doGiottoClustree

cluster cells using leiden methodology to visualize different resolutions

doGiottoClustree(
  gobject,
  res_vector = NULL,
  res_seq = NULL,
  return_gobject = FALSE,
  show_plot = NULL,
  save_plot = NULL,
  return_plot = NULL,
  save_param = list(),
  default_save_name = "clustree",
  verbose = TRUE,
  ...
)

Arguments

gobject

giotto object

res_vector

vector of different resolutions to test

res_seq

list of float numbers indicating start, end, and step size for resolution testing, i.e. (0.1, 0.6, 0.1)

return_gobject

default FALSE. See details for more info.

show_plot

by default, pulls from provided gobject instructions

save_plot

by default, pulls from provided gobject instructions

return_plot

by default, pulls from provided gobject instructions

save_param

list of saving parameters from GiottoVisuals::all_plots_save_function()

default_save_name

name of saved plot, default "clustree"

verbose

be verbose

...

Arguments passed on to clustree::clustree

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
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_text_angle: the rotation of the node text
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
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 and for a Seurat object it must be one of data, counts or scale.data
assay: name of assay to pull expression and clustering data from for Seurat objects

Value

a plot object (default), OR a giotto object (if specified)

Details

This function tests different resolutions for Leiden clustering and provides a visualization of cluster sizing as resolution varies.

By default, the tested leiden clusters are NOT saved to the Giotto object, and a plot is returned.

If return_gobject is set to TRUE, and a giotto object with all tested leiden cluster information will be returned.

Examples

g <- GiottoData::loadGiottoMini("visium")
#> 1. read Giotto object
#> 2. read Giotto feature information
#> 3. read Giotto spatial information
#> 4. read Giotto image information
#> python already initialized in this session
#>  active environment : '/usr/bin/python3'
#>  python version : 3.12

doGiottoClustree(
    gobject = g, res_vector = c(0.5, 0.8), return_plot = FALSE,
    show_plot = FALSE, save_plot = FALSE
)
#> Error: package 'clustree' is not yet installed
#> 
#>  To install:
#> install.packages(c("clustree"))

Arguments

Value

Details

See also

Examples