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Package ‘clustree’
February 24, 2019
Type Package
Title Visualise Clusterings at Different Resolutions
Version 0.3.0
Date 2019-02-24
Maintainer Luke Zappia 
Description Deciding what resolution to use can be a difficult question when
approaching a clustering analysis. One way to approach this problem is to
look at how samples move as the number of clusters increases. This package
allows you to produce clustering trees, a visualisation for interrogating
clusterings as resolution increases.
License GPL-3
Encoding UTF-8
LazyData true
URL https://github.com/lazappi/clustree
BugReports https://github.com/lazappi/clustree/issues
VignetteBuilder knitr
Depends R (>= 3.4), ggraph
Imports checkmate, igraph, dplyr, grid, ggplot2, viridis, methods,
rlang, tidygraph, ggrepel
Suggests testthat, knitr, rmarkdown, SingleCellExperiment, Seurat,
covr, SummarizedExperiment, pkgdown, spelling
RoxygenNote 6.1.1
Language en-GB
NeedsCompilation no
Author Luke Zappia [aut, cre] (),
Alicia Oshlack [aut] (),
Andrea Rau [ctb]
Repository CRAN
Date/Publication 2019-02-24 15:40:03 UTC
1

2

add_node_labels

R topics documented:
clustree-package . . . .
add_node_labels . . . .
add_node_points . . . .
aggr_metadata . . . . . .
assert_colour_node_aes .
assert_node_aes . . . . .
assert_numeric_node_aes
build_tree_graph . . . .
calc_sc3_stability . . . .
calc_sc3_stability_cluster
check_node_aes_list . .
clustree . . . . . . . . .
clustree_overlay . . . . .
get_tree_edges . . . . .
get_tree_nodes . . . . .
iris_clusts . . . . . . . .
overlay_node_points . .
plot_overlay_side . . . .
sc_example . . . . . . .
store_node_aes . . . . .

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Index

clustree-package

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2
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3
4
4
5
5
6
7
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8
9
12
15
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16
17
18
19
20

Clustree

Description
Deciding what resolution to use can be a difficult question when approaching a clustering analysis. One way to approach this problem is to look at how samples move as the number of clusters
increases. This package allows you to produce clustering trees, a visualisation for interrogating
clusterings as resolution increases.

add_node_labels

Add node labels

Description
Add node labels to a clustering tree plot with the specified aesthetics.
Usage
add_node_labels(node_label, node_colour, node_label_size,
node_label_colour, node_label_nudge, allowed)

add_node_points

3

Arguments
node_label

the name of a metadata column for node labels

node_colour

either a value indicating a colour to use for all nodes or the name of a metadata
column to colour nodes by

node_label_size
size of node label text
node_label_colour
colour of node_label text
node_label_nudge
numeric value giving nudge in y direction for node labels
allowed

add_node_points

vector of allowed node attributes to use as aesthetics

Add node points

Description
Add node points to a clustering tree plot with the specified aesthetics.

Usage
add_node_points(node_colour, node_size, node_alpha, allowed)

Arguments
node_colour

either a value indicating a colour to use for all nodes or the name of a metadata
column to colour nodes by

node_size

either a numeric value giving the size of all nodes or the name of a metadata
column to use for node sizes

node_alpha

either a numeric value giving the alpha of all nodes or the name of a metadata
column to use for node transparency

allowed

vector of allowed node attributes to use as aesthetics

4

assert_colour_node_aes

aggr_metadata

Aggregate metadata

Description
Aggregate a metadata column to get a summarized value for a cluster node
Usage
aggr_metadata(node_data, col_name, col_aggr, metadata, is_cluster)
Arguments
node_data

data.frame containing information about a set of cluster nodes

col_name

the name of the metadata column to aggregate

col_aggr

string naming a function used to aggregate the column

metadata

data.frame providing metadata on samples

is_cluster

logical vector indicating which rows of metadata are in the node to be summarized

Value
data.frame with aggregated data

assert_colour_node_aes
Assert colour node aesthetics

Description
Raise error if an incorrect set of colour node parameters has been supplied.
Usage
assert_colour_node_aes(node_aes_name, prefix, metadata, node_aes,
node_aes_aggr, min, max)

assert_node_aes

5

Arguments
node_aes_name

name of the node aesthetic to check

prefix

string indicating columns containing clustering information

metadata

data.frame containing metadata on each sample that can be used as node aesthetics

node_aes

value of the node aesthetic to check

node_aes_aggr

aggregation function associated with the node aesthetic

min

minimum numeric value allowed

max

maximum numeric value allowed

assert_node_aes

Assert node aesthetics

Description
Raise error if an incorrect set of node parameters has been supplied.
Usage
assert_node_aes(node_aes_name, prefix, metadata, node_aes, node_aes_aggr)
Arguments
node_aes_name

name of the node aesthetic to check

prefix

string indicating columns containing clustering information

metadata

data.frame containing metadata on each sample that can be used as node aesthetics

node_aes

value of the node aesthetic to check

node_aes_aggr

aggregation function associated with the node aesthetic

assert_numeric_node_aes
Assert numeric node aesthetics

Description
Raise error if an incorrect set of numeric node parameters has been supplied.
Usage
assert_numeric_node_aes(node_aes_name, prefix, metadata, node_aes,
node_aes_aggr, min, max)

6

build_tree_graph

Arguments
node_aes_name

name of the node aesthetic to check

prefix

string indicating columns containing clustering information

metadata

data.frame containing metadata on each sample that can be used as node aesthetics

node_aes

value of the node aesthetic to check

node_aes_aggr

aggregation function associated with the node aesthetic

min

minimum numeric value allowed

max

maximum numeric value allowed

build_tree_graph

Build tree graph

Description
Build a tree graph from a set of clusterings, metadata and associated aesthetics
Usage
build_tree_graph(clusterings, prefix, count_filter, prop_filter, metadata,
node_aes_list)
Arguments
clusterings

numeric matrix containing clustering information, each column contains clustering at a separate resolution

prefix

string indicating columns containing clustering information

count_filter

count threshold for filtering edges in the clustering graph

prop_filter

in proportion threshold for filtering edges in the clustering graph

metadata

data.frame containing metadata on each sample that can be used as node aesthetics

node_aes_list

nested list containing node aesthetics

Value
tidygraph::tbl_graph object containing the tree graph

calc_sc3_stability

7

calc_sc3_stability

Calculate SC3 stability

Description
Calculate the SC3 stability index for every cluster at every resolution in a set of clusterings. The
index varies from 0 to 1, where 1 suggests that a cluster is more stable across resolutions. See
calc_sc3_stability_cluster() for more details.
Usage
calc_sc3_stability(clusterings)
Arguments
clusterings

numeric matrix containing clustering information, each column contains clustering at a separate resolution

Value
matrix with stability score for each cluster

calc_sc3_stability_cluster
Calculate single SC3 stability

Description
Calculate the SC3 stability index for a single cluster in a set of clusterings. The index varies from 0
to 1, where 1 suggests that a cluster is more stable across resolutions.
Usage
calc_sc3_stability_cluster(clusterings, res, cluster)
Arguments
clusterings

numeric matrix containing clustering information, each column contains clustering at a separate resolution

res

resolution of the cluster to calculate stability for

cluster

index of the cluster to calculate stability for

8

check_node_aes_list

Details
This index was originally introduced in the SC3 package for clustering single-cell RNA-seq data.
Clusters are awarded increased stability if they share the same samples as a cluster at another resolution and penalised at higher resolutions. We use a slightly different notation to describe the score
but the results are the same:
s(ck,i ) =

XX
1
size(ck,i ∩ cl,j )
size(L) + 1
size(cl,j ) ∗ size(Nl )2
l∈L j∈Nl

Where:
• c_{x, y} is cluster y at resolution x
• k is the resolution of the cluster we want to score
• i is the index of the cluster we want to score
• L is the set of all resolutions except k
• l is a resolution in L
• N_l is the set of clusters at resolution l that share samples with c_{k, i}
• j is a cluster in N_l
Value
SC3 stability index
See Also
The documentation for the calculate_stability function in the SC3 package

check_node_aes_list

Check node aes list

Description
Warn if node aesthetic names are incorrect
Usage
check_node_aes_list(node_aes_list)
Arguments
node_aes_list

List of node aesthetics

Value
Corrected node aesthetics list

clustree

9

clustree

Plot a clustering tree

Description
Creates a plot of a clustering tree showing the relationship between clusterings at different resolutions.
Usage
clustree(x, ...)
## S3 method for class 'matrix'
clustree(x, prefix, suffix = NULL, metadata = NULL,
count_filter = 0, prop_filter = 0.1, layout = c("tree",
"sugiyama"), use_core_edges = TRUE, highlight_core = FALSE,
node_colour = prefix, node_colour_aggr = NULL, node_size = "size",
node_size_aggr = NULL, node_size_range = c(4, 15), node_alpha = 1,
node_alpha_aggr = NULL, node_text_size = 3,
scale_node_text = FALSE, node_text_colour = "black",
node_label = NULL, node_label_aggr = NULL, node_label_size = 3,
node_label_nudge = -0.2, edge_width = 1.5, edge_arrow = TRUE,
edge_arrow_ends = c("last", "first", "both"), show_axis = FALSE,
return = c("plot", "graph", "layout"), ...)
## S3 method for class 'data.frame'
clustree(x, prefix, ...)
## S3 method for class 'SingleCellExperiment'
clustree(x, prefix, exprs = "counts", ...)
## S3 method for class 'seurat'
clustree(x, prefix = "res.", exprs = c("data",
"raw.data", "scale.data"), ...)
Arguments
x

object containing clustering data

...

extra parameters passed to other methods

prefix

string indicating columns containing clustering information

suffix

string at the end of column names containing clustering information

metadata

data.frame containing metadata on each sample that can be used as node aesthetics

count_filter

count threshold for filtering edges in the clustering graph

prop_filter

in proportion threshold for filtering edges in the clustering graph

10

clustree
layout

string specifying the "tree" or "sugiyama" layout, see igraph::layout_as_tree()
and igraph::layout_with_sugiyama() for details

use_core_edges logical, whether to only use core tree (edges with maximum in proportion for
a node) when creating the graph layout, all (unfiltered) edges will still be displayed
highlight_core logical, whether to increase the edge width of the core network to make it easier
to see
node_colour

either a value indicating a colour to use for all nodes or the name of a metadata
column to colour nodes by
node_colour_aggr
if node_colour is a column name than a string giving the name of a function to
aggregate that column for samples in each cluster
node_size

either a numeric value giving the size of all nodes or the name of a metadata
column to use for node sizes

node_size_aggr if node_size is a column name than a string giving the name of a function to
aggregate that column for samples in each cluster
node_size_range
numeric vector of length two giving the maximum and minimum point size for
plotting nodes
node_alpha

either a numeric value giving the alpha of all nodes or the name of a metadata
column to use for node transparency

node_alpha_aggr
if node_aggr is a column name than a string giving the name of a function to
aggregate that column for samples in each cluster
node_text_size numeric value giving the size of node text if scale_node_text is FALSE
scale_node_text
logical indicating whether to scale node text along with the node size
node_text_colour
colour value for node text (and label)
node_label
additional label to add to nodes
node_label_aggr
if node_label is a column name than a string giving the name of a function to
aggregate that column for samples in each cluster
node_label_size
numeric value giving the size of node label text
node_label_nudge
numeric value giving nudge in y direction for node labels
edge_width

numeric value giving the width of plotted edges

edge_arrow
logical indicating whether to add an arrow to edges
edge_arrow_ends
string indicating which ends of the line to draw arrow heads if edge_arrow is
TRUE, one of "last", "first", or "both"
show_axis

whether to show resolution axis

clustree

11

return

string specifying what to return, either "plot" (a ggplot object), "graph" (a
tbl_graph object) or "layout" (a ggraph layout object)

exprs

source of gene expression information to use as node aesthetics, for SingleCellExperiment
objects it must be a name in assayNames(x), for a seurat object it must be one
of data, raw.data or scale.data

Details
Data sources
Plotting a clustering tree requires information about which cluster each sample has been assigned to
at different resolutions. This information can be supplied in various forms, as a matrix, data.frame
or more specialised object. In all cases the object provided must contain numeric columns with the
naming structure PXS where P is a prefix indicating that the column contains clustering information,
X is a numeric value indicating the clustering resolution and S is any additional suffix to be removed.
For SingleCellExperiment objects this information must be in the colData slot and for Seurat
objects it must be in the meta.data slot. For all objects except matrices any additional columns can
be used as aesthetics, for matrices an additional metadata data.frame can be supplied if required.
Filtering
Edges in the graph can be filtered by adjusting the count_filter and prop_filter parameters.
The count_filter removes any edges that represent less than that number of samples, while the
prop_filter removes edges that represent less than that proportion of cells in the node it points
towards.
Node aesthetics
The aesthetics of the plotted nodes can be controlled in various ways. By default the colour indicates the clustering resolution, the size indicates the number of samples in that cluster and the
transparency is set to 100 Each of these can be set to a specific value or linked to a supplied metadata column. For a SingleCellExperiment or Seurat object the names of genes can also be used.
If a metadata column is used than an aggregation function must also be supplied to combine the
samples in each cluster. This function must take a vector of values and return a single value.
Layout
The clustering tree can be displayed using either the Reingold-Tilford tree layout algorithm or
the Sugiyama layout algorithm for layered directed acyclic graphs. These layouts were selected
as the are the algorithms available in the igraph package designed for trees. The ReingoldTilford algorithm places children below their parents while the Sugiyama places nodes in layers
while trying to minimise the number of crossing edges. See igraph::layout_as_tree() and
igraph::layout_with_sugiyama() for more details. When use_core_edges is TRUE (default)
only the core tree of the maximum in proportion edges for each node are used for constructing the
layout. This can often lead to more attractive layouts where the core tree is more visible.

Value
a ggplot object (default), a tbl_graph object or a ggraph layout object depending on the value of
return

12

clustree_overlay

Examples
data(iris_clusts)
clustree(iris_clusts, prefix = "K")

clustree_overlay

Overlay a clustering tree

Description
Creates a plot of a clustering tree overlaid on a scatter plot of individual samples.
Usage
clustree_overlay(x, ...)
## S3 method for class 'matrix'
clustree_overlay(x, prefix, metadata, x_value, y_value,
suffix = NULL, count_filter = 0, prop_filter = 0.1,
node_colour = prefix, node_colour_aggr = NULL, node_size = "size",
node_size_aggr = NULL, node_size_range = c(4, 15), node_alpha = 1,
node_alpha_aggr = NULL, edge_width = 1, use_colour = c("edges",
"points"), alt_colour = "black", point_size = 3, point_alpha = 0.2,
point_shape = 18, label_nodes = FALSE, label_size = 3,
plot_sides = FALSE, side_point_jitter = 0.45,
side_point_offset = 1, ...)
## S3 method for class 'data.frame'
clustree_overlay(x, prefix, ...)
## S3 method for class 'SingleCellExperiment'
clustree_overlay(x, prefix, x_value,
y_value, exprs = "counts", red_dim = NULL, ...)
## S3 method for class 'seurat'
clustree_overlay(x, x_value, y_value, prefix = "res.",
exprs = c("data", "raw.data", "scale.data"), red_dim = NULL, ...)
Arguments
x

object containing clustering data

...

extra parameters passed to other methods

prefix

string indicating columns containing clustering information

metadata

data.frame containing metadata on each sample that can be used as node aesthetics

x_value

numeric metadata column to use as the x axis

clustree_overlay

13

y_value

numeric metadata column to use as the y axis

suffix

string at the end of column names containing clustering information

count_filter

count threshold for filtering edges in the clustering graph

prop_filter

in proportion threshold for filtering edges in the clustering graph

node_colour

either a value indicating a colour to use for all nodes or the name of a metadata
column to colour nodes by

node_colour_aggr
if node_colour is a column name than a string giving the name of a function to
aggregate that column for samples in each cluster
node_size

either a numeric value giving the size of all nodes or the name of a metadata
column to use for node sizes

node_size_aggr if node_size is a column name than a string giving the name of a function to
aggregate that column for samples in each cluster
node_size_range
numeric vector of length two giving the maximum and minimum point size for
plotting nodes
node_alpha
node_alpha_aggr

either a numeric value giving the alpha of all nodes or the name of a metadata
column to use for node transparency
if node_aggr is a column name than a string giving the name of a function to
aggregate that column for samples in each cluster

edge_width

numeric value giving the width of plotted edges

use_colour

one of "edges" or "points" specifying which element to apply the colour aesthetic to

alt_colour

colour value to be used for edges or points (whichever is NOT given by use_colour)

point_size

numeric value giving the size of sample points

point_alpha

numeric value giving the alpha of sample points

point_shape

numeric value giving the shape of sample points

label_nodes

logical value indicating whether to add labels to clustering graph nodes

label_size

numeric value giving the size of node labels is label_nodes is TRUE

plot_sides

logical value indicating whether to produce side on plots

side_point_jitter
numeric value giving the y-direction spread of points in side plots
side_point_offset
numeric value giving the y-direction offset for points in side plots
exprs

source of gene expression information to use as node aesthetics, for SingleCellExperiment
objects it must be a name in assayNames(x), for a seurat object it must be one
of data, raw.data or scale.data

red_dim

dimensionality reduction to use as a source for x_value and y_value

14

clustree_overlay

Details
Data sources
Plotting a clustering tree requires information about which cluster each sample has been assigned to
at different resolutions. This information can be supplied in various forms, as a matrix, data.frame
or more specialised object. In all cases the object provided must contain numeric columns with the
naming structure PXS where P is a prefix indicating that the column contains clustering information,
X is a numeric value indicating the clustering resolution and S is any additional suffix to be removed.
For SingleCellExperiment objects this information must be in the colData slot and for Seurat
objects it must be in the meta.data slot. For all objects except matrices any additional columns can
be used as aesthetics.
Filtering
Edges in the graph can be filtered by adjusting the count_filter and prop_filter parameters.
The count_filter removes any edges that represent less than that number of samples, while the
prop_filter removes edges that represent less than that proportion of cells in the node it points
towards.
Node aesthetics
The aesthetics of the plotted nodes can be controlled in various ways. By default the colour indicates the clustering resolution, the size indicates the number of samples in that cluster and the
transparency is set to 100 Each of these can be set to a specific value or linked to a supplied metadata column. For a SingleCellExperiment or Seurat object the names of genes can also be used.
If a metadata column is used than an aggregation function must also be supplied to combine the
samples in each cluster. This function must take a vector of values and return a single value.
Colour aesthetic
The colour aesthetic can be applied to either edges or sample points by setting use_colour. If
"edges" is selected edges will be coloured according to the clustering resolution they originate at. If
"points" is selected they will be coloured according to the cluster they are assigned to at the highest
resolution.
Dimensionality reductions
For SingleCellExperiment and Seurat objects precomputed dimensionality reductions can be
used for x or y aesthetics. To do so red_dim must be set to the name of a dimensionality reduction
in reducedDimNames(x) (for a SingleCellExperiment) or x@dr (for a Seurat object). x_value
and y_value can then be set to red_dimX when red_dim matches the red_dim argument and X is
the column of the dimensionality reduction to use.
Value
a ggplot object if plot_sides is FALSE or a list of ggplot objects if plot_sides is TRUE
Examples
data(iris_clusts)
clustree_overlay(iris_clusts, prefix = "K", x_value = "PC1", y_value = "PC2")

get_tree_edges

get_tree_edges

15

Get tree edges

Description
Extract the edges from a set of clusterings
Usage
get_tree_edges(clusterings, prefix)
Arguments
clusterings

numeric matrix containing clustering information, each column contains clustering at a separate resolution

prefix

string indicating columns containing clustering information

Value
data.frame containing edge information

get_tree_nodes

Get tree nodes

Description
Extract the nodes from a set of clusterings and add relevant attributes
Usage
get_tree_nodes(clusterings, prefix, metadata, node_aes_list)
Arguments
clusterings

numeric matrix containing clustering information, each column contains clustering at a separate resolution

prefix

string indicating columns containing clustering information

metadata

data.frame containing metadata on each sample that can be used as node aesthetics

node_aes_list

nested list containing node aesthetics

Value
data.frame containing node information

16

overlay_node_points

iris_clusts

Clustered Iris dataset

Description
Iris dataset clustered using k-means with a range of values of k
Usage
iris_clusts
Format
iris_clusts is a data.frame containing the normal iris dataset with additional columns holding
k-means clusterings at different values of k and the first two principal components
Source
set.seed(1)
iris_mat <- as.matrix(iris[1:4])
iris_km <- sapply(1:5, function(x) {
km <- kmeans(iris_mat, centers = x, iter.max = 100, nstart = 10)
km$cluster
})
colnames(iris_km) <- paste0("K", 1:5)
iris_clusts <- cbind(iris, iris_km)
iris_pca <- prcomp(iris_clusts[1:4])
iris_clusts$PC1 <- iris_pca$x[, 1]
iris_clusts$PC2 <- iris_pca$x[, 2]

overlay_node_points

Overlay node points

Description
Overlay clustering tree nodes on a scatter plot with the specified aesthetics.
Usage
overlay_node_points(nodes, x_value, y_value, node_colour, node_size,
node_alpha)

plot_overlay_side

17

Arguments
nodes

data.frame describing nodes

x_value

column of nodes to use for the x position

y_value

column of nodes to use for the y position

node_colour

either a value indicating a colour to use for all nodes or the name of a metadata
column to colour nodes by

node_size

either a numeric value giving the size of all nodes or the name of a metadata
column to use for node sizes

node_alpha

either a numeric value giving the alpha of all nodes or the name of a metadata
column to use for node transparency

plot_overlay_side

Plot overlay side

Description
Plot the side view of a clustree overlay plot. If the ordinary plot shows the tree from above this plot
shows it from the side, highlighting either the x or y dimension and the clustering resolution.
Usage
plot_overlay_side(nodes, edges, points, prefix, side_value, graph_attr,
node_size_range, edge_width, use_colour, alt_colour, point_size,
point_alpha, point_shape, label_nodes, label_size, y_jitter, y_offset)
Arguments
nodes

data.frame describing nodes

edges

data.frame describing edges

points

data.frame describing points

prefix

string indicating columns containing clustering information

side_value

string giving the metadata column to use for the x axis

graph_attr
list describing graph attributes
node_size_range
numeric vector of length two giving the maximum and minimum point size for
plotting nodes
edge_width

numeric value giving the width of plotted edges

use_colour

one of "edges" or "points" specifying which element to apply the colour aesthetic to

alt_colour

colour value to be used for edges or points (whichever is NOT given by use_colour)

point_size

numeric value giving the size of sample points

point_alpha

numeric value giving the alpha of sample points

18

sc_example
point_shape

numeric value giving the shape of sample points

label_nodes

logical value indicating whether to add labels to clustering graph nodes

label_size

numeric value giving the size of node labels is label_nodes is TRUE

y_jitter

numeric value giving the y-direction spread of points in side plots

y_offset

numeric value giving the y-direction offset for points in side plots

Value
RETURN_DESCRIPTION

sc_example

Simulated scRNA-seq dataset

Description
A simulated scRNA-seq dataset generated using the splatter package and clustered using the SC3
and Seurat packages.
Usage
sc_example
Format
sc_example is a list holding a simulated scRNA-seq dataset. Items in the list included the simulated
counts, normalised log counts, tSNE dimensionality reduction and cell assignments from SC3 and
Seurat clustering.
Source
# Simulation
library("splatter") # Version 1.2.1
sim <- splatSimulate(batchCells = 200, nGenes = 10000,
group.prob = c(0.4, 0.2, 0.2, 0.15, 0.05),
de.prob = c(0.1, 0.2, 0.05, 0.1, 0.05),
method = "groups", seed = 1)
sim_counts <- counts(sim)[1:1000, ]
# SC3 Clustering
library("SC3") # Version 1.7.6
library("scater") # Version 1.6.2
sim_sc3 <- SingleCellExperiment(assays = list(counts = sim_counts))
rowData(sim_sc3)$feature_symbol <- rownames(sim_counts)
sim_sc3 <- normalise(sim_sc3)

store_node_aes

19

sim_sc3 <- sc3(sim_sc3, ks = 1:8, biology = FALSE, n_cores = 1)
sim_sc3 <- runTSNE(sim_sc3)
# Seurat Clustering
library("Seurat") # Version 2.2.0
sim_seurat <- CreateSeuratObject(sim_counts)
sim_seurat <- NormalizeData(sim_seurat, display.progress = FALSE)
sim_seurat <- FindVariableGenes(sim_seurat, do.plot = FALSE,
display.progress = FALSE)
sim_seurat <- ScaleData(sim_seurat, display.progress = FALSE)
sim_seurat <- RunPCA(sim_seurat, do.print = FALSE)
sim_seurat <- FindClusters(sim_seurat, dims.use = 1:6,
resolution = seq(0, 1, 0.1),
print.output = FALSE)
sc_example <- list(counts = counts(sim_sc3),
tsne = reducedDim(sim_sc3),
sc3_clusters = colData(sim_sc3),
seurat_clusters = sim_seurat@meta.data)

store_node_aes

Store node aesthetics

Description
Store the names of node attributes to use as aesthetics as graph attributes
Usage
store_node_aes(graph, node_aes_list, metadata)
Arguments
graph

graph to store attributes in

node_aes_list

nested list containing node aesthetics

metadata

data.frame containing metadata that can be used as aesthetics

Value
graph with additional attributes

Index
∗Topic datasets
iris_clusts, 16
sc_example, 18
add_node_labels, 2
add_node_points, 3
aggr_metadata, 4
assert_colour_node_aes, 4
assert_node_aes, 5
assert_numeric_node_aes, 5
build_tree_graph, 6
calc_sc3_stability, 7
calc_sc3_stability_cluster, 7
calc_sc3_stability_cluster(), 7
check_node_aes_list, 8
clustree, 9
clustree-package, 2
clustree_overlay, 12
get_tree_edges, 15
get_tree_nodes, 15
igraph::layout_as_tree(), 10, 11
igraph::layout_with_sugiyama(), 10, 11
iris_clusts, 16
overlay_node_points, 16
plot_overlay_side, 17
sc_example, 18
store_node_aes, 19
tidygraph::tbl_graph, 6

20



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