netVisual_embeddingPairwiseZoomIn: Zoom into the 2D visualization of the joint manifold learning...

In sqjin/CellChat: Inference and analysis of cell-cell communication from single-cell and spatial transcriptomics data

Zoom into the 2D visualization of the joint manifold learning of signaling networks from two datasets

Description

Zoom into the 2D visualization of the joint manifold learning of signaling networks from two datasets

Usage

netVisual_embeddingPairwiseZoomIn(
  object,
  slot.name = "netP",
  type = c("functional", "structural"),
  comparison = NULL,
  color.use = NULL,
  nCol = 1,
  point.shape = NULL,
  pathway.remove = NULL,
  dot.size = c(2, 6),
  label.size = 2.8,
  dot.alpha = 0.5,
  xlabel = NULL,
  ylabel = NULL,
  do.label = T,
  show.legend = F,
  show.axes = T
)

Arguments

object	CellChat object
slot.name	the slot name of object that is used to compute centrality measures of signaling networks
type	"functional","structural"
comparison	a numerical vector giving the datasets for comparison. Default are all datasets when object is a merged object
color.use	defining the color for each cell group
nCol	number of columns in the plot
point.shape	a numeric vector giving the point shapes. By default point.shape <- c(21, 0, 24, 23, 25, 10, 12), see available shapes at http://www.sthda.com/english/wiki/r-plot-pch-symbols-the-different-point-shapes-available-in-r
pathway.remove	a character vector defining the signaling to remove
dot.size	a range defining the size of the symbol
label.size	font size of the text
dot.alpha	transparency
xlabel	label of x-axis
ylabel	label of y-axis
do.label	label the each point
show.legend	whether show the legend
show.axes	whether show the axes

sqjin/CellChat documentation built on Nov. 10, 2023, 4:29 a.m.