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 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 data.frame
clustree_overlay(x, prefix, ...)
# S3 method for SingleCellExperiment
clustree_overlay(
  x,
  prefix,
  x_value,
  y_value,
  exprs = "counts",
  red_dim = NULL,
  ...
)
# S3 method for seurat
clustree_overlay(
  x,
  x_value,
  y_value,
  prefix = "res.",
  exprs = c("data", "raw.data", "scale.data"),
  red_dim = NULL,
  ...
)
# S3 method for Seurat
clustree_overlay(
  x,
  x_value,
  y_value,
  prefix = paste0(assay, "_snn_res."),
  exprs = c("data", "counts", "scale.data"),
  red_dim = NULL,
  assay = NULL,
  ...
)

Value

a ggplot object if plot_sides is FALSE or a list of ggplot

objects if plot_sides is TRUE

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
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: 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
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 and for a Seurat object it must be one of data, counts or scale.data
red_dim: dimensionality reduction to use as a source for x_value and y_value
assay: name of assay to pull expression and clustering data from for Seurat objects

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.

Examples

Run this code

data(nba_clusts)
clustree_overlay(nba_clusts, prefix = "K", x_value = "PC1", y_value = "PC2")

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