netVisual_embeddingPairwise: 2D visualization of the joint manifold learning of signaling...

View source: R/visualization.R

netVisual_embeddingPairwiseR Documentation

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

Description

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

Usage

netVisual_embeddingPairwise(
  object,
  slot.name = "netP",
  type = c("functional", "structural"),
  comparison = NULL,
  color.use = NULL,
  point.shape = NULL,
  pathway.labeled = NULL,
  top.label = 1,
  pathway.remove = NULL,
  pathway.remove.show = TRUE,
  dot.size = c(2, 6),
  label.size = 2.5,
  dot.alpha = 0.5,
  xlabel = "Dim 1",
  ylabel = "Dim 2",
  title = NULL,
  do.label = T,
  show.legend = T,
  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

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.labeled

a char vector giving the signaling names to show when labeling each point

top.label

the fraction of signaling pathways to label

pathway.remove

a character vector defining the signaling to remove

pathway.remove.show

whether show the removed signaling names

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

title

main title of the plot

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.