab.cex = 1.2,
    subplot.xaxis.cex = 1,
    subplot.xaxis.fontface = 'bold',
    subplot.xaxis.rot = 90,
    subplot.ylab.cex = 1.2,
    subplot.yaxis.cex = 1,
    subplot.yaxis.fontface = 'bold',
    hm.xaxis.rot = 90,
    legend.size = 3,
    legend.title.cex = 1.2,
    legend.label.cex = 1,
    legend.x = 0.9,
    legend.y = 0.8,
    plot.objects.heights = c(0.3, 1),
    add.median.text = FALSE,
    ...
    );
create.ccf.summary.heatmap(
    DF,
    ccf.limits = NULL,
    median.col = 'median.ccf.per.sample',
    clone.order = NULL,
    sample.order = NULL,
    colour.scheme = c('white', 'blue'),
    clone.colours = NULL,
    subplot.xlab.cex = 1.2,
    subplot.xaxis.cex = 1,
    subplot.xaxis.fontface = 'bold',
    subplot.xaxis.rot = 90,
    subplot.ylab.cex = 1.2,
    subplot.yaxis.cex = 1,
    subplot.yaxis.fontface = 'bold',
    hm.xaxis.rot = 90,
    legend.size = 3,
    legend.title.cex = 1.2,
    legend.label.cex = 1,
    legend.x = 0.9,
    legend.y = 0.8,
    plot.objects.heights = c(0.3, 1),
    add.median.text = FALSE,
    ...
    );

Arguments

`DF`	A data-frame with the following column names: 'ID', 'SNV.id', 'clone.id', 'CCF'.
`ccf.limits`	CCF limits to be applied to the heatmap. Must be a vector of length 2 for min and max thresholds. Defaults to `NULL`
`median.col`	Defaults to “median.ccf.per.sample”
`clone.order`	Defaults to `NULL`
`sample.order`	Defaults to `NULL`
`colour.scheme`	Heatmap colour scheme. Defaults to `c('white', 'blue')`
`clone.colours`	A named vector specifying the color to use for each clone to generate a covariate heatmap. If `NULL`, no covariates will be added.
`subplot.xlab.cex`	Subplot parameter. Defaults to 1.2
`subplot.xaxis.cex`	Subplot parameter. Defaults to 1
`subplot.xaxis.fontface`	Subplot parameter. Defaults to “bold”
`subplot.xaxis.rot`	Subplot parameter. Defaults to 90
`subplot.ylab.cex`	Subplot parameter. Defaults to 1.2
`subplot.yaxis.cex`	Subplot parameter. Defaults to 1
`subplot.yaxis.fontface`	Subplot parameter. Defaults to “bold”
`hm.xaxis.rot`	Subplot parameter. Defaults to 90
`legend.size`	Legend parameter. Defaults to 3
`legend.title.cex`	Legend parameter. Defaults to 1.2
`legend.label.cex`	Legend parameter. Defaults to 1
`legend.x`	Legend parameter. Defaults to 0.9
`legend.y`	Legend parameter. Defaults to 0.8
`plot.objects.heights`	If `clone.colours` is specified, this must be an array of length 3. Defaults to `c(0.3, 1)`. See BoutrosLab.plotting.general::create.multipanelplot()
`add.median.text`	If `TRUE`, median CCF values will be added to the heatmap. Defaults to `FALSE`
`...`	Pass through argument. See BoutrosLab.plotting.general::create.multipanelplot() for further details.

Value

A 'grob' object of the summary plot.

Author(s)

Helena Winata

Examples

set.seed(123);
snv <- do.call(rbind, lapply(split(snv, list(snv$ID, snv$clone.id)), function(df) {
    df$median.ccf.per.sample <- median(df$CCF)
    df$total.nsnv <- length(unique(df$SNV.id))
    df
    }));

create.ccf.summary.heatmap(snv);
set.seed(123);
snv <- do.call(rbind, lapply(split(snv, list(snv$ID, snv$clone.id)), function(df) {
    df$median.ccf.per.sample <- median(df$CCF)
    df$total.nsnv <- length(unique(df$SNV.id))
    df
    }));

create.ccf.summary.heatmap(snv);

Create Clone Genome Distribution Plot

Description

This function creates a plot showing the distribution of clones across the genome. It generates a scatter plot of the SNVs colored by clone ID and a density plot showing the density of each clone across the genome. The function can handle both single and multi-sample inputs.

Usage

create.clone.genome.distribution.plot(
    snv.df,
    genome.build = 'GRCh37',
    clone.order = NULL,
    clone.colours = NULL,
    filename = NULL,
    multi.sample = FALSE,
    alpha = 0.25,
    legend.x = 0.1,
    legend.y = 0.55,
    ...
    )
create.clone.genome.distribution.plot(
    snv.df,
    genome.build = 'GRCh37',
    clone.order = NULL,
    clone.colours = NULL,
    filename = NULL,
    multi.sample = FALSE,
    alpha = 0.25,
    legend.x = 0.1,
    legend.y = 0.55,
    ...
    )

Arguments

`snv.df`	A data frame containing the SNV data. It must have columns 'chr', 'pos', and 'clone.id'. If `multi.sample = TRUE`, it must also have a column 'ID' specifying the sample ID for each SNV.
`genome.build`	The genome build to use. Defaults to “GRCh37”.
`clone.order`	The order in which to plot the clones. If `NULL`, clones will be sorted alphabetically.
`clone.colours`	A named vector specifying the color to use for each clone. If `NULL`, colors will be automatically assigned.
`filename`	Directory or filepath to save the plot in. If `multi.sample = TRUE`, this must be a directory. if `multi.sample = FALSE`, this must be a filepath. If `NULL`, the plot will not be saved.
`multi.sample`	Logical indicating whether the input data contains multiple samples. Defaults to `FALSE`.
`alpha`	Transparency level of the points in the scatter plot. Defaults to 0.25.
`legend.x`	Legend parameter. Defaults to 0.1
`legend.y`	Legend parameter. Defaults to 0.55
`...`	Additional arguments to be passed to `BoutrosLab.plotting.general::create.multipanelplot()`.

Details

This function preprocesses the input data frame, extracts chromosome information, and iterates over each sample to create a clone genome distribution plot. For each sample, it calculates the density of each clone across the genome and creates a scatter plot of the SNVs colored by clone ID and a density plot showing the density of each clone.

Value

A 'grob' object.

Author(s)

Helena Winata, Selina Wu

Examples

set.seed(123);
create.clone.genome.distribution.plot(snv);
set.seed(123);
create.clone.genome.distribution.plot(snv);

Cluster Heatmap

Description

Creates a heatmap of cancer cell fraction (CCF) distribution across tumour samples with clone IDs as a covariate beneath the heatmap.

Usage

create.cluster.heatmap(
    DF,
    ccf.limits = NULL,
    clone.colours = NULL,
    height = 6,
    width = 11,
    xaxis.col = NULL,
    legend.size = 3,
    legend.title.cex = 1.2,
    legend.label.cex = 1,
    filename = NULL,
    xlab.label = 'SNVs',
    xlab.cex = 1.2,
    xaxis.cex = 1,
    xaxis.fontface = 'bold',
    y.spacing = 1,
    colour.scheme = c('white', 'blue'),
    ...
    );
create.cluster.heatmap(
    DF,
    ccf.limits = NULL,
    clone.colours = NULL,
    height = 6,
    width = 11,
    xaxis.col = NULL,
    legend.size = 3,
    legend.title.cex = 1.2,
    legend.label.cex = 1,
    filename = NULL,
    xlab.label = 'SNVs',
    xlab.cex = 1.2,
    xaxis.cex = 1,
    xaxis.fontface = 'bold',
    y.spacing = 1,
    colour.scheme = c('white', 'blue'),
    ...
    );

Arguments

`DF`	A data-frame with the following column names: 'ID', 'SNV.id', 'clone.id', 'CCF'.
`ccf.limits`	CCF limits to be applied to the heatmap. Must be a vector of length 2 for min and max thresholds. Defaults to `NULL`
`clone.colours`	Named list to provide a colour scheme for the clone ID covariate bar. If NULL, colours will be randomly generated. Defaults to `NULL`.
`height`	Defaults to 6
`width`	Defaults to 11
`xaxis.col`	Column in DF to extract x-axis labels from. Defaults to `NULL`.
`legend.size`	Width of the legend boxes in 'character' units. Defaults to 3
`legend.title.cex`	Size of titles in the legends. Defaults to 1.2
`legend.label.cex`	Size of text labels in the legends. Defaults to 1
`filename`	Filename for tiff output, or if NULL returns the trellis object itself. Defaults to `NULL`.
`xlab.label`	Defaults to “SNVs”.
`xlab.cex`	Defaults to 1.2
`xaxis.cex`	Defaults to 1
`xaxis.fontface`	Defaults to “bold”.
`y.spacing`	Spacing between heatmap and clone covariate bar. Defaults to 1
`colour.scheme`	Colour scheme for the heatmap. Defaults to `c('white', 'blue')`.
`...`	Pass through argument. See BoutrosLab.plotting.general::create.heatmap() for further details.

Value

A 'grob' object of the heatmap.

Author(s)

Helena Winata

Examples

set.seed(123);

create.cluster.heatmap(
    DF = snv,
    ccf.limits = c(0, 1),
    ylab.label = 'Sample'
    );
set.seed(123);

create.cluster.heatmap(
    DF = snv,
    ccf.limits = c(0, 1),
    ylab.label = 'Sample'
    );

Phylogenetic Tree Plot

Description

Create a phylogenetic tree plot using 'SRCGrob'. The 'width', 'heigth', 'units', 'res', and 'bg' arguments are passed to the 'grDevices' function to save the plot to file.

Usage

create.phylogenetic.tree(
    tree,
    filename = NULL,
    width = 5,
    height = 5,
    units = 'in',
    res = 500,
    bg = 'white',
    ...
    )
create.phylogenetic.tree(
    tree,
    filename = NULL,
    width = 5,
    height = 5,
    units = 'in',
    res = 500,
    bg = 'white',
    ...
    )

Arguments

`tree`	Tree structure data.frame
`filename`	Filename for plot output, or if `NULL` displays the tree using `grid.draw`. Defaults to `NULL`.
`width`	Width of the plot. Defaults to 5.
`height`	Height of the plot. Defaults to 5.
`units`	Units of the plot. Defaults to “in”.
`res`	Resolution of the plot. Defaults to 500.
`bg`	Background colour of the plot. Defaults to “white”.
`...`	Pass through argument. See SRCGrob() for further details.

Value

A 'grob' of class "SRCGrob".

Author(s)

Helena Winata

Examples

# Simple Tree Plot
simple.tree <- data.frame(
    parent = c(NA, 1, 1)
    );
create.phylogenetic.tree(simple.tree);

# Non-numeric node.id
phy.dt <- data.frame(
    node.id = c('MRCA', 'C01', 'C02', 'C03', 5:7),
    parent = c(NA, 'MRCA', 'MRCA', 'MRCA', 'C02', 'C03', 'C03')
    );
create.phylogenetic.tree(phy.dt);

# Numeric disordered node.id with labels
phy.dt <- data.frame(
    node.id = c(1, 2, 5, 6, 4, 9, 10),
    parent = c(NA, 1, 1, 1, 5, 6, 6),
    label = c('MRCA', 'C01', 'C02', 'C03', 5:7)
    );
create.phylogenetic.tree(phy.dt);
# Simple Tree Plot
simple.tree <- data.frame(
    parent = c(NA, 1, 1)
    );
create.phylogenetic.tree(simple.tree);

# Non-numeric node.id
phy.dt <- data.frame(
    node.id = c('MRCA', 'C01', 'C02', 'C03', 5:7),
    parent = c(NA, 'MRCA', 'MRCA', 'MRCA', 'C02', 'C03', 'C03')
    );
create.phylogenetic.tree(phy.dt);

# Numeric disordered node.id with labels
phy.dt <- data.frame(
    node.id = c(1, 2, 5, 6, 4, 9, 10),
    parent = c(NA, 1, 1, 1, 5, 6, 6),
    label = c('MRCA', 'C01', 'C02', 'C03', 5:7)
    );
create.phylogenetic.tree(phy.dt);

Convert Data Frame to Array

Description

Converts a long-format data frame into a 2D matrix/array where: - Rows correspond to unique values from the x.axis column - Columns correspond to unique values from the y.axis column - Cell values are taken from the value column - NA values are replaced with 0

Usage

data.frame.to.array(
    DF,
    value = 'CCF',
    x.axis = 'SNV.id',
    y.axis = 'ID'
    );
data.frame.to.array(
    DF,
    value = 'CCF',
    x.axis = 'SNV.id',
    y.axis = 'ID'
    );

Arguments

`DF`	A data frame in long format containing the data to be reshaped.
`value`	The name of the column containing the values to be reshaped. Defaults to `'CCF'`.
`x.axis`	The name of the column to be used as row names in the output matrix. Defaults to `'SNV.id'`.
`y.axis`	The name of the column to be used as column names in the output matrix. Defaults to `'ID'`.

Value

A 2D 'matrix'.

Author(s)

Helena Winata

Examples

df <- data.frame(
    SNV.id = c(rep(paste0('snv', 1:3), 3)),
    ID = c(rep('S1', 3), rep('S2', 3), rep('S3', 3)),
    CCF = runif(9)
    );

arr <- data.frame.to.array(df);
df <- data.frame(
    SNV.id = c(rep(paste0('snv', 1:3), 3)),
    ID = c(rep('S1', 3), rep('S2', 3), rep('S3', 3)),
    CCF = runif(9)
    );

arr <- data.frame.to.array(df);

GRCh37 Chromosom Information

Description

Chromosome information for the GRCh37 genome build. Used for plotting.

Format

data.frame

GRCh38 Chromosom Information

Description

Chromosome information for the GRCh38 genome build. Used for plotting.

Format

data.frame

SNV dataframe

Description

Dataframe containing the following columns: 'ID', 'SNV.id', 'CCF', 'clone.id', 'chr', 'pos'. Used to generate plot examples.

Format

data.frame

Subclone Tree Plot

Description

Creates a phylogenetic tree using subclonal reconstruction data. An optional node text data.frame can be used to annotate notable genes alonside branches.

Usage

SRCGrob(
    tree,
    node.text = NULL,
    main = NULL,
    horizontal.padding = 0.1,
    scale1 = 1,
    scale2 = 1,
    yat = NULL,
    yaxis1.label = NULL,
    yaxis2.label = NULL,
    xlab.cex = 1.55,
    ylab.cex = 1.55,
    xaxis.cex = 1.45,
    yaxis.cex = 1.45,
    xaxis.label = 'CCF',
    label.cex = NA,
    node.text.cex = 0.85,
    main.y = NULL,
    main.cex = 1.7,
    node.text.line.dist = 0.1,
    polygon.colour.scheme = 'grey',
    polygon.scale = 1,
    add.normal = FALSE,
    use.radians = FALSE,
    normal.cex = 1,
    label.nodes = TRUE,
    disable.polygons = FALSE,
    length.from.node.edge = TRUE,
    size.units = 'npc',
    scale.bar = FALSE,
    scale.bar.coords = c(0.5, 1),
    scale.size.1 = NA,
    scale.size.2 = NA,
    scale.padding = 1
    );
SRCGrob(
    tree,
    node.text = NULL,
    main = NULL,
    horizontal.padding = 0.1,
    scale1 = 1,
    scale2 = 1,
    yat = NULL,
    yaxis1.label = NULL,
    yaxis2.label = NULL,
    xlab.cex = 1.55,
    ylab.cex = 1.55,
    xaxis.cex = 1.45,
    yaxis.cex = 1.45,
    xaxis.label = 'CCF',
    label.cex = NA,
    node.text.cex = 0.85,
    main.y = NULL,
    main.cex = 1.7,
    node.text.line.dist = 0.1,
    polygon.colour.scheme = 'grey',
    polygon.scale = 1,
    add.normal = FALSE,
    use.radians = FALSE,
    normal.cex = 1,
    label.nodes = TRUE,
    disable.polygons = FALSE,
    length.from.node.edge = TRUE,
    size.units = 'npc',
    scale.bar = FALSE,
    scale.bar.coords = c(0.5, 1),
    scale.size.1 = NA,
    scale.size.2 = NA,
    scale.padding = 1
    );

Arguments

`tree`	Tree structure data.frame
`node.text`	Dataframe for text labels to be displayed next to nodes
`main`	Main plot title
`horizontal.padding`	Increase/reduce the plot's horizontal padding proportionally. A positive value will expand the padding, and a negative value will reduce it.
`scale1`	Proportionally scale the values of the first branch length column in the tree input.
`scale2`	Proportionally scale the values of the second branch length column in the tree input.
`yat`	Specific values to be used for the y-axis ticks. A list is required, with each element corresponding to an axis.
`yaxis1.label`	Text label for the first, leftmost y-axis
`yaxis2.label`	Text label for the second, rightmost y-axis
`xlab.cex`	Font size for the x-axis label
`ylab.cex`	Font size for the y-axis labels
`xaxis.cex`	Font size for the x-axis tick labels
`yaxis.cex`	Font size for the y-axis tick labels
`xaxis.label`	Text label for the x-axis
`label.cex`	Font size for the node labels
`node.text.cex`	Font size for the node text
`main.y`	Move the main plot title position up or down
`main.cex`	Font size for the main plot title
`node.text.line.dist`	Distance between node text and tree branches (as a value between 0 and 1)
`add.normal`	Adds a normal
`use.radians`	Unit to be used for 'angle' column (degrees or radians)
`normal.cex`	Font size within the normal 'box'
`label.nodes`	Enable/disable node labels
`disable.polygons`	Disables CCF polygon drawing (even when CCF values are provided)
`polygon.colour.scheme`	Vector of colour values to be used for CCF polygons. Creates a gradient color scheme.
`polygon.scale`	Multiplier to scale CCF polygons (must be positive).
`length.from.node.edge`	Sets the branch length to be calculated from the edge of the node instead of the centre
`size.units`	Grid units to be used for all specific size/length parameters
`scale.bar`	Set to `TRUE` to add a scale bar instead of y-axis
`scale.bar.coords`	Coordinates for the scale bar placement `c(x.pos, y.pos)`
`scale.size.1`	Overide size of the first scale bar
`scale.size.2`	Overide size of the second scale bar
`scale.padding`	Padding between scale bars if more than 1 is present

Value

A ‘grob' of class ’SRCGrob'

Author(s)

Dan Knight

Examples

# Simple Tree Plot
simple.tree <- data.frame(
    parent = c(NA, 1, 1)
    );

SRCGrob(simple.tree);


# Specify Branch Lengths
branch.lengths.tree <- data.frame(
    simple.tree,
    length1 = c(10, 20, 15)
    );

SRCGrob(branch.lengths.tree);


# Cellular Prevalence
CCF.tree <- data.frame(
    branch.lengths.tree,
    CCF = c(1, 0.3, 0.5)
    );

SRCGrob(CCF.tree);


# Scaled Branch Lengths
SRCGrob(
    branch.lengths.tree,
    scale1 = 0.8
    );


# Override Branch Angles in Degrees
degrees.tree <- data.frame(
    parent = c(NA, 1, 2),
    angle = c(NA, NA, 90)
    );

SRCGrob(degrees.tree);


# Override Branch Angles in Radians
radians.tree <- data.frame(
    parent = c(NA, 1, 2),
    angle = c(NA, NA, pi / 2)
    );

SRCGrob(
    radians.tree,
    use.radians = TRUE
    );


# Plot Title
SRCGrob(
    simple.tree,
    main = 'Example Plot'
    );


# Y-Axis Label
SRCGrob(
    branch.lengths.tree,
    yaxis1.label = 'SNVs'
    );


# Y-Axis Ticks
yaxis1.ticks <- seq(0, 10, 2);

SRCGrob(
    branch.lengths.tree,
    yat = list(yaxis1.ticks)
    );


# Scale Bar
SRCGrob(
    branch.lengths.tree,
    yaxis1.label = 'SNVs',
    scale.bar = TRUE
    );


# Normal
SRCGrob(
    simple.tree,
    add.normal = TRUE
    );


# Nodeless Mode
nodeless.tree <- data.frame(
    parent = c(NA, 1, 2, 2),
    draw.node = c(TRUE, FALSE, TRUE, TRUE)
    );

SRCGrob(nodeless.tree);
# Simple Tree Plot
simple.tree <- data.frame(
    parent = c(NA, 1, 1)
    );

SRCGrob(simple.tree);


# Specify Branch Lengths
branch.lengths.tree <- data.frame(
    simple.tree,
    length1 = c(10, 20, 15)
    );

SRCGrob(branch.lengths.tree);


# Cellular Prevalence
CCF.tree <- data.frame(
    branch.lengths.tree,
    CCF = c(1, 0.3, 0.5)
    );

SRCGrob(CCF.tree);


# Scaled Branch Lengths
SRCGrob(
    branch.lengths.tree,
    scale1 = 0.8
    );


# Override Branch Angles in Degrees
degrees.tree <- data.frame(
    parent = c(NA, 1, 2),
    angle = c(NA, NA, 90)
    );

SRCGrob(degrees.tree);


# Override Branch Angles in Radians
radians.tree <- data.frame(
    parent = c(NA, 1, 2),
    angle = c(NA, NA, pi / 2)
    );

SRCGrob(
    radians.tree,
    use.radians = TRUE
    );


# Plot Title
SRCGrob(
    simple.tree,
    main = 'Example Plot'
    );


# Y-Axis Label
SRCGrob(
    branch.lengths.tree,
    yaxis1.label = 'SNVs'
    );


# Y-Axis Ticks
yaxis1.ticks <- seq(0, 10, 2);

SRCGrob(
    branch.lengths.tree,
    yat = list(yaxis1.ticks)
    );


# Scale Bar
SRCGrob(
    branch.lengths.tree,
    yaxis1.label = 'SNVs',
    scale.bar = TRUE
    );


# Normal
SRCGrob(
    simple.tree,
    add.normal = TRUE
    );


# Nodeless Mode
nodeless.tree <- data.frame(
    parent = c(NA, 1, 2, 2),
    draw.node = c(TRUE, FALSE, TRUE, TRUE)
    );

SRCGrob(nodeless.tree);

Update Tree Descendant Property

Description

Function to iteratively update a specified property for all descendants of a given parent node(s) in a tree structure. The function works recursively, meaning it will update values for all descendants, not just immediate children. If a parent_id has no children, the original data frame is returned unchanged.

Usage

update.descendant.property(
    x,
    parent.id,
    property,
    value,
    overwrite = TRUE
    );
update.descendant.property(
    x,
    parent.id,
    property,
    value,
    overwrite = TRUE
    );

Arguments

`x`	A data frame representing a tree structure with at least the following columns: `parent` (parent node identifiers), and the column specified by `property`
`parent.id`	Character or numeric vector specifying the identifier(s) of the parent node(s)
`property`	Character string specifying the name of the column/property to be updated
`value`	The value to assign to the property column
`overwrite`	Logical; if `TRUE`, overwrites existing values; if `FALSE`, only fills in NA values. Defaults to `TRUE`

Value

A 'data.frame' with modified columns.

Author(s)

Helena Winata

Examples

tree <- data.frame(
    node.id = c('A', 'B', 'C', 'D', 'E', 'F'),
    parent = c(NA, 'A', 'B', 'A', 'C', 'E'),
    node.size = 1
    );

tree2 <- update.descendant.property(
    x = tree,
    parent.id = 'B',
    property = 'node.size',
    value = 2
    );
tree <- data.frame(
    node.id = c('A', 'B', 'C', 'D', 'E', 'F'),
    parent = c(NA, 'A', 'B', 'A', 'C', 'E'),
    node.size = 1
    );

tree2 <- update.descendant.property(
    x = tree,
    parent.id = 'B',
    property = 'node.size',
    value = 2
    );

Package 'CancerEvolutionVisualization'

Help Index

Colour scheme vector

Description

Format

CCF Distribution Heatmap

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

CCF Summary Heatmap

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Create Clone Genome Distribution Plot

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Cluster Heatmap

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Phylogenetic Tree Plot

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Convert Data Frame to Array

Description

Usage

Arguments

Value

Author(s)

Examples

GRCh37 Chromosom Information

Description

Format

GRCh38 Chromosom Information

Description

Format

SNV dataframe

Description

Format

Subclone Tree Plot

Description

Usage

Arguments

Value

Author(s)

Examples

Update Tree Descendant Property

Description

Usage

Arguments

Value

Author(s)

Examples