rankNet: Rank signaling networks based on the information flow or the...

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

Rank signaling networks based on the information flow or the number of interactions

Description

This function can also be used to rank signaling from certain cell groups to other cell groups

Usage

rankNet(
  object,
  slot.name = "netP",
  measure = c("weight", "count"),
  mode = c("comparison", "single"),
  comparison = c(1, 2),
  color.use = NULL,
  stacked = FALSE,
  sources.use = NULL,
  targets.use = NULL,
  signaling = NULL,
  pairLR = NULL,
  signaling.type = NULL,
  do.stat = FALSE,
  cutoff.pvalue = 0.05,
  tol = 0.05,
  thresh = 0.05,
  show.raw = FALSE,
  return.data = FALSE,
  x.rotation = 90,
  title = NULL,
  bar.w = 0.75,
  font.size = 8,
  do.flip = TRUE,
  x.angle = NULL,
  y.angle = 0,
  x.hjust = 1,
  y.hjust = 1,
  axis.gap = FALSE,
  ylim = NULL,
  segments = NULL,
  tick_width = NULL,
  rel_heights = c(0.9, 0, 0.1)
)

Arguments

object	CellChat object
slot.name	the slot name of object that is used to compute centrality measures of signaling networks
measure	"weight" or "count". "weight": comparing the total interaction weights (strength); "count": comparing the number of interactions;
mode	"single","comparison"
comparison	a numerical vector giving the datasets for comparison; a single value means ranking for only one dataset and two values means ranking comparison for two datasets
color.use	defining the color for each cell group
stacked	whether plot the stacked bar plot
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 vector giving the signaling pathway to show
pairLR	a vector giving the names of L-R pairs to show (e.g, pairLR = c("IL1A_IL1R1_IL1RAP","IL1B_IL1R1_IL1RAP"))
signaling.type	a char giving the types of signaling from the three categories c("Secreted Signaling", "ECM-Receptor", "Cell-Cell Contact")
do.stat	whether do a paired Wilcoxon test to determine whether there is significant difference between two datasets. Default = FALSE
cutoff.pvalue	the cutoff of pvalue when doing Wilcoxon test; Default = 0.05
tol	a tolerance when considering the relative contribution being equal between two datasets. contribution.relative between 1-tol and 1+tol will be considered as equal contribution
thresh	threshold of the p-value for determining significant interaction
show.raw	whether show the raw information flow. Default = FALSE, showing the scaled information flow to provide compariable data scale; When stacked = TRUE, use raw information flow by default.
return.data	whether return the data.frame consisting of the calculated information flow of each signaling pathway or L-R pair
x.rotation	rotation of x-labels
title	main title of the plot
bar.w	the width of bar plot
font.size	font size
do.flip	whether flip the x-y axis
x.angle, y.angle, x.hjust, y.hjust	parameters for rotating and spacing axis labels
axis.gap	whetehr making gaps in y-axes
ylim, segments, tick_width, rel_heights	parameters in the function gg.gap when making gaps in y-axes e.g., ylim = c(0, 35), segments = list(c(11, 14),c(16, 28)), tick_width = c(5,2,5), rel_heights = c(0.8,0,0.1,0,0.1) https://tobiasbusch.xyz/an-r-package-for-everything-ep2-gaps

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