netVisual_bubble: Show all the significant interactions (L-R pairs) from some...

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

Show all the significant interactions (L-R pairs) from some cell groups to other cell groups

Description

The dot color and size represent the calculated communication probability and p-values.

Usage

netVisual_bubble(
  object,
  sources.use = NULL,
  targets.use = NULL,
  signaling = NULL,
  pairLR.use = NULL,
  sort.by.source = FALSE,
  sort.by.target = FALSE,
  sort.by.source.priority = TRUE,
  color.heatmap = c("Spectral", "viridis"),
  n.colors = 10,
  direction = -1,
  thresh = 0.05,
  comparison = NULL,
  group = NULL,
  remove.isolate = FALSE,
  max.dataset = NULL,
  min.dataset = NULL,
  min.quantile = 0,
  max.quantile = 1,
  line.on = TRUE,
  line.size = 0.2,
  color.text.use = TRUE,
  color.text = NULL,
  title.name = NULL,
  font.size = 10,
  font.size.title = 10,
  show.legend = TRUE,
  grid.on = TRUE,
  color.grid = "grey90",
  angle.x = 90,
  vjust.x = NULL,
  hjust.x = NULL,
  return.data = FALSE
)

Arguments

object	CellChat object
sources.use	a vector giving the index or the name of source cell groups
targets.use	a vector giving the index or the name of target cell groups.
signaling	a character vector giving the name of signaling pathways of interest
pairLR.use	a data frame consisting of one column named either "interaction_name" or "pathway_name", defining the interactions of interest and the order of L-R on y-axis
sort.by.source, sort.by.target, sort.by.source.priority	set the order of interacting cell pairs on x-axis; please check examples for details
color.heatmap	A character string or vector indicating the colormap option to use. It can be the avaibale color palette in viridis_pal() or brewer.pal()
n.colors	number of basic colors to generate from color palette
direction	Sets the order of colors in the scale. If 1, the default colors are used. If -1, the order of colors is reversed.
thresh	threshold of the p-value for determining significant interaction
comparison	a numerical vector giving the datasets for comparison in the merged object; e.g., comparison = c(1,2)
group	a numerical vector giving the group information of different datasets; e.g., group = c(1,2,2)
remove.isolate	whether remove the entire empty column, i.e., communication between certain cell groups
max.dataset	a scale, keep the communications with highest probability in max.dataset (i.e., certrain condition)
min.dataset	a scale, keep the communications with lowest probability in min.dataset (i.e., certrain condition)
min.quantile, max.quantile	minimum and maximum quantile cutoff values for the colorbar, may specify quantile in [0,1]
line.on	whether add vertical line when doing comparison analysis for the merged object
line.size	size of vertical line if added
color.text.use	whether color the xtick labels according to the dataset origin when doing comparison analysis
color.text	the colors for xtick labels according to the dataset origin when doing comparison analysis
title.name	main title of the plot
font.size, font.size.title	font size of all the text and the title name
show.legend	whether show legend
grid.on, color.grid	whether add grid
angle.x, vjust.x, hjust.x	parameters for adjusting the rotation of xtick labels
return.data	whether return the data.frame for replotting

Examples

## Not run: 
# show all the significant interactions (L-R pairs) from some cell groups (defined by 'sources.use') to other cell groups (defined by 'targets.use')
netVisual_bubble(cellchat, sources.use = 4, targets.use = c(5:11), remove.isolate = FALSE)

# show all the significant interactions (L-R pairs) associated with certain signaling pathways
netVisual_bubble(cellchat, sources.use = 4, targets.use = c(5:11), signaling = c("CCL","CXCL"))

# show all the significant interactions (L-R pairs) based on user's input (defined by `pairLR.use`; the order of L-R is also based on user's input)
pairLR.use <- extractEnrichedLR(cellchat, signaling = c("CCL","CXCL","FGF"))
netVisual_bubble(cellchat, sources.use = c(3,4), targets.use = c(5:8), pairLR.use = pairLR.use, remove.isolate = TRUE)

# set the order of interacting cell pairs on x-axis
# (1) Default: first sort cell pairs based on the appearance of sources in levels(object@idents), and then based on the appearance of targets in levels(object@idents)
# (2) sort cell pairs based on the targets.use defined by users
netVisual_bubble(cellchat, targets.use = c("LC","Inflam. DC","cDC2","CD40LG+ TC"), pairLR.use = pairLR.use, remove.isolate = TRUE, sort.by.target = T)
# (3) sort cell pairs based on the sources.use defined by users
netVisual_bubble(cellchat, sources.use = c("FBN1+ FIB","APOE+ FIB","Inflam. FIB"), pairLR.use = pairLR.use, remove.isolate = TRUE, sort.by.source = T)
# (4) sort cell pairs based on the sources.use and then targets.use defined by users
netVisual_bubble(cellchat, sources.use = c("FBN1+ FIB","APOE+ FIB","Inflam. FIB"), targets.use = c("LC","Inflam. DC","cDC2","CD40LG+ TC"), pairLR.use = pairLR.use, remove.isolate = TRUE, sort.by.source = T, sort.by.target = T)
# (5) sort cell pairs based on the targets.use and then sources.use defined by users
netVisual_bubble(cellchat, sources.use = c("FBN1+ FIB","APOE+ FIB","Inflam. FIB"), targets.use = c("LC","Inflam. DC","cDC2","CD40LG+ TC"), pairLR.use = pairLR.use, remove.isolate = TRUE, sort.by.source = T, sort.by.target = T, sort.by.source.priority = FALSE)

# show all the increased interactions in the second dataset compared to the first dataset
netVisual_bubble(cellchat, sources.use = 4, targets.use = c(5:8), remove.isolate = TRUE, max.dataset = 2)

# show all the decreased interactions in the second dataset compared to the first dataset
netVisual_bubble(cellchat, sources.use = 4, targets.use = c(5:8), remove.isolate = TRUE, max.dataset = 1)

## End(Not run)

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