Package 'CellChat'

Title: Inference and analysis of cell-cell communication from single-cell and spatially resolved transcriptomics data
Description: an open source R tool that infers, visualizes and analyzes the cell-cell communication networks from scRNA-seq and spatially resolved transcriptomics data.
Authors: Suoqin Jin
Maintainer: Suoqin Jin <[email protected]>
License: GPL-3
Version: 2.2.0.9001
Built: 2026-05-23 09:07:27 UTC
Source: https://github.com/jinworks/CellChat

Help Index

Add the cell information into meta slot

Description

Add the cell information into meta slot

Usage

addMeta(object, meta, meta.name = NULL)

Arguments

object

CellChat object
meta

cell information to be added
meta.name

the name of column to be assigned

Add a reduced space of the data into CellChat object

Description

Add a reduced space of the data into CellChat object

Usage

addReduction(
  object,
  dr = NULL,
  dr.name = NULL,
  seu.obj = NULL,
  dr.use = NULL,
  force.add = FALSE
)

Arguments

object

CellChat object from a single dataset
dr

A data frame (rows are cells with rownames) consisting of a low-dimensional space for visualization
dr.name

A char name of the reduction method for the input 'dr'
seu.obj

A Seurat object with the reduced space of the data
dr.use

A char name of the reduction method to use when taking 'seu.obj' as input. By default, all reduced space in 'seu.obj' will be added in 'object@dr'
force.add

Whether to force to add a new reduced space when a reduced space exists in 'object@dr'

Examples

## Not run: 
cellChat <- addReduction(object = cellchat, dr = cell.embeddings, dr.name = "umap")

cellChat <- addReduction(object = cellchat, seu.obj = seu.obj)

## End(Not run)

Calculate the aggregated network by counting the number of links or summarizing the communication probability

Description

Calculate the aggregated network by counting the number of links or summarizing the communication probability

Usage

aggregateNet(
  object,
  sources.use = NULL,
  targets.use = NULL,
  signaling = NULL,
  pairLR.use = NULL,
  remove.isolate = TRUE,
  thresh = 0.05,
  return.object = TRUE
)

Arguments

object

CellChat object
sources.use, targets.use, signaling, pairLR.use

Please check the description in function subsetCommunication
remove.isolate

whether removing the isolate cell groups without any interactions when applying subsetCommunication
thresh

threshold of the p-value for determining significant interaction
return.object

whether return an updated CellChat object

Value

Return an updated CellChat object:

'object@net$count' is a matrix: rows and columns are sources and targets respectively, and elements are the number of interactions between any two cell groups. USER can convert a matrix to a data frame using the function 'reshape2::melt()'

'object@net$weight' is also a matrix containing the interaction weights between any two cell groups

'object@net$sum' is deprecated. Use 'object@net$weight'

compute alpha centrality

Description

compute alpha centrality

Usage

alpha_centrality(g)

Arguments

g

a graph objecct

The CellChat Class

Description

The CellChat object is created from a single-cell transcriptomic data matrix, Seurat V3 or SingleCellExperiment object. When inputting an data matrix, it takes a digital data matrices as input. Genes should be in rows and cells in columns. rownames and colnames should be included. The class provides functions for data preprocessing, intercellular communication network inference, communication network analysis, and visualization.

Details

# Class definitions

Bar plot for average gene expression

Description

Please check barplot_internalfor detailed description of the arguments.

Usage

barPlot(
  object,
  features,
  group.by = NULL,
  split.by = NULL,
  color.use = NULL,
  method = c("truncatedMean", "triMean", "median"),
  trim = 0.1,
  assay = "RNA",
  x.lab.rot = FALSE,
  ncol = 1,
  ...
)

Arguments

object

seurat object
features

Features to plot (gene expression, metrics)
group.by

Name of one or more metadata columns to group (color) cells by (for example, orig.ident); pass 'ident' to group by identity class
split.by

Name of a metadata column to split plot by;
color.use

defining the color for each condition/dataset
method

methods for computing the average gene expression per cell group. By default = "truncatedMean", where a value should be assigned to 'trim;
trim

the fraction (0 to 0.5) of observations to be trimmed from each end of x before the mean is computed.
assay

Name of assay to use, defaults to the active assay
x.lab.rot

whether do rotation for the x.tick.label
ncol

number of columns to show in the plot
...

Extra parameters passed to barplot_internal

Value

ggplot2 object

Bar plot for dataframe

Description

Bar plot for dataframe

Usage

barplot_internal(
  df,
  x = "cellType",
  y = "value",
  fill = "condition",
  legend.title = NULL,
  width = 0.6,
  title.name = NULL,
  xlabel = NULL,
  ylabel = NULL,
  color.use = NULL,
  remove.xtick = FALSE,
  stat.add = FALSE,
  stat.method = "wilcox.test",
  percent.y = FALSE,
  label.x = 1.5,
  show.legend = TRUE,
  x.lab.rot = FALSE,
  size.text = 10
)

Arguments

df

a dataframe
x

Name of one column to show on the x-axis
y

Name of one column to show on the y-axis
fill

Name of one column to compare the values
legend.title

Name of legend
width

bar width
title.name

Name of the main title
xlabel

Name of x label
ylabel

Name of y label
color.use

defining the color of bar plot;
remove.xtick

whether remove x tick
stat.add

whether adding statistical test
stat.method, label.x

parameters for ggpubr::stat_compare_means
percent.y

whether showing y-values as percentage
show.legend

Whether show the legend
x.lab.rot

Whether rorate the xtick labels
size.text

font size

Value

ggplot2 object

Build SNN matrix

Description

Build SNN matrix

Usage

buildSNN(data.use, k = 10, k.scale = 10, prune.SNN = 1/15)

Arguments

data.use

Features x samples matrix to use to build the SNN
k

Defines k for the k-nearest neighbor algorithm
k.scale

Granularity option for k.param
prune.SNN

Sets the cutoff for acceptable Jaccard distances when computing the neighborhood overlap for the SNN construction.

Value

Returns similarity matrix in sparse matrix format

ggplot theme in CellChat

Description

ggplot theme in CellChat

Usage

CellChat_theme_opts()

The key slots used in the CellChat object are described below.

Description

The key slots used in the CellChat object are described below.

Slots

data.raw

raw count data matrix

data

normalized data matrix for CellChat analysis (Genes should be in rows and cells in columns)

data.signaling

a subset of normalized matrix only containing signaling genes

data.scale

scaled data matrix

data.smooth

smoothed data

images

a list of information of spatial transcriptomics data

net

a three-dimensional array P (K×K×N), where K is the number of cell groups and N is the number of ligand-receptor pairs. Each row of P indicates the communication probability originating from the sender cell group to other cell groups.

netP

a three-dimensional array representing cel-cell communication networks on a signaling pathway level

DB

ligand-receptor interaction database used in the analysis (a subset of CellChatDB)

LR

a list of information related with ligand-receptor pairs

meta

data frame storing the information associated with each cell

idents

a factor defining the cell identity used for all analysis. It becomes a list for a merged CellChat object

var.features

A list: one element is a vector consisting of the identified over-expressed signaling genes; one element is a data frame returned from the differential expression analysis

dr

List of the reduced 2D coordinates, one per method, e.g., umap/tsne/dm

options

List of miscellaneous data, such as parameters used throughout analysis, and a indicator whether the CellChat object is a single or merged

Ligand-receptor interactions in CellChat database for human

Description

The ligand-receptor interaction database curated in CellChat tool

Usage

CellChatDB.human

Format

A list includes the ligand-receptor interactions

Source

https://github.com/sqjin/CellChat/

Ligand-receptor interactions in CellChat database for mouse

Description

The ligand-receptor interaction database curated in CellChat tool

Usage

CellChatDB.mouse

Format

A list includes the ligand-receptor interactions

Source

https://github.com/sqjin/CellChat/

Ligand-receptor interactions in CellChat database for Zebrafish

Description

The ligand-receptor interaction database curated in CellChat tool

Usage

CellChatDB.zebrafish

Format

A list includes the ligand-receptor interactions

Source

https://github.com/sqjin/CellChat/

check the official Gene Symbol

Description

check the official Gene Symbol

Usage

checkGeneSymbol(geneSet, geneIfo)

Arguments

geneSet

gene set to check
geneIfo

official Gene Symbol

Color interpolation

Description

This function is modified from https://rdrr.io/cran/circlize/src/R/utils.R Colors are linearly interpolated according to break values and corresponding colors through CIE Lab color space ('colorspace::LAB') by default. Values exceeding breaks will be assigned with corresponding maximum or minimum colors.

Usage

colorRamp3(breaks, colors, transparency = 0, space = "LAB")

Arguments

breaks

A vector indicating numeric breaks
colors

A vector of colors which correspond to values in “breaks“
transparency

A single value in “[0, 1]“. 0 refers to no transparency and 1 refers to full transparency
space

color space in which colors are interpolated. Value should be one of "RGB", "HSV", "HLS", "LAB", "XYZ", "sRGB", "LUV", see 'colorspace::color-class' for detail.

Value

It returns a function which accepts a vector of numeric values and returns interpolated colors.

Examples

## Not run: 
col_fun = colorRamp3(c(-1, 0, 1), c("green", "white", "red"))
col_fun(c(-2, -1, -0.5, 0, 0.5, 1, 2))

## End(Not run)

Comparing the number of inferred communication links between different datasets

Description

Comparing the number of inferred communication links between different datasets

Usage

compareInteractions(
  object,
  measure = c("count", "weight"),
  color.use = NULL,
  group = NULL,
  group.levels = NULL,
  group.facet = NULL,
  group.facet.levels = NULL,
  n.row = 1,
  color.alpha = 1,
  legend.title = NULL,
  width = 0.6,
  title.name = NULL,
  digits = 3,
  xlabel = NULL,
  ylabel = NULL,
  remove.xtick = FALSE,
  show.legend = TRUE,
  x.lab.rot = FALSE,
  angle.x = 45,
  vjust.x = NULL,
  hjust.x = 1,
  size.text = 10
)

Arguments

object

A merged CellChat object
measure

"count" or "weight". "count": comparing the number of interactions; "weight": comparing the total interaction weights (strength)
color.use

defining the color for each group of datasets
group

a vector giving the groups of different datasets to define colors of the bar plot. Default: only one group and a single color
group.levels

the factor level in the defined group
group.facet

Name of one metadata column defining faceting groups
group.facet.levels

the factor level in the defined group.facet
n.row

Number of rows in facet_grid()
color.alpha

transparency
legend.title

legend title
width

bar width
title.name

main title of the plot
digits

integer indicating the number of decimal places (round) to be used when 'measure' is 'weight'.
xlabel

label of x-axis
ylabel

label of y-axis
remove.xtick

whether remove xtick
show.legend

whether show the legend
x.lab.rot, angle.x, vjust.x, hjust.x

adjusting parameters if rotating xtick.labels when x.lab.rot = TRUE
size.text

font size of the text

Value

A ggplot object

Compute averaged expression values for each cell group

Description

Compute averaged expression values for each cell group

Usage

computeAveExpr(
  object,
  features = NULL,
  group.by = NULL,
  type = c("triMean", "truncatedMean", "median"),
  trim = NULL,
  slot.name = c("data.signaling", "data"),
  data.use = NULL
)

Arguments

object

CellChat object
features

a char vector giving the used features. default use all features
group.by

cell group information; default is 'object@idents' when input is a single object and 'object@idents$joint' when input is a merged object; otherwise it should be one of the column names of the meta slot
type

methods for computing the average gene expression per cell group.

By default = "triMean", defined as a weighted average of the distribution's median and its two quartiles (https://en.wikipedia.org/wiki/Trimean);

When setting ‘type = "truncatedMean"', a value should be assigned to ’trim'. See the function 'base::mean'.
trim

the fraction (0 to 0.25) of observations to be trimmed from each end of x before the mean is computed.
slot.name

the data in the slot.name to use
data.use

a customed data matrix. Default: data.use = NULL and the expression matrix in the 'slot.name' is used

Value

Returns a matrix with genes as rows, cell groups as columns.

Compute cell-cell distance based on the spatial coordinates

Description

Compute cell-cell distance based on the spatial coordinates

Usage

computeCellDistance(
  coordinates,
  interaction.range = NULL,
  ratio = NULL,
  tol = NULL
)

Arguments

coordinates

a data matrix in which each row gives the spatial locations/coordinates of each cell/spot
interaction.range

The maximum interaction/diffusion range of ligands. This hard threshold is used to filter out the connections between spatially distant cells
ratio

The conversion factor when converting spatial coordinates from Pixels or other units to Micrometers (i.e.,Microns).

For example, setting 'ratio = 0.18' indicates that 1 pixel equals 0.18um in the coordinates. For 10X visium, it is the ratio of the theoretical spot size (i.e., 65um) over the number of pixels that span the diameter of a theoretical spot size in the full-resolution image (i.e., 'spot.size.fullres' in the 'scalefactors_json.json' file).
tol

The tolerance factor to increase the robustness when comparing the center-to-center distance against the 'interaction.range'. This can be the half value of cell/spot size in the unit of um.

For example, for 10X visium, 'tol' can be set as '65/2'; for slide-seq, 'tol' can be set as '10/2'. If the cell/spot size is not known, we provide a function 'computeCellDistance' to compute the center-to-center distance. 'tol' can be the the half value of the minimum center-to-center distance.

Value

an object of class "dist" giving the pairwise cell-cell distance

Compute Centrality measures for a signaling network

Description

Compute Centrality measures for a signaling network

Usage

computeCentralityLocal(net)

Arguments

net

compute the centrality measures on a specific signaling network given by a 2 or 3 dimemsional array net

Compute the communication probability/strength between any interacting cell groups

Description

To further speed up on large-scale datasets, USER can downsample the data using the function 'subset' from Seurat package (e.g., pbmc.small <- subset(pbmc, downsample = 500)), or using the function 'sketchData' from CellChat, in particular for the large cell clusters;

Usage

computeCommunProb(
  object,
  type = c("triMean", "truncatedMean", "thresholdedMean", "median"),
  trim = 0.1,
  LR.use = NULL,
  raw.use = TRUE,
  population.size = FALSE,
  distance.use = TRUE,
  interaction.range = 250,
  scale.distance = 0.01,
  k.min = 10,
  contact.dependent = TRUE,
  contact.range = NULL,
  contact.knn.k = NULL,
  contact.dependent.forced = FALSE,
  do.symmetric = TRUE,
  nboot = 100,
  seed.use = 1L,
  Kh = 0.5,
  n = 1
)

Arguments

object

CellChat object
type

Methods for computing the average gene expression per cell group. By default = "triMean", producing fewer but stronger interactions; When setting ‘type = "truncatedMean"', a value should be assigned to ’trim', producing more interactions.
trim

the fraction (0 to 0.25) of observations to be trimmed from each end of x before the mean is computed
LR.use

A subset of ligand-receptor interactions used in inferring communication network
raw.use

Whether use the raw data (i.e., '[email protected]') or the smoothed data (i.e., '[email protected]'). Set raw.use = FALSE to use the projected data when analyzing single-cell data with shallow sequencing depth because the projected data could help to reduce the dropout effects of signaling genes, in particular for possible zero expression of subunits of ligands/receptors.
population.size

Whether consider the proportion of cells in each group across all sequenced cells. Set population.size = FALSE if analyzing sorting-enriched single cells, to remove the potential artifact of population size. Set population.size = TRUE if analyzing unsorted single-cell transcriptomes, with the reason that abundant cell populations tend to send collectively stronger signals than the rare cell populations.

Parameters for spatial data analysis:
distance.use

Whether to use distance constraints to compute communication probability. Setting 'distance.use = TRUE' indicates that the cell-cell communication probability is inversely proportional to the computed distance. Setting 'distance.use = FALSE' will only filter out interactions between spatially distant regions, but not add distance constraints.
interaction.range

The maximum interaction/diffusion length of ligands (Unit: microns). This hard threshold is used to filter out the connections between spatially distant regions
scale.distance

A scale or normalization factor for the spatial distances when setting 'distance.use = TRUE'. For example, scale.distance equals 1, 0.1, 0.01, 0.001, 0.11, or 0.011. We choose this values such that the minimum value of the scaled distances is in [1,2]. This value is not necessary when setting 'distance.use = FALSE'.

When comparing communication across different CellChat objects, the same scale factor should be used. For a single CellChat analysis, different scale factors will not affect the ranking of the signaling based on their interaction strength.
k.min

The minimum number of interacting cell pairs required for defining spatially proximal cell groups.
contact.dependent

Whether using the 'contact-dependent' manner for inference signaling, that is determining interacting cell pairs by requiring cells to be in direct membrane-membrane contact. By default 'contact.dependent = TRUE' when inferring contact-dependent and juxtacrine signaling (that is "Cell-Cell Contact" signaling classified in CellChatDB$interaction$annotation). If only focusing on 'Secreted Signaling', the 'contact-dependent' manner will be not used except for setting 'contact.dependent.forced = TRUE'.
contact.range

The interaction range (Unit: microns) to restrict the contact-dependent signaling when 'contact.dependent = TRUE'. For spatial transcriptomics in a single-cell resolution, 'contact.range' is approximately equal to the estimated cell diameter (i.e., the cell center-to-center distance), which means that contact-dependent and juxtacrine signaling can only happens when the two cells are contact to each other.

Typically, 'contact.range = 10', which is a typical human cell size. However, for low-resolution spatial data such as 10X visium, it should be the cell center-to-center distance (i.e., 'contact.range = 100' for visium data). The function 'computeCellDistance' can compute the center-to-center distance.
contact.knn.k

Number of neighbors to restrict the contact-dependent signaling within the neatest neighbors when 'contact.dependent = TRUE'. By default, CellChat uses 'contact.range' to restrict the contact-dependent signaling; however, users can also provide a value of 'contact.knn.k', in order to determine interacting cell pairs based on the k-nearest neighbors (knn). For 10X visium, contact.knn.k = 6. For other spatial technologies, this value may be hard to determine because the sequenced cells/spots are usually not regularly arranged.
contact.dependent.forced

Whether forcing to use the 'contact-dependent' manner for inference signaling for all L-R pairs including secreted signaling. Users can set 'contact.dependent.forced = TRUE' if also preferring interactions within a contact manner for 'Secreted Signaling'.
do.symmetric

Whether converting the adjacent matrix into symmetric one when determining spatially proximal cell groups. Default is TRUE, indicating that if adj(i,j) or adj(j,i) is zero, then both are zeros.
nboot

Threshold of p-values
seed.use

Set a random seed. By default, set the seed to 1.
Kh

Parameter in Hill function
n

Parameter in Hill function

Value

A CellChat object with updated slot 'net':

object@net$prob is the inferred communication probability (strength) array, where the first, second and third dimensions represent a source, target and ligand-receptor pair, respectively.

USER can access all the inferred cell-cell communications using the function 'subsetCommunication(object)', which returns a data frame.

object@net$pval is the corresponding p-values of each interaction

Compute the communication probability on signaling pathway level by summarizing all related ligands/receptors

Description

Compute the communication probability on signaling pathway level by summarizing all related ligands/receptors

Usage

computeCommunProbPathway(
  object = NULL,
  net = NULL,
  pairLR.use = NULL,
  thresh = 0.05
)

Arguments

object

CellChat object
net

A list from object@net; If net = NULL, net = object@net
pairLR.use

A dataframe giving the ligand-receptor interactions; If pairLR.use = NULL, pairLR.use = object@LR$LRsig
thresh

threshold of the p-value for determining significant interaction

Value

A CellChat object with updated slot 'netP':

object@netP$prob is the communication probability array on signaling pathway level; USER can convert this array to a data frame using the function 'reshape2::melt()',

e.g., 'df.netP <- reshape2::melt(object@netP$prob, value.name = "prob"); colnames(df.netP)[1:3] <- c("source","target","pathway_name")' or access all significant interactions using the function subsetCommunication

object@netP$pathways list all the signaling pathways with significant communications.

From version >= 1.1.0, pathways are ordered based on the total communication probabilities. NB: pathways with small total communication probabilities might be also very important since they might be specifically activated between only few cell types.

Compute the eigengap of a given matrix for inferring the number of clusters

Description

Compute the eigengap of a given matrix for inferring the number of clusters

Usage

computeEigengap(CM, tau = NULL, tol = 0.01)

Arguments

CM

consensus matrix
tau

truncated consensus matrix
tol

tolerance

Compute and visualize the enrichment score of ligand-receptor pairs in one condition compared to another condition

Description

Compute and visualize the enrichment score of ligand-receptor pairs in one condition compared to another condition

Usage

computeEnrichmentScore(
  df,
  measure = c("ligand", "signaling", "LR-pair"),
  variable.both = TRUE,
  species = c("mouse", "human"),
  db = NULL,
  color.use = NULL,
  color.name = "Dark2",
  n.color = 8,
  scale = c(4, 0.8),
  min.freq = 0,
  max.words = 200,
  random.order = FALSE,
  rot.per = 0,
  return.data = FALSE,
  seed = 1,
  ...
)

Arguments

df

a dataframe
measure

compute the enrichment score in terms of "ligand", "signaling",or "LR-pair"
variable.both

variable.both = TRUE will require that both ligand and receptor from one pair are over-expressed; variable.both = FALSE will only require that either ligand or receptor from one pair is over-expressed.
species

define the species as one of the c('mouse','human') to extract the CellChatDB; For other species, users need to provide a ligand-receptor database 'db'
db

a customized ligand-receptor database 'db'
color.use

defining the color for each group of datasets
color.name

the color names in RColorBrewer::brewer.pal
n.color

the number of colors
scale

A vector of length 2 indicating the range of the size of the words.
min.freq

words with frequency below min.freq will not be plotted
max.words

Maximum number of words to be plotted. least frequent terms dropped
random.order

plot words in random order. If false, they will be plotted in decreasing frequency
rot.per

proportion words with 90 degree rotation
return.data

whether return the data frame for plotting wordcloud
seed

set a seed
...

Other parameters passing to wordcloud::wordcloud

Value

A ggplot object

Modeling the effect of agonist on the ligand-receptor interaction

Description

Modeling the effect of agonist on the ligand-receptor interaction

Usage

computeExpr_agonist(data.use, pairLRsig, cofactor_input, index.agonist, Kh, n)

Arguments

data.use

data matrix
pairLRsig

the L-R interactions
cofactor_input

the cofactor_input from CellChatDB
index.agonist

the index of agonist in the database
Kh

a parameter in Hill function
n

Hill coefficient

Modeling the effect of antagonist on the ligand-receptor interaction

Description

Modeling the effect of antagonist on the ligand-receptor interaction

Usage

computeExpr_antagonist(
  data.use,
  pairLRsig,
  cofactor_input,
  index.antagonist,
  Kh,
  n
)

Arguments

data.use

data matrix
pairLRsig

the L-R interactions
cofactor_input

the cofactor_input from CellChatDB
index.antagonist

the index of antagonist in the database
Kh

a parameter in Hill function
n

Hill coefficient

Compute the expression of complex in individual cells using geometric mean

Description

Compute the expression of complex in individual cells using geometric mean

Usage

computeExpr_complex(complex_input, data.use, complex)

Arguments

complex_input

the complex_input from CellChatDB
data.use

data matrix (row are genes and columns are cells or cell groups)
complex

the names of complex

Modeling the effect of coreceptor on the ligand-receptor interaction

Description

Modeling the effect of coreceptor on the ligand-receptor interaction

Usage

computeExpr_coreceptor(cofactor_input, data.use, pairLRsig, type = c("A", "I"))

Arguments

cofactor_input

the cofactor_input from CellChatDB
data.use

data matrix
pairLRsig

a data frame giving ligand-receptor interactions
type

when type == "A", computing expression of co-activation receptor; when type == "I", computing expression of co-inhibition receptor.

Compute the expression of ligands or receptors using geometric mean

Description

Compute the expression of ligands or receptors using geometric mean

Usage

computeExpr_LR(geneLR, data.use, complex_input)

Arguments

geneLR

a char vector giving a set of ligands or receptors
data.use

data matrix (row are genes and columns are cells or cell groups)
complex_input

the complex_input from CellChatDB

Modeling the effect of agonist on the ligand-receptor interaction

Description

Modeling the effect of agonist on the ligand-receptor interaction

Usage

computeExprGroup_agonist(
  data.use,
  pairLRsig,
  cofactor_input,
  group,
  index.agonist,
  Kh,
  FunMean,
  n
)

Arguments

data.use

data matrix
pairLRsig

the L-R interactions
cofactor_input

the cofactor_input from CellChatDB
group

a factor defining the cell groups
index.agonist

the index of agonist in the database
Kh

a parameter in Hill function
FunMean

the function for computing mean expression per group
n

Hill coefficient

Modeling the effect of antagonist on the ligand-receptor interaction

Description

Modeling the effect of antagonist on the ligand-receptor interaction

Usage

computeExprGroup_antagonist(
  data.use,
  pairLRsig,
  cofactor_input,
  group,
  index.antagonist,
  Kh,
  FunMean,
  n
)

Arguments

data.use

data matrix
pairLRsig

the L-R interactions
cofactor_input

the cofactor_input from CellChatDB
group

a factor defining the cell groups
index.antagonist

the index of antagonist in the database
Kh

a parameter in Hill function
FunMean

the function for computing mean expression per group
n

Hill coefficient

Compute eigenvalues of associated Laplacian matrix of a given matrix

Description

Compute eigenvalues of associated Laplacian matrix of a given matrix

Usage

computeLaplacian(CM, tol = 0.01)

Arguments

CM

consensus matrix
tol

tolerance

Compute the structural distance between two signaling networks

Description

Compute the structural distance between two signaling networks

Usage

computeNetD_structure(g, h, w1 = 0.45, w2 = 0.45, w3 = 0.1)

Arguments

g

a graph object of one signaling network
h

a graph object of another signaling network
w1

parameter
w2

parameter
w3

parameter

Compute signaling network similarity for any pair of signaling networks

Description

Compute signaling network similarity for any pair of signaling networks

Usage

computeNetSimilarity(
  object,
  slot.name = "netP",
  type = c("functional", "structural"),
  k = NULL,
  thresh = NULL
)

Arguments

object

CellChat object
slot.name

the slot name of object that is used to compute centrality measures of signaling networks
type

"functional","structural"
k

the number of nearest neighbors
thresh

the fraction (0 to 0.25) of interactions to be trimmed before computing network similarity

Compute signaling network similarity for any pair of datasets

Description

Compute signaling network similarity for any pair of datasets

Usage

computeNetSimilarityPairwise(
  object,
  slot.name = "netP",
  type = c("functional", "structural"),
  comparison = NULL,
  k = NULL,
  thresh = NULL
)

Arguments

object

A merged 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
k

the number of nearest neighbors
thresh

the fraction (0 to 0.25) of interactions to be trimmed before computing network similarity

Compute the region distance based on the spatial locations of each splot/cell of the spatial transcriptomics

Description

Compute the region distance based on the spatial locations of each splot/cell of the spatial transcriptomics

Usage

computeRegionDistance(
  coordinates,
  meta,
  interaction.range = NULL,
  ratio = NULL,
  tol = NULL,
  k.min = 10,
  contact.dependent = TRUE,
  contact.range = NULL,
  contact.knn.k = NULL,
  do.symmetric = TRUE
)

Arguments

coordinates

a data matrix in which each row gives the spatial locations/coordinates of each cell/spot
meta

a data frame including at least two columns named 'group' and 'samples'. 'meta$group' is a factor vector defining the regions/labels of each cell/spot. 'meta$samples' is a factor vector defining the sample labels of each dataset.
interaction.range

The maximum interaction/diffusion range of ligands. This hard threshold is used to filter out the connections between spatially distant regions
ratio

a numerical vector giving the conversion factor when converting spatial coordinates from Pixels or other units to Micrometers (i.e.,Microns).

For example, setting 'ratio = 0.18' indicates that 1 pixel equals 0.18um in the coordinates. For 10X visium, it is the ratio of the theoretical spot size (i.e., 65um) over the number of pixels that span the diameter of a theoretical spot size in the full-resolution image (i.e., 'spot.size.fullres' in the 'scalefactors_json.json' file).
tol

a numerical vector giving the tolerance factor to increase the robustness when comparing the center-to-center distance against the 'interaction.range'. This can be the half value of cell/spot size in the unit of um.

For example, for 10X visium, 'tol' can be set as '65/2'; for slide-seq, 'tol' can be set as '10/2'. If the cell/spot size is not known, we provide a function 'computeCellDistance' to compute the center-to-center distance. 'tol' can be the the half value of the minimum center-to-center distance.
k.min

the minimum number of interacting cell pairs required for defining adjacent cell groups
contact.dependent

Whether determining spatially proximal cell groups based on either the contact.range or the k-nearest neighbors (knn). By default 'contact.dependent = TRUE' when inferring contact-dependent and juxtacrine signaling (including ECM-Receptor and Cell-Cell Contact signaling classified in CellChatDB$interaction$annotation). If only focusing on 'Secreted Signaling', the 'contact.dependent' will be automatically set as FALSE except for 'contact.dependent.forced = TRUE'.
contact.range

The interaction range (Unit: microns) to restrict the contact-dependent signaling. For spatial transcriptomics in a single-cell resolution, 'contact.range' is approximately equal to the estimated cell diameter (i.e., the cell center-to-center distance), which means that contact-dependent and juxtacrine signaling can only happens when the two cells are contact to each other.

Typically, 'contact.range = 10', which is a typical human cell size. However, for low-resolution spatial data such as 10X visium, it should be the cell center-to-center distance (i.e., 'contact.range = 100' for visium data). The function 'computeCellDistance' can compute the center-to-center distance.
contact.knn.k

Number of neighbors to restrict the contact-dependent signaling within the neatest neighbors. By default, CellChat uses 'contact.range' to restrict the contact-dependent signaling; however, users can also provide a value of 'contact.knn.k', in order to determine spatially proximal cell groups based on the k-nearest neighbors (knn). For 10X visium, contact.knn.k = 6. For other spatial technologies, this value may be hard to determine because the sequenced cells/spots are usually not regularly arranged.
do.symmetric

Whether converting the adjacent matrix into symmetric one when determining spatially proximal cell groups. Default is TRUE, indicating that if adj(i,j) or adj(j,i) is zero, then both are zeros.

Value

A list including a square matrix giving the pairwise region distances and an adjacent matrix indicating physically contacting cell groups based on either the contact.range or the k-nearest neighbors

Create a new CellChat object from a data matrix, Seurat or SingleCellExperiment object

Description

Create a new CellChat object from a data matrix, Seurat or SingleCellExperiment object

Usage

createCellChat(
  object,
  meta = NULL,
  group.by = NULL,
  datatype = c("RNA", "spatial"),
  coordinates = NULL,
  spatial.factors = NULL,
  assay = NULL,
  do.sparse = T
)

Arguments

object

a normalized (NOT count) data matrix (genes by cells), Seurat or SingleCellExperiment object
meta

a data frame (rows are cells with rownames) consisting of cell information, which will be used for defining cell groups. If input is a Seurat or SingleCellExperiment object, the meta data in the object will be used
group.by

a char name of the variable in meta data, defining cell groups. If input is a data matrix and group.by is NULL, the input ‘meta' should contain a column named ’labels', If input is a Seurat or SingleCellExperiment object, USER must provide 'group.by' to define the cell groups. e.g, group.by = "ident" for Seurat object
datatype

By default datatype = "RNA"; when running CellChat on spatial imaging data, set datatype = "spatial" and input 'spatial.factors'
coordinates

a data matrix in which each row gives the spatial locations/coordinates of each cell/spot
spatial.factors

a data frame containing two distance factors 'ratio' and 'tol', which is dependent on spatial transcriptomics technologies (and specific datasets).

USER must input this data frame when datatype = "spatial". spatial.factors must contain an element named 'ratio', which is the conversion factor when converting spatial coordinates from Pixels or other units to Micrometers (i.e.,Microns). For example, setting 'ratio = 0.18' indicates that 1 pixel equals 0.18um in the coordinates,

and another element named 'tol', which is the tolerance factor to increase the robustness when comparing the center-to-center distance against the 'interaction.range'. This can be the half value of cell/spot size in the unit of um. If the cell/spot size is not known, we provide a function 'computeCellDistance' to compute the cell center-to-center distance. 'tol' can be the the half value of the minimum center-to-center distance. Of note, CellChat does not need an accurate tolerance factor, which is used for determining whether considering the cell-pair as spatially proximal if their distance is greater than 'interaction.range' but smaller than "'interaction.range' + 'tol'".
assay

Assay to use when the input is a Seurat or SingleCellExperiment object. NB: The data in the 'integrated' assay in Seurat is not suitable for CellChat analysis because it contains negative values.
do.sparse

whether use sparse format

Examples

## Not run: 
Create a CellChat object from single-cell transcriptomics data
# Input is a data matrix
## create a dataframe consisting of the cell labels
meta = data.frame(labels = cell.labels, row.names = names(cell.labels))
cellChat <- createCellChat(object = data.input, meta = meta, group.by = "labels")

# input is a Seurat object
## use the default cell identities of Seurat object
cellChat <- createCellChat(object = seurat.obj, group.by = "ident", assay = "RNA")
## use other meta information as cell groups
cellChat <- createCellChat(object = seurat.obj, group.by = "seurat.clusters")

# input is a SingleCellExperiment object
cellChat <- createCellChat(object = sce.obj, group.by = "sce.clusters")

# input is a AnnData object
sce <- zellkonverter::readH5AD(file = "adata.h5ad")
assayNames(sce) # retrieve all the available assays within sce object
counts <- assay(sce, "X") # add a new assay entry "logcounts" if not available and make sure this is the original count data matrix
library.size <- Matrix::colSums(counts)
logcounts(sce) <- log1p(Matrix::t(Matrix::t(counts)/library.size) * 10000)
meta <- as.data.frame(SingleCellExperiment::colData(sce))
cellChat <- createCellChat(object = sce, group.by = "sce.clusters")


Create a CellChat object from spatial transcriptomics data
# Input is a data matrix
cellChat <- createCellChat(object = data.input, meta = meta, group.by = "labels",
                           datatype = "spatial", coordinates = coordinates, spatial.factors = spatial.factors)

# input is a Seurat object
cellChat <- createCellChat(object = seurat.obj, group.by = "ident", assay = "SCT",
                           datatype = "spatial", spatial.factors = spatial.factors)


## End(Not run)

Dot plot

Description

The size of the dot encodes the percentage of cells within a class, while the color encodes the AverageExpression level across all cells within a class

Usage

dotPlot(
  object,
  features,
  rotation = TRUE,
  colormap = "OrRd",
  color.direction = 1,
  color.use = c("#F8766D", "#00BFC4"),
  scale = TRUE,
  col.min = -2.5,
  col.max = 2.5,
  dot.scale = 6,
  assay = "RNA",
  idents = NULL,
  group.by = NULL,
  split.by = NULL,
  legend.width = 0.5,
  angle.x = 45,
  hjust.x = 1,
  angle.y = 0,
  hjust.y = 0.5,
  show.legend = TRUE,
  ...
)

Arguments

object

seurat object
features

Features to plot (gene expression, metrics)
rotation

whether rotate the plot
colormap

RColorbrewer palette to use (check available palette using RColorBrewer::display.brewer.all()). default will use customed color palette
color.direction

Sets the order of colours in the scale. If 1, the default, colours are as output by RColorBrewer::brewer.pal(). If -1, the order of colours is reversed.
color.use

defining the color for each condition/dataset
scale

whther show x-axis text
col.min

Minimum scaled average expression threshold (everything smaller will be set to this)
col.max

Maximum scaled average expression threshold (everything larger will be set to this)
dot.scale

Scale the size of the points, similar to cex
assay

Name of assay to use, defaults to the active assay
idents

Which classes to include in the plot (default is all)
group.by

Name of one or more metadata columns to group (color) cells by (for example, orig.ident); pass 'ident' to group by identity class
split.by

Name of a metadata column to split plot by;
legend.width

legend width
angle.x

angle for x-axis text rotation
hjust.x

adjust x axis text
angle.y

angle for y-axis text rotation
hjust.y

adjust y axis text
show.legend

whether show the legend
...

Extra parameters passed to DotPlot from Seurat package

Value

ggplot2 object

compute the Shannon entropy

Description

compute the Shannon entropy

Usage

entropia(a)

Arguments

a

a numeric vector

extract the max value of the y axis

Description

extract the max value of the y axis

Usage

extract_max(p)

Arguments

p

ggplot object

Identify all the significant interactions (L-R pairs) and related signaling genes for a given signaling pathway

Description

Identify all the significant interactions (L-R pairs) and related signaling genes for a given signaling pathway

Usage

extractEnrichedLR(
  object,
  signaling,
  geneLR.return = FALSE,
  enriched.only = TRUE,
  thresh = 0.05,
  geneInfo = NULL,
  complex_input = NULL
)

Arguments

object

CellChat object
signaling

a char vector containing signaling pathway names for searching
geneLR.return

whether return the related signaling genes of enriched L-R pairs
enriched.only

whether only return the identified enriched signaling genes in the database. Default = TRUE, returning the significantly enriched signaling interactions
thresh

threshold of the p-value for determining significant interaction
geneInfo

a dataframe with gene official symbol (there should be one column named 'Symbol')
complex_input

signaling complex information from CellChatDB

Value

The returned value depends on the input argument:

When 'geneLR.return = FALSE', it returns a data frame containing the significant interactions (L-R pairs)

When 'geneLR.return = TRUE', it returns a list, the first element is a data frame containing the significant interactions (L-R pairs), and the second is a vector containing the related signaling genes of enriched L-R pairs, which can be used for examining the gene expression pattern using the function plotGeneExpression

Identify all the significant interactions (L-R pairs) and related signaling genes for a given signaling pathway

Description

Identify all the significant interactions (L-R pairs) and related signaling genes for a given signaling pathway

Usage

extractEnrichedLR_internal(
  net,
  LR,
  DB,
  signaling,
  enriched.only = TRUE,
  thresh = 0.05
)

Arguments

net, LR, DB

object@net object@LR object@DB
signaling

a char vector containing signaling pathway names for searching
enriched.only

whether only return the identified enriched signaling genes in the database. Default = TRUE, returning the significantly enriched signaling interactions
thresh

threshold of the p-value for determining significant interaction

Value

a list: list(geneLR, pairLR.name.use)

Extract the genes involved in CellChatDB

Description

Extract the genes involved in CellChatDB

Usage

extractGene(CellChatDB)

Arguments

CellChatDB

CellChatDB databse used in the analysis

Extract the gene name

Description

Extract the gene name

Usage

extractGeneSubset(geneSet, complex_input, geneIfo)

Arguments

geneSet

gene set
complex_input

complex in CellChatDB databse
geneIfo

official gene symbol

Extract the signaling gene names from ligand-receptor pairs

Description

Extract the signaling gene names from ligand-receptor pairs

Usage

extractGeneSubsetFromPair(
  pairLR,
  object = NULL,
  complex_input = NULL,
  geneInfo = NULL,
  combined = TRUE
)

Arguments

pairLR

data frame must contain columns named 'ligand' and 'receptor'
object

a CellChat object
complex_input

complex in CellChatDB databse
geneInfo

official gene symbol
combined

whether combining the ligand genes and receptor genes

Extract L-R pairs associated with a given gene set

Description

Extract L-R pairs associated with a given gene set

Usage

extractLRfromGenes(geneSet, db)

Arguments

geneSet

a vector of genes
db

one of the CellChatDB databases (e.g., CellChatDB.human, CellChatDB.mouse...)

Filter cell-cell communication if there are only few number of cells in certain cell groups or inconsistent cell-cell communication across samples

Description

Filter cell-cell communication if there are only few number of cells in certain cell groups or inconsistent cell-cell communication across samples

Usage

filterCommunication(
  object,
  min.cells = 10,
  min.samples = NULL,
  rare.keep = FALSE,
  nonFilter.keep = FALSE
)

Arguments

object

CellChat object
min.cells

The minmum number of cells required in each cell group for cell-cell communication
min.samples

The minmum number of samples required for consistent cell-cell communication across samples (that is an interaction present in at least 'min.samples' samples) when mutiple samples/replicates/batches are merged as an input for CellChat analysis.
rare.keep

Whether to keep the interactions associated with the rare populations when min.samples >= 2. When a rare population is identified in the merged samples (say 15 cells in this rare population from two samples), it is likely to filter out the interactions associated with this rare population when setting min.samples >= 2. Setting 'rare.keep = TRUE' to retain the identified interactions associated with this rare population.
nonFilter.keep

Whether to keep the non-filtered cell-cell communication in the CellChat object. This is useful for avoiding re-running 'computeCommunProb' if you want to adjust the parameters when running 'filterCommunication'.

Value

CellChat object with an updated slot net

Find the enriched signaling according to the genes (e.g.DEGs) and cell groups of interest

Description

Find the enriched signaling according to the genes (e.g.DEGs) and cell groups of interest

Usage

findEnrichedSignaling(
  object,
  features,
  idents = NULL,
  pattern = c("both", "outgoing", "incoming"),
  thresh = 0.05
)

Arguments

object

CellChat object
features

a vector giving the genes of interest
idents

a vector giving the names of cell groups of interest. If idents = NULL, it returns signaling according to the input features.
pattern

"both", "outgoing" or "incoming"
thresh

threshold of the p-value for determining significant interaction

Value

a dataframe of the cell-cell communication associated with the input features.

Examples

## Not run: 
# find all the significant outgoing signaling according to the features and cell groups of interest
df <- findEnrichedSignaling(object, features = c("CCL19", "CXCL12"), idents = c("Inflam. FIB", "COL11A1+ FIB"), pattern ="outgoing")

## End(Not run)

Compute the geometric mean

Description

Compute the geometric mean

Usage

geometricMean(x, na.rm = TRUE)

Arguments

x

a numeric vector
na.rm

whether remove na

Compute the maximum value of certain measures in the inferred cell-cell communication networks

Description

To better control the node size and edge weights of the inferred networks across different datasets, we compute the maximum number of cells per cell group and the maximum number of interactions (or interaction weights) across all datasets

Usage

getMaxWeight(
  object.list,
  slot.name = c("idents", "net"),
  attribute = c("idents", "count")
)

Arguments

object.list

List of CellChat objects
slot.name

the slot name of object that is used to compute the maximum value.

When slot.name = "idents", 'attribute' should be "idents", which will compute the maximum number of cells per cell group across all datasets

When slot.name = "net", 'attribute' can be either "count" or "weight", which will compute he maximum number of interactions (or interaction weights) across all datasets

When slot.name = "net" or "netP", 'attribute' can be a single pathway name or a ligand-receptor pair name
attribute

the attribute to compute the maximum values. 'attribute' should have the same length as 'slot.name'.

'attribute' can only be "count", "weight","count.merged","weight.merged" or a single pathway name or a ligand-receptor pair name

Value

A numeric vector

Generate ggplot2 colors

Description

Generate ggplot2 colors

Usage

ggPalette(n)

Arguments

n

number of colors to generate

Identification of major signals for specific cell groups and general communication patterns

Description

Identification of major signals for specific cell groups and general communication patterns

Usage

identifyCommunicationPatterns(
  object,
  slot.name = "netP",
  pattern = c("outgoing", "incoming"),
  k = NULL,
  k.range = seq(2, 10),
  heatmap.show = TRUE,
  color.use = NULL,
  color.heatmap = "Spectral",
  title.legend = "Contributions",
  width = 4,
  height = 6,
  font.size = 8
)

Arguments

object

CellChat object
slot.name

the slot name of object that is used to compute centrality measures of signaling networks
pattern

"outgoing" or "incoming"
k

the number of patterns
k.range

a range of the number of patterns
heatmap.show

whether showing heatmap
color.use

the character vector defining the color of each cell group
color.heatmap

a color name in brewer.pal
title.legend

the title of legend in heatmap
width

width of heatmap
height

height of heatmap
font.size

fontsize in heatmap

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

Description

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

Usage

identifyEnrichedInteractions(
  object,
  from,
  to,
  bidirection = FALSE,
  pair.only = TRUE,
  pairLR.use0 = NULL,
  thresh = 0.05
)

Arguments

object

CellChat object
from

a vector giving the index or the name of source cell groups
to

a corresponding vector giving the index or the name of target cell groups. Note: The length of 'from' and 'to' must be the same, giving the corresponding pair of cell groups for communication.
bidirection

whether show the bidirectional communication, i.e., both 'from'->'to' and 'to'->'from'.
pair.only

whether only return ligand-receptor pairs without pathway names and communication strength
pairLR.use0

ligand-receptor pairs to use; default is all the significant interactions
thresh

threshold of the p-value for determining significant interaction

Identify over-expressed signaling genes associated with each cell group

Description

USERS can use customized gene set as over-expressed signaling genes by setting '[email protected][[features.name]] <- features.sig' The Bonferroni corrected/adjusted p value can be obtained via '[email protected][[paste0(features.name, ".info")]]'. Note that by default 'features.name = "features"'

Usage

identifyOverExpressedGenes(
  object,
  data.use = NULL,
  group.by = NULL,
  idents.use = NULL,
  invert = FALSE,
  group.dataset = NULL,
  pos.dataset = NULL,
  group.DE.combined = FALSE,
  features.name = "features",
  only.pos = TRUE,
  features = NULL,
  return.object = TRUE,
  thresh.pc = 0,
  thresh.fc = 0,
  thresh.p = 0.05,
  do.DE = TRUE,
  do.fast = TRUE,
  min.cells = 10
)

Arguments

object

CellChat object
data.use

a customed data matrix. Default: data.use = NULL and the expression matrix in the slot 'data.signaling' is used
group.by

cell group information; default is 'object@idents'; otherwise it should be one of the column names of the meta slot
idents.use

a subset of cell groups used for analysis
invert

whether to invert the idents.use
group.dataset

dataset origin information in a merged CellChat object; set it as one of the column names of meta slot when identifying the highly enriched genes in one dataset for each cell group
pos.dataset

the dataset name used for identifying highly enriched genes in this dataset for each cell group
group.DE.combined

Whether to perform differential expression between conditions by ignoring cell group information. By default, group.DE.combined = FALSE, which will perform differential expression analysis between two biological conditions for each cell group; When group.DE.combined = TRUE, it will perform DE analysis by combining all cell groups together.
features.name

a char name used for storing the over-expressed signaling genes in '[email protected][[features.name]]'
only.pos

Only return positive markers
features

features used for identifying Over Expressed genes. default use all features
return.object

whether to return the object; otherwise return a data frame consisting of over-expressed signaling genes associated with each cell group
thresh.pc

Threshold of the fraction of cells expressed in one cluster, i.e., thresh.pc = 0.1
thresh.fc

Threshold of Log Fold Change, i.e., thresh.pc = 0.1
thresh.p

Threshold of p-values, i.e., thresh.pc = 0.05
do.DE

Whether to perform differential expression analysis. By default do.DE = TRUE; When do.DE = FALSE, selecting over-expressed genes that are expressed in more than 'min.cells' cells.
do.fast

If do.fast = TRUE, then perform a ultra-fast Wilcoxon test using presto package; otherwise using stats package. These two methods produce different logFC values, and the presto::wilcoxauc method gives smaller values.
min.cells

the minmum number of expressed cells required for the genes that are considered for cell-cell communication analysis

Value

A CellChat object or a data frame. If returning a CellChat object, two new elements named 'features.name' and paste0(features.name, ".info") will be added into the list '[email protected]' '[email protected][[features.name]]' is a vector consisting of the identified over-expressed signaling genes; '[email protected][[paste0(features.name, ".info")]]' is a data frame returned from the differential expression analysis

Identify over-expressed ligand-receptor interactions (pairs) within the used CellChatDB

Description

Identify over-expressed ligand-receptor interactions (pairs) within the used CellChatDB

Usage

identifyOverExpressedInteractions(
  object,
  features.name = "features",
  variable.both = TRUE,
  features = NULL,
  return.object = TRUE
)

Arguments

object

CellChat object
features.name

a char name used for assess the results in '[email protected][[features.name]]'
variable.both

variable.both = TRUE will require that both ligand and receptor from one pair are over-expressed;

variable.both = FALSE will only require that either ligand or receptor from one pair is over-expressed, leading to more over-expressed ligand-receptor interactions (pairs) for further analysis.
features

a vector of features to use. default use all over-expressed genes in '[email protected][[features.name]]'
return.object

whether returning a CellChat object. If FALSE, it will return a data frame containing the over-expressed ligand-receptor pairs

Value

A CellChat object or a data frame. If returning a CellChat object, a new element named 'LRsig' will be added into the list 'object@LR'

Identify over-expressed ligands and (complex) receptors associated with each cell group

Description

This function identifies the over-expressed ligands and (complex) receptors based on the identified signaling genes from 'identifyOverExpressedGenes'.

Usage

identifyOverExpressedLigandReceptor(
  object,
  features.name = "features",
  features = NULL,
  return.object = TRUE
)

Arguments

object

CellChat object
features.name

a char name used for storing the over-expressed ligands and receptors in '[email protected][[paste0(features.name, ".LR")]]'
features

a vector of features to use. default use all over-expressed genes in '[email protected][[features.name]]'
return.object

whether returning a CellChat object. If FALSE, it will return a data frame containing over-expressed ligands and (complex) receptors associated with each cell group

Value

A CellChat object or a data frame. If returning a CellChat object, a new element named paste0(features.name, ".LR") will be added into the list '[email protected]'

Update a CellChat object by lifting up the cell groups to the same cell labels across all datasets

Description

This function is useful when comparing inferred communications across different datasets with different cellular compositions

Usage

liftCellChat(object, group.new = NULL)

Arguments

object

A single or merged CellChat object
group.new

A char vector giving the cell labels to lift up. The order of cell labels in the vector will be used for setting the new cell identity.

If the input is a merged CellChat object and group.new = NULL, it will use the cell labels from one dataset with the maximum number of cell groups

If the input is a single CellChat object, 'group.new' must be defined.

Value

a updated CellChat object

Merge CellChat objects

Description

Merge CellChat objects

Usage

mergeCellChat(
  object.list,
  add.names = NULL,
  merge.data = FALSE,
  cell.prefix = FALSE
)

Arguments

object.list

A list of multiple CellChat objects
add.names

A vector containing the name of each dataset
merge.data

whether merging the data for ALL genes. Default only merges the data of signaling genes
cell.prefix

whether prefix cell names

Compute the number of interactions/interaction strength between cell types based on their associated cell subpopulations

Description

Compute the number of interactions/interaction strength between cell types based on their associated cell subpopulations

Usage

mergeInteractions(object, group.merged)

Arguments

object

CellChat object
group.merged

a factor defining the group for merging different clusters/subpopulations

Value

An updated slot 'net' by adding three elements:

'count.merged': the number of interactions between cell types (i.e., merged cell groups)

'weight.merged': interaction strength between cell types (i.e., merged cell groups)

'group.merged' the defined group for merging different clusters/subpopulations

modified vlnplot

Description

modified vlnplot

Usage

modify_vlnplot(
  object,
  features,
  idents = NULL,
  split.by = NULL,
  cols = NULL,
  show.text.y = TRUE,
  line.size = NULL,
  pt.size = 0,
  plot.margin = margin(0, 0, 0, 0, "cm"),
  ...
)

Arguments

object

Seurat object
features

Features to plot (gene expression, metrics)
idents

Which classes to include in the plot (default is all)
split.by

Name of a metadata column to split plot by;
cols

defining the color for each cell group
show.text.y

whther show y-axis text
line.size

line width in the violin plot
pt.size

size of the dots
plot.margin

adjust the white space between each plot
...

pass any arguments to VlnPlot in Seurat

generate circle symbol

Description

generate circle symbol

Usage

mycircle(coords, v = NULL, params)

Arguments

coords

coordinates of points
v

vetex
params

parameters

Compute the network centrality scores allowing identification of dominant senders, receivers, mediators and influencers in all inferred communication networks

Description

NB: This function was previously named as 'netAnalysis_signalingRole'. The previous function 'netVisual_signalingRole' is now named as 'netAnalysis_signalingRole_network'.

Usage

netAnalysis_computeCentrality(
  object = NULL,
  slot.name = "netP",
  net = NULL,
  net.name = NULL,
  thresh = 0.05
)

Arguments

object

CellChat object; If object = NULL, USER must provide 'net'
slot.name

the slot name of object that is used to compute centrality measures of signaling networks. Setting slot.name = "netP" to compute the network centrality scores at the level of signaling pathways, and setting slot.name = "net" to compute the network centrality scores at the level of ligand-receptor pairs
net

compute the centrality measures on a specific signaling network given by a 2 or 3 dimemsional array net
net.name

a character vector giving the name of signaling networks
thresh

threshold of the p-value for determining significant interaction

Compute and visualize the contribution of each ligand-receptor pair in the overall signaling pathways

Description

Compute and visualize the contribution of each ligand-receptor pair in the overall signaling pathways

Usage

netAnalysis_contribution(
  object,
  signaling,
  signaling.name = NULL,
  sources.use = NULL,
  targets.use = NULL,
  width = 0.1,
  vertex.receiver = NULL,
  thresh = 0.05,
  return.data = FALSE,
  x.rotation = 0,
  title = "Contribution of each L-R pair",
  font.size = 10,
  font.size.title = 10
)

Arguments

object

CellChat object
signaling

a signaling pathway name
signaling.name

alternative signaling pathway name to show on the 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.
width

the width of individual bar
vertex.receiver

a numeric vector giving the index of the cell groups as targets in the first hierarchy plot
thresh

threshold of the p-value for determining significant interaction
return.data

whether return the data.frame consisting of the predicted L-R pairs and their contribution
x.rotation

rotation of x-label
title

the title of the plot
font.size

font size of the text
font.size.title

font size of the title

2D visualization of differential signaling roles (dominant senders (sources) or receivers (targets) ) of each cell group when comparing mutiple datasets

Description

This scatter plot shows the differential signaling roles (dominant senders (sources) or receivers (targets) in a 2D space.

x-axis and y-axis are respectively the differential outgoing or incoming communication probability associated with each cell group. Dot colors indicate different cell groups. Dot shapes indicate different categories of cell groups if 'group“ is defined.

Positive values indicate the increase in the second dataset while negative values indicate the increase in the first dataset

Usage

netAnalysis_diff_signalingRole_scatter(
  object,
  color.use = NULL,
  comparison = c(1, 2),
  signaling = NULL,
  signaling.exclude = NULL,
  idents.exclude = NULL,
  slot.name = "netP",
  group = NULL,
  dot.size = 2.5,
  point.shape = c(21, 22, 24, 23, 25, 8, 3),
  label.size = 3,
  dot.alpha = 0.6,
  x.measure = "outdeg",
  y.measure = "indeg",
  xlabel = "Outgoing interaction strength",
  ylabel = "Incoming interaction strength",
  title = NULL,
  font.size = 10,
  font.size.title = 10,
  do.label = T,
  show.legend = T,
  show.axes = T
)

Arguments

object

A merged CellChat object of a list of CellChat objects
color.use

defining the color for each cell group
comparison

an index vector giving the two datasets for comparison
signaling

a char vector containing signaling pathway names. signaling = NULL: Signaling role analysis on the aggregated cell-cell communication network from all signaling pathways
signaling.exclude

signaling pathways to exclude
idents.exclude

cell groups to exclude. This is useful when zooming into the small changes
slot.name

the slot name of object that is used to compute centrality measures of signaling networks
group

a vector to categorize the cell groups, e.g., categorize the cell groups into two major categories: immune cells and fibroblasts
dot.size

the size of the symbol
point.shape

point shape when group is not NULL
label.size

font size of the text
dot.alpha

transparency
x.measure

The measure used as x-axis. This measure should be one of 'names(slot(object, slot.name)$centr[[1]])' computed from 'netAnalysis_computeCentrality'

Default = "outdeg" is the weighted outgoing links (i.e., Outgoing interaction strength). If setting as "outdeg_unweighted", it represents the total number of outgoing signaling.
y.measure

The measure used as y-axis. This measure should be one of 'names(slot(object, slot.name)$centr[[1]])' computed from 'netAnalysis_computeCentrality'

Default = "indeg" is the weighted incoming links (i.e., Incoming interaction strength). If setting as "indeg_unweighted", it represents the total number of incoming signaling.
xlabel

label of x-axis
ylabel

label of y-axis
title

main title of the plot
font.size

font size of the text
font.size.title

font size of the title
do.label

label the each point
show.legend

whether show the legend
show.axes

whether show the axes

Value

ggplot object

Dot plots showing the associations of latent patterns with cell groups and ligand-receptor pairs or signaling pathways

Description

Using a contribution score of each cell group to each signaling pathway computed by multiplying W by H obtained from 'identifyCommunicationPatterns', we constructed a dot plot in which the dot size is proportion to the contribution score to show association between cell group and their enriched signaling pathways.

Usage

netAnalysis_dot(
  object,
  slot.name = "netP",
  pattern = c("outgoing", "incoming"),
  cutoff = NULL,
  color.use = NULL,
  pathway.show = NULL,
  group.show = NULL,
  shape = 21,
  dot.size = c(1, 3),
  dot.alpha = 1,
  main.title = NULL,
  font.size = 10,
  font.size.title = 12
)

Arguments

object

CellChat object
slot.name

the slot name of object that is used to compute centrality measures of signaling networks
pattern

"outgoing" or "incoming"
cutoff

the threshold for filtering out weak links. Default is 1/R where R is the number of latent patterns. We set the elements in W and H to be zero if they are less than 'cutoff'.
color.use

the character vector defining the color of each cell group
pathway.show

the character vector defining the signaling to show
group.show

the character vector defining the cell group to show
shape

the shape of the symbol: 21 for circle and 22 for square
dot.size

a range defining the size of the symbol
dot.alpha

transparency
main.title

the title of plot
font.size

font size of the text
font.size.title

font size of the title

River plot showing the associations of latent patterns with cell groups and ligand-receptor pairs or signaling pathways

Description

River (alluvial) plot shows the correspondence between the inferred latent patterns and cell groups as well as ligand-receptor pairs or signaling pathways.

Usage

netAnalysis_river(
  object,
  slot.name = "netP",
  pattern = c("outgoing", "incoming"),
  cutoff = 0.5,
  sources.use = NULL,
  targets.use = NULL,
  signaling = NULL,
  color.use = NULL,
  color.use.pattern = NULL,
  color.use.signaling = "grey50",
  do.order = FALSE,
  main.title = NULL,
  font.size = 2.5,
  font.size.title = 12
)

Arguments

object

CellChat object
slot.name

the slot name of object: “netP” or “net”. Use “netP” to analyze cell-cell communication at the level of signaling pathways, and “net” to analyze cell-cell communication at the level of ligand-receptor pairs.
pattern

"outgoing" or "incoming"
cutoff

the threshold for filtering out weak links
sources.use

a vector giving the index or the name of source cell groups of interest
targets.use

a vector giving the index or the name of target cell groups of interest
signaling

a character vector giving the name of signaling pathways of interest
color.use

the character vector defining the color of each cell group
color.use.pattern

the character vector defining the color of each pattern
color.use.signaling

the character vector defining the color of each signaling
do.order

whether reorder the cell groups or signaling according to their similarity
main.title

the title of plot
font.size

font size of the text
font.size.title

font size of the title

Details

The thickness of the flow indicates the contribution of the cell group or signaling pathway to each latent pattern. The height of each pattern is proportional to the number of its associated cell groups or signaling pathways.

Outgoing patterns reveal how the sender cells coordinate with each other as well as how they coordinate with certain signaling pathways to drive communication.

Incoming patterns show how the target cells coordinate with each other as well as how they coordinate with certain signaling pathways to respond to incoming signaling.

2D visualization of differential outgoing and incoming signaling associated with one cell group

Description

Positive values indicate the increase in the second dataset while negative values indicate the increase in the first dataset

Usage

netAnalysis_signalingChanges_scatter(
  object,
  idents.use,
  color.use = c("grey10", "#F8766D", "#00BFC4"),
  comparison = c(1, 2),
  signaling = NULL,
  signaling.label = NULL,
  top.label = 1,
  signaling.exclude = NULL,
  xlims = NULL,
  ylims = NULL,
  slot.name = "netP",
  dot.size = 2.5,
  point.shape = c(21, 22, 24, 23),
  label.size = 3,
  dot.alpha = 0.6,
  x.measure = "outdeg",
  y.measure = "indeg",
  xlabel = "Differential outgoing interaction strength",
  ylabel = "Differential incoming interaction strength",
  title = NULL,
  font.size = 10,
  font.size.title = 10,
  do.label = T,
  show.legend = T,
  show.axes = T
)

Arguments

object

A merged CellChat object of a list of CellChat objects
idents.use

the cell group names of interest. Should be one of 'levels(object@idents$joint)'
color.use

a vector with three elements: the first is for coloring shared pathways, the second is for specific pathways in the first dataset, and the third is for specific pathways in the second dataset
comparison

an index vector giving the two datasets for comparison
signaling

a char vector containing signaling pathway names. signaling = NULL: Signaling role analysis on the aggregated cell-cell communication network from all signaling pathways
signaling.label

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

the fraction of signaling pathways to label
signaling.exclude

signaling pathways to exclude when plotting
xlims, ylims

set x-Axis and y-Axis Limits for zoom into the plot. e.g., xlims = c(-0.05, 0.1), ylims = c(-0.01, 0.035)
slot.name

the slot name of object
dot.size

the size of the symbol
point.shape

point shape
label.size

font size of the text
dot.alpha

transparency
x.measure

The measure used as x-axis. This measure should be one of 'names(slot(object, slot.name)$centr[[1]])' computed from 'netAnalysis_computeCentrality'

Default = "outdeg" is the weighted outgoing links (i.e., Outgoing interaction strength). If setting as "outdeg_unweighted", it represents the total number of outgoing signaling.
y.measure

The measure used as y-axis. This measure should be one of 'names(slot(object, slot.name)$centr[[1]])' computed from 'netAnalysis_computeCentrality'

Default = "indeg" is the weighted incoming links (i.e., Incoming interaction strength). If setting as "indeg_unweighted", it represents the total number of incoming signaling.
xlabel

label of x-axis
ylabel

label of y-axis
title

main title of the plot
font.size

font size of the text
font.size.title

font size of the title
do.label

label the each point
show.legend

whether show the legend
show.axes

whether show the axes

Value

ggplot object

Heatmap showing the contribution of signals (signaling pathways or ligand-receptor pairs) to cell groups in terms of outgoing or incoming signaling

Description

In this heatmap, colobar represents the relative signaling strength of a signaling pathway across cell groups (NB: values are row-scaled). The top colored bar plot shows the total signaling strength of a cell group by summarizing all signaling pathways displayed in the heatmap. The right grey bar plot shows the total signaling strength of a signaling pathway by summarizing all cell groups displayed in the heatmap.

Usage

netAnalysis_signalingRole_heatmap(
  object,
  signaling = NULL,
  pattern = c("outgoing", "incoming", "all"),
  slot.name = "netP",
  color.use = NULL,
  color.heatmap = "BuGn",
  title = NULL,
  width = 10,
  height = 8,
  ylim.top = NULL,
  ylim.right = NULL,
  font.size = 8,
  font.size.title = 10,
  cluster.rows = FALSE,
  cluster.cols = FALSE
)

Arguments

object

CellChat object
signaling

a character vector giving the names of signaling networks of interest
pattern

this parameter can be set as "outgoing", "incoming" or "all". When pattern = "all", CellChat aggregates the outgoing and incoming signaling strength together;
slot.name

the slot name of object that is used to examine the signaling patterns at the level of signaling pathways (slot.name = "netP") or ligand-receptor pairs (slot.name = "net");
color.use

the character vector defining the color of each cell group
color.heatmap

a color name in brewer.pal
title

title name
width

width of heatmap
height

height of heatmap
ylim.top

set the range of the top barplot (e.g., ylim.top = c(0, 4))
ylim.right

set the range of the right barplot (e.g., ylim.right = c(0, 5))
font.size

fontsize in heatmap
font.size.title

font size of the title
cluster.rows

whether cluster rows
cluster.cols

whether cluster columns

Heatmap showing the centrality scores/importance of cell groups as senders, receivers, mediators and influencers in a single intercellular communication network

Description

Heatmap showing the centrality scores/importance of cell groups as senders, receivers, mediators and influencers in a single intercellular communication network

Usage

netAnalysis_signalingRole_network(
  object,
  signaling,
  slot.name = "netP",
  measure = c("outdeg", "indeg", "flowbet", "info"),
  measure.name = c("Sender", "Receiver", "Mediator", "Influencer"),
  color.use = NULL,
  color.heatmap = "BuGn",
  width = 6.5,
  height = 1.4,
  font.size = 8,
  font.size.title = 10,
  cluster.rows = FALSE,
  cluster.cols = FALSE
)

Arguments

object

CellChat object
signaling

a character vector giving the name of signaling networks
slot.name

the slot name of object that is used to compute centrality measures of signaling networks
measure

centrality measures to show
measure.name

the names of centrality measures to show
color.use

the character vector defining the color of each cell group
color.heatmap

a color name in brewer.pal
width

width of heatmap
height

height of heatmap
font.size

fontsize in heatmap
font.size.title

font size of the title
cluster.rows

whether cluster rows
cluster.cols

whether cluster columns

2D visualization of dominant senders (sources) and receivers (targets)

Description

This scatter plot shows the dominant senders (sources) and receivers (targets) in a 2D space. x-axis and y-axis are respectively the total outgoing or incoming communication probability associated with each cell group. Dot size is proportional to the number of inferred links (both outgoing and incoming) associated with each cell group. Dot colors indicate different cell groups. Dot shapes indicate different categories of cell groups if 'group“ is defined.

Usage

netAnalysis_signalingRole_scatter(
  object,
  signaling = NULL,
  color.use = NULL,
  slot.name = "netP",
  group = NULL,
  weight.MinMax = NULL,
  dot.size = c(2, 6),
  point.shape = c(21, 22, 24, 23, 25, 8, 3),
  label.size = 3,
  dot.alpha = 0.6,
  x.measure = "outdeg",
  y.measure = "indeg",
  xlabel = "Outgoing interaction strength",
  ylabel = "Incoming interaction strength",
  title = NULL,
  font.size = 10,
  font.size.title = 10,
  do.label = T,
  show.legend = T,
  show.axes = T
)

Arguments

object

CellChat object
signaling

a char vector containing signaling pathway names. signaling = NULL: signaling role analysis on the aggregated cell-cell communication network from all signaling pathways
color.use

defining the color for each cell group
slot.name

the slot name of object that is used to compute centrality measures of signaling networks
group

a vector to categorize the cell groups, e.g., categorize the cell groups into two major categories: immune cells and fibroblasts
weight.MinMax

the Minmum/maximum weight, which is useful to control the dot size when comparing multiple datasets
dot.size

a range defining the size of the symbol
point.shape

point shape when group is not NULL
label.size

font size of the text
dot.alpha

transparency
x.measure

The measure used as x-axis. This measure should be one of 'names(slot(object, slot.name)$centr[[1]])' computed from 'netAnalysis_computeCentrality'

Default = "outdeg" is the weighted outgoing links (i.e., outgoing interaction strength). If setting as "outdeg_unweighted", it represents the total number of outgoing signaling.
y.measure

The measure used as y-axis. This measure should be one of 'names(slot(object, slot.name)$centr[[1]])' computed from 'netAnalysis_computeCentrality'

Default = "indeg" is the weighted incoming links (i.e., incoming interaction strength). If setting as "indeg_unweighted", it represents the total number of incoming signaling.
xlabel

label of x-axis
ylabel

label of y-axis
title

main title of the plot
font.size

font size of the text
font.size.title

font size of the title
do.label

label the each point
show.legend

whether show the legend
show.axes

whether show the axes

Value

ggplot object

Classification learning of the signaling networks

Description

Classification learning of the signaling networks

Usage

netClustering(
  object,
  slot.name = "netP",
  type = c("functional", "structural"),
  comparison = NULL,
  k = NULL,
  methods = "kmeans",
  do.plot = TRUE,
  fig.id = NULL,
  do.parallel = TRUE,
  nCores = 4,
  k.eigen = NULL
)

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. No need to define for a single dataset. Default are all datasets when object is a merged object
k

the number of signaling groups when running kmeans
methods

the methods for clustering: "kmeans" or "spectral"
do.plot

whether showing the eigenspectrum for inferring number of clusters; Default will save the plot
fig.id

add a unique figure id when saving the plot
do.parallel

whether doing parallel when inferring the number of signaling groups when running kmeans
nCores

number of workers when doing parallel
k.eigen

the number of eigenvalues used when doing spectral clustering

Manifold learning of the signaling networks based on their similarity

Description

Manifold learning of the signaling networks based on their similarity

Usage

netEmbedding(
  object,
  slot.name = "netP",
  type = c("functional", "structural"),
  comparison = NULL,
  pathway.remove = NULL,
  umap.method = c("umap-learn", "uwot"),
  n_neighbors = NULL,
  min_dist = 0.3,
  ...
)

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. No need to define for a single dataset. Default are all datasets when object is a merged object
pathway.remove

a range of the number of patterns
umap.method

UMAP implementation to run.

Can be umap-learn: Run the python umap-learn package; uwot: Runs umap via the uwot R package; If umap.method = "uwot", please make sure you have installed the 'uwot' (https://github.com/jlmelville/uwot)
n_neighbors

the number of nearest neighbors in running umap
min_dist

This controls how tightly the embedding is allowed compress points together. Larger values ensure embedded points are moreevenly distributed, while smaller values allow the algorithm to optimise more accurately with regard to local structure. Sensible values are in the range 0.001 to 0.5.
...

Parameters passing to umap

Mapping the differential expressed genes (DEG) information onto the inferred cell-cell communications

Description

This function returns a data frame consisting of all the inferred cell-cell communications with mapped DEG information

Usage

netMappingDEG(object, features.name, variable.all = TRUE, thresh = 0.05)

Arguments

object

CellChat object
features.name

a char name used for extracting the DEG in '[email protected][[features.name]]'
variable.all

variable.all = TRUE will compute the c("pvalues", "logFC", "pct.1", "pct.2") for a ligand/receptor complex using the mean value of its all subunits, that is requiring all subunits of the complex are differential expressed; variable.all = FALSE will compute the minimum value of "pvalues" and maximum value of c("logFC", "pct.1", "pct.2") among the subunits, that is only requiring that any one of the subunits of the complex is differential expressed.
thresh

threshold of the p-value for determining significant interaction

Value

a data frame of the inferred cell-cell communications, consisting of source, target, interaction_name, pathway_name, prob and other CellChatDB information as well as DEG information

Visualize the inferred cell-cell communication network

Description

Automatically save plots in the current working directory.

Usage

netVisual(
  object,
  signaling,
  signaling.name = NULL,
  color.use = NULL,
  vertex.receiver = NULL,
  sources.use = NULL,
  targets.use = NULL,
  top = 1,
  remove.isolate = FALSE,
  vertex.weight = 1,
  vertex.weight.max = NULL,
  vertex.size.max = NULL,
  weight.scale = TRUE,
  edge.weight.max.individual = NULL,
  edge.weight.max.aggregate = NULL,
  edge.width.max = 8,
  layout = c("circle", "hierarchy", "chord", "spatial"),
  height = 5,
  thresh = 0.05,
  pt.title = 12,
  title.space = 6,
  vertex.label.cex = 0.8,
  from = NULL,
  to = NULL,
  bidirection = NULL,
  vertex.size = NULL,
  out.format = c("svg", "png"),
  sample.use = NULL,
  alpha.image = 0.15,
  point.size = 1.5,
  group = NULL,
  cell.order = NULL,
  small.gap = 1,
  big.gap = 10,
  scale = FALSE,
  reduce = -1,
  show.legend = FALSE,
  legend.pos.x = 20,
  legend.pos.y = 20,
  nCol = NULL,
  ...
)

Arguments

object

CellChat object
signaling

a signaling pathway name
signaling.name

alternative signaling pathway name to show on the plot
color.use

the character vector defining the color of each cell group
vertex.receiver

a numeric vector giving the index of the cell groups as targets in the first hierarchy 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.
top

the fraction of interactions to show (0 < top <= 1)
remove.isolate

whether remove the isolate nodes in the communication network
vertex.weight

The weight of vertex: either a scale value or a vector

Default is a scale value being 1, indicating all vertex is plotted in the same size;

Set 'vertex.weight' as a vector to plot vertex in different size; setting 'vertex.weight = NULL' will have vertex with different size that are portional to the number of cells in each cell group.
vertex.weight.max

the maximum weight of vertex; defualt = max(vertex.weight)
vertex.size.max

the maximum vertex size for visualization
weight.scale

whether scale the edge weight
edge.weight.max.individual

the maximum weight of edge when plotting the individual L-R netwrok; defualt = max(net)
edge.weight.max.aggregate

the maximum weight of edge when plotting the aggregated signaling pathway network
edge.width.max

The maximum edge width for visualization
layout

"hierarchy", "circle" or "chord"
height

height of plot
thresh

threshold of the p-value for determining significant interaction
pt.title

font size of the text
title.space

the space between the title and plot
vertex.label.cex

The label size of vertex in the network
from, to, bidirection

Deprecated. Use 'sources.use','targets.use'
vertex.size

Deprecated. Use 'vertex.weight'

Parameters below are set for "chord" diagram. Please also check the function 'netVisual_chord_cell' for more parameters.
out.format

the format of output figures: svg, png and pdf

Parameters below are set for "spatial" diagram. Please also check the function 'netVisual_spatial' for more parameters.
sample.use

the sample used for visualization, which should be the element in 'object@meta$samples'.
alpha.image

the transparency of individual spots
point.size

the size of spots
group

A named group labels for making multiple-group Chord diagrams. The sector names should be used as the names in the vector. The order of group controls the sector orders and if group is set as a factor, the order of levels controls the order of groups.
cell.order

a char vector defining the cell type orders (sector orders)
small.gap

Small gap between sectors.
big.gap

Gap between the different sets of sectors, which are defined in the 'group' parameter
scale

scale each sector to same width; default = FALSE; however, it is set to be TRUE when remove.isolate = TRUE
reduce

if the ratio of the width of certain grid compared to the whole circle is less than this value, the grid is removed on the plot. Set it to value less than zero if you want to keep all tiny grid.
show.legend

whether show the figure legend
legend.pos.x, legend.pos.y

adjust the legend position
nCol

number of columns when displaying the network mediated by ligand-receptor using "circle" or "chord"
...

other parameters (e.g.,vertex.label.cex, vertex.label.color, alpha.edge, label.edge, edge.label.color, edge.label.cex, edge.curved, text.x, text.y) passing to 'netVisual_hierarchy1','netVisual_hierarchy2','netVisual_circle'. NB: some parameters might be not supported

Visualize the inferred signaling network of signaling pathways by aggregating all L-R pairs

Description

Visualize the inferred signaling network of signaling pathways by aggregating all L-R pairs

Usage

netVisual_aggregate(
  object,
  signaling,
  signaling.name = NULL,
  color.use = NULL,
  thresh = 0.05,
  vertex.receiver = NULL,
  sources.use = NULL,
  targets.use = NULL,
  idents.use = NULL,
  top = 1,
  remove.isolate = FALSE,
  vertex.weight = 1,
  vertex.weight.max = NULL,
  vertex.size.max = NULL,
  weight.scale = TRUE,
  edge.weight.max = NULL,
  edge.width.max = 8,
  layout = c("circle", "hierarchy", "chord", "spatial"),
  pt.title = 12,
  title.space = 6,
  vertex.label.cex = 0.8,
  sample.use = NULL,
  alpha.image = 0.15,
  point.size = 1.5,
  group = NULL,
  cell.order = NULL,
  small.gap = 1,
  big.gap = 10,
  scale = FALSE,
  reduce = -1,
  show.legend = FALSE,
  legend.pos.x = 20,
  legend.pos.y = 20,
  ...
)

Arguments

object

CellChat object
signaling

a signaling pathway name
signaling.name

alternative signaling pathway name to show on the plot
color.use

the character vector defining the color of each cell group
thresh

threshold of the p-value for determining significant interaction
vertex.receiver

a numeric vector giving the index of the cell groups as targets in the first hierarchy 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.
idents.use

a vector giving the index or the name of cell groups of interest.
top

the fraction of interactions to show
remove.isolate

whether remove the isolate nodes in the communication network
vertex.weight

The weight of vertex: either a scale value or a vector

Default is a scale value being 1, indicating all vertex is plotted in the same size;

Set 'vertex.weight' as a vector to plot vertex in different size; setting 'vertex.weight = NULL' will have vertex with different size that are portional to the number of cells in each cell group.
vertex.weight.max

the maximum weight of vertex; defualt = max(vertex.weight)
vertex.size.max

the maximum vertex size for visualization
weight.scale

whether scale the edge weight
edge.weight.max

the maximum weight of edge; defualt = max(net)
edge.width.max

The maximum edge width for visualization
layout

"hierarchy", "circle", "chord" or "spatial"
pt.title

font size of the text
title.space

the space between the title and plot
vertex.label.cex

The label size of vertex in the network

Parameters below are set for "spatial" diagram. Please also check the function 'netVisual_spatial' for more parameters.
sample.use

the sample used for visualization, which should be the element in 'object@meta$samples'.
alpha.image

the transparency of individual spots
point.size

the size of spots

Parameters below are set for "chord" diagram. Please also check the function 'netVisual_chord_cell' for more parameters.
group

A named group labels for making multiple-group Chord diagrams. The sector names should be used as the names in the vector. The order of group controls the sector orders and if group is set as a factor, the order of levels controls the order of groups.
cell.order

a char vector defining the cell type orders (sector orders)
small.gap

Small gap between sectors.
big.gap

Gap between the different sets of sectors, which are defined in the 'group' parameter
scale

scale each sector to same width; default = FALSE; however, it is set to be TRUE when remove.isolate = TRUE
reduce

if the ratio of the width of certain grid compared to the whole circle is less than this value, the grid is removed on the plot. Set it to value less than zero if you want to keep all tiny grid.
show.legend

whether show the figure legend
legend.pos.x, legend.pos.y

adjust the legend position
...

other parameters (e.g.,vertex.label.cex, vertex.label.color, alpha.edge, label.edge, edge.label.color, edge.label.cex, edge.curved, text.x,text.y) passing to 'netVisual_hierarchy1','netVisual_hierarchy2','netVisual_circle','netVisual_spatial'. NB: some parameters might be not supported

Value

an object of class "recordedplot" or ggplot

Visualization of (differential) number of interactions

Description

Visualization of (differential) number of interactions

Usage

netVisual_barplot(
  object,
  comparison = c(1, 2),
  measure = c("count", "weight"),
  sources.use = NULL,
  targets.use = NULL,
  invert.source = FALSE,
  invert.target = FALSE,
  signaling = NULL,
  slot.name = c("netP", "net"),
  color.use = NULL,
  title.name = NULL,
  x.lab.rot = FALSE,
  ...
)

Arguments

object

A merged CellChat object or a single CellChat object
comparison

a numerical vector giving the datasets for comparison in object.list; e.g., comparison = c(1,2)
measure

"count" or "weight". "count": comparing the number of interactions; "weight": comparing the total interaction weights (strength)
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.
invert.source, invert.target

retain the complementary set
signaling

a character vector giving the name of signaling networks in a single CellChat object
slot.name

the slot name of object. Set is to be "netP" if input signaling is a pathway name; Set is to be "net" if input signaling is a ligand-receptor pair
color.use

the character vector defining the color of each cell group
title.name

the name of the title
x.lab.rot

do rotation for the x-ticklabels
...

Parameters passing to 'barplot_internal'

Value

an object of ggplot

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,
  dot.size.min = NULL,
  dot.size.max = 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 to remove the entire empty columns, i.e., communication between certain cell groups
max.dataset

a scale, keeping the communications with highest probability in max.dataset (i.e., certrain condition)
min.dataset

a scale, keeping 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 to add vertical line when doing comparison analysis for the merged object
line.size

size of vertical line if added
color.text.use

whether to 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
dot.size.min, dot.size.max

Size of smallest and largest points
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 to show legend
grid.on, color.grid

whether to add grid
angle.x, vjust.x, hjust.x

parameters for adjusting the rotation of xtick labels
return.data

whether to 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)

Chord diagram for visualizing cell-cell communication for a signaling pathway

Description

Names of cell states will be displayed in this chord diagram

Usage

netVisual_chord_cell(
  object,
  signaling = NULL,
  net = NULL,
  slot.name = "netP",
  color.use = NULL,
  group = NULL,
  cell.order = NULL,
  sources.use = NULL,
  targets.use = NULL,
  lab.cex = 0.8,
  small.gap = 1,
  big.gap = 10,
  annotationTrackHeight = c(0.03),
  remove.isolate = FALSE,
  link.visible = TRUE,
  scale = FALSE,
  directional = 1,
  link.target.prop = TRUE,
  reduce = -1,
  transparency = 0.4,
  link.border = NA,
  title.name = NULL,
  show.legend = FALSE,
  legend.pos.x = 20,
  legend.pos.y = 20,
  nCol = NULL,
  thresh = 0.05,
  ...
)

Arguments

object

CellChat object
signaling

a character vector giving the name of signaling networks
net

a weighted matrix or a data frame with three columns defining the cell-cell communication network
slot.name

the slot name of object: slot.name = "net" when visualizing cell-cell communication network per each ligand-receptor pair associated with a given signaling pathway; slot.name = "netP" when visualizing cell-cell communication network at the level of signaling pathways
color.use

colors for the cell groups
group

A named group labels for making multiple-group Chord diagrams. The sector names should be used as the names in the vector. The order of group controls the sector orders and if group is set as a factor, the order of levels controls the order of groups.
cell.order

a char vector defining the cell type orders (sector orders)
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.
lab.cex

font size for the text
small.gap

Small gap between sectors.
big.gap

Gap between the different sets of sectors, which are defined in the 'group' parameter
annotationTrackHeight

annotationTrack Height
remove.isolate

whether remove sectors without any links
link.visible

whether plot the link. The value is logical, if it is set to FALSE, the corresponding link will not plotted, but the space is still ocuppied. The format is a matrix with names or a data frame with three columns
scale

scale each sector to same width; default = FALSE; however, it is set to be TRUE when remove.isolate = TRUE
directional

Whether links have directions. 1 means the direction is from the first column in df to the second column, -1 is the reverse, 0 is no direction, and 2 for two directional.
link.target.prop

If the Chord diagram is directional, for each source sector, whether to draw bars that shows the proportion of target sectors.
reduce

if the ratio of the width of certain grid compared to the whole circle is less than this value, the grid is removed on the plot. Set it to value less than zero if you want to keep all tiny grid.
transparency

Transparency of link colors
link.border

border for links, single scalar or a matrix with names or a data frame with three columns
title.name

title name
show.legend

whether show the figure legend
legend.pos.x, legend.pos.y

adjust the legend position
nCol

number of columns when displaying the figures
thresh

threshold of the p-value for determining significant interaction when visualizing links at the level of ligands/receptors;
...

other parameters passing to chordDiagram

Value

an object of class "recordedplot"

Chord diagram for visualizing cell-cell communication from a weighted adjacency matrix or a data frame

Description

Names of cell states/groups will be displayed in this chord diagram

Usage

netVisual_chord_cell_internal(
  net,
  color.use = NULL,
  group = NULL,
  cell.order = NULL,
  sources.use = NULL,
  targets.use = NULL,
  lab.cex = 0.8,
  small.gap = 1,
  big.gap = 10,
  annotationTrackHeight = c(0.03),
  remove.isolate = FALSE,
  link.visible = TRUE,
  scale = FALSE,
  directional = 1,
  link.target.prop = TRUE,
  reduce = -1,
  transparency = 0.4,
  link.border = NA,
  title.name = NULL,
  show.legend = FALSE,
  legend.pos.x = 20,
  legend.pos.y = 20,
  ...
)

Arguments

net

a weighted matrix or a data frame with three columns defining the cell-cell communication network
color.use

colors for the cell groups
group

A named group labels for making multiple-group Chord diagrams. The sector names should be used as the names in the vector. The order of group controls the sector orders and if group is set as a factor, the order of levels controls the order of groups.
cell.order

a char vector defining the cell type orders (sector orders)
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.
lab.cex

font size for the text
small.gap

Small gap between sectors.
big.gap

Gap between the different sets of sectors, which are defined in the 'group' parameter
annotationTrackHeight

annotationTrack Height
remove.isolate

whether remove sectors without any links
link.visible

whether plot the link. The value is logical, if it is set to FALSE, the corresponding link will not plotted, but the space is still ocuppied. The format is a matrix with names or a data frame with three columns
scale

scale each sector to same width; default = FALSE; however, it is set to be TRUE when remove.isolate = TRUE
directional

Whether links have directions. 1 means the direction is from the first column in df to the second column, -1 is the reverse, 0 is no direction, and 2 for two directional.
link.target.prop

If the Chord diagram is directional, for each source sector, whether to draw bars that shows the proportion of target sectors.
reduce

if the ratio of the width of certain grid compared to the whole circle is less than this value, the grid is removed on the plot. Set it to value less than zero if you want to keep all tiny grid.
transparency

Transparency of link colors
link.border

border for links, single scalar or a matrix with names or a data frame with three columns
title.name

title name of the plot
show.legend

whether show the figure legend
legend.pos.x, legend.pos.y

adjust the legend position
...

other parameters passing to chordDiagram

Value

an object of class "recordedplot"

Chord diagram for visualizing cell-cell communication for a set of ligands/receptors or signaling pathways

Description

Names of ligands/receptors or signaling pathways will be displayed in this chord diagram

Usage

netVisual_chord_gene(
  object,
  slot.name = "net",
  color.use = NULL,
  signaling = NULL,
  pairLR.use = NULL,
  net = NULL,
  sources.use = NULL,
  targets.use = NULL,
  lab.cex = 0.8,
  small.gap = 1,
  big.gap = 10,
  annotationTrackHeight = c(0.03),
  link.visible = TRUE,
  scale = FALSE,
  directional = 1,
  link.target.prop = TRUE,
  reduce = -1,
  transparency = 0.4,
  link.border = NA,
  title.name = NULL,
  legend.pos.x = 20,
  legend.pos.y = 20,
  show.legend = TRUE,
  thresh = 0.05,
  ...
)

Arguments

object

CellChat object
slot.name

the slot name of object: slot.name = "net" when visualizing links at the level of ligands/receptors; slot.name = "netP" when visualizing links at the level of signaling pathways
color.use

colors for the cell groups
signaling

a character vector giving the name of signaling networks
pairLR.use

a data frame consisting of one column named either "interaction_name" or "pathway_name", defining the interactions of interest
net

A data frame consisting of the interactions of interest. net should have at least three columns: "source","target" and "interaction_name" when visualizing links at the level of ligands/receptors; "source","target" and "pathway_name" when visualizing links at the level of signaling pathway; "interaction_name" and "pathway_name" must be the matched names in CellChatDB$interaction.
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.
lab.cex

font size for the text
small.gap

Small gap between sectors.
big.gap

Gap between the different sets of sectors, which are defined in the 'group' parameter
annotationTrackHeight

annotationTrack Height
link.visible

whether plot the link. The value is logical, if it is set to FALSE, the corresponding link will not plotted, but the space is still ocuppied. The format is a matrix with names or a data frame with three columns
scale

scale each sector to same width; default = FALSE; however, it is set to be TRUE when remove.isolate = TRUE
directional

Whether links have directions. 1 means the direction is from the first column in df to the second column, -1 is the reverse, 0 is no direction, and 2 for two directional.
link.target.prop

If the Chord diagram is directional, for each source sector, whether to draw bars that shows the proportion of target sectors.
reduce

if the ratio of the width of certain grid compared to the whole circle is less than this value, the grid is removed on the plot. Set it to value less than zero if you want to keep all tiny grid.
transparency

Transparency of link colors
link.border

border for links, single scalar or a matrix with names or a data frame with three columns
title.name

title name of the plot
legend.pos.x, legend.pos.y

adjust the legend position
show.legend

whether show the figure legend
thresh

threshold of the p-value for determining significant interaction when visualizing links at the level of ligands/receptors;
...

other parameters to chordDiagram

Value

an object of class "recordedplot"

Circle plot of cell-cell communication network

Description

The width of edges represent the strength of the communication.

Usage

netVisual_circle(
  net,
  color.use = NULL,
  title.name = NULL,
  sources.use = NULL,
  targets.use = NULL,
  idents.use = NULL,
  remove.isolate = FALSE,
  top = 1,
  weight.scale = FALSE,
  vertex.weight = 20,
  vertex.weight.max = NULL,
  vertex.size.max = NULL,
  vertex.label.cex = 1,
  vertex.label.color = "black",
  edge.weight.max = NULL,
  edge.width.max = 8,
  alpha.edge = 0.6,
  label.edge = FALSE,
  edge.label.color = "black",
  edge.label.cex = 0.8,
  edge.curved = 0.2,
  shape = "circle",
  layout = in_circle(),
  margin = 0.2,
  vertex.size = NULL,
  arrow.width = 1,
  arrow.size = 0.2,
  text.x = 0,
  text.y = 1.5
)

Arguments

net

A weighted matrix representing the connections
color.use

Colors represent different cell groups
title.name

the name of the title
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.
idents.use

a vector giving the index or the name of cell groups of interest.
remove.isolate

whether remove the isolate nodes in the communication network
top

the fraction of interactions to show
weight.scale

whether scale the weight
vertex.weight

The weight of vertex: either a scale value or a vector
vertex.weight.max

the maximum weight of vertex; defualt = max(vertex.weight)
vertex.size.max

the maximum vertex size for visualization
vertex.label.cex

The label size of vertex
vertex.label.color

The color of label for vertex
edge.weight.max

the maximum weight of edge; defualt = max(net)
edge.width.max

The maximum edge width for visualization
alpha.edge

the transprency of edge
label.edge

Whether or not shows the label of edges
edge.label.color

The color for single arrow
edge.label.cex

The size of label for arrows
edge.curved

Specifies whether to draw curved edges, or not. This can be a logical or a numeric vector or scalar. First the vector is replicated to have the same length as the number of edges in the graph. Then it is interpreted for each edge separately. A numeric value specifies the curvature of the edge; zero curvature means straight edges, negative values means the edge bends clockwise, positive values the opposite. TRUE means curvature 0.5, FALSE means curvature zero
shape

The shape of the vertex, currently “circle”, “square”, “csquare”, “rectangle”, “crectangle”, “vrectangle”, “pie” (see vertex.shape.pie), ‘sphere’, and “none” are supported, and only by the plot.igraph command. “none” does not draw the vertices at all, although vertex label are plotted (if given). See shapes for details about vertex shapes and vertex.shape.pie for using pie charts as vertices.
layout

The layout specification. It must be a call to a layout specification function.
margin

The amount of empty space below, over, at the left and right of the plot, it is a numeric vector of length four. Usually values between 0 and 0.5 are meaningful, but negative values are also possible, that will make the plot zoom in to a part of the graph. If it is shorter than four then it is recycled.
vertex.size

Deprecated. Use 'vertex.weight'
arrow.width

The width of arrows
arrow.size

the size of arrow
text.x, text.y

the x- and y-coordinates to add the text

Value

an object of class "recordedplot"

Circle plot showing differential cell-cell communication network between two datasets

Description

The width of edges represent the relative number of interactions or interaction strength. Red (or blue) colored edges represent increased (or decreased) signaling in the second dataset compared to the first one.

Usage

netVisual_diffInteraction(
  object,
  comparison = c(1, 2),
  measure = c("count", "weight", "count.merged", "weight.merged"),
  color.use = NULL,
  color.edge = c("#b2182b", "#2166ac"),
  title.name = NULL,
  sources.use = NULL,
  targets.use = NULL,
  remove.isolate = FALSE,
  top = 1,
  weight.scale = FALSE,
  vertex.weight = 20,
  vertex.weight.max = NULL,
  vertex.size.max = 15,
  vertex.label.cex = 1,
  vertex.label.color = "black",
  edge.weight.max = NULL,
  edge.width.max = 8,
  alpha.edge = 0.6,
  label.edge = FALSE,
  edge.label.color = "black",
  edge.label.cex = 0.8,
  edge.curved = 0.2,
  shape = "circle",
  layout = in_circle(),
  margin = 0.2,
  arrow.width = 1,
  arrow.size = 0.2
)

Arguments

object

A merged CellChat objects
comparison

a numerical vector giving the datasets for comparison in object.list; e.g., comparison = c(1,2)
measure

"count" or "weight". "count": comparing the number of interactions; "weight": comparing the total interaction weights (strength)
color.use

Colors represent different cell groups
color.edge

Colors for indicating whether the signaling is increased ('color.edge[1]') or decreased ('color.edge[2]')
title.name

the name of the title
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.
remove.isolate

whether remove the isolate nodes in the communication network
top

the fraction of interactions to show
weight.scale

whether scale the weight
vertex.weight

The weight of vertex: either a scale value or a vector
vertex.weight.max

the maximum weight of vertex; defualt = max(vertex.weight)
vertex.size.max

the maximum vertex size for visualization
vertex.label.cex

The label size of vertex
vertex.label.color

The color of label for vertex
edge.weight.max

the maximum weight of edge; defualt = max(net)
edge.width.max

The maximum edge width for visualization
alpha.edge

the transprency of edge
label.edge

Whether or not shows the label of edges
edge.label.color

The color for single arrow
edge.label.cex

The size of label for arrows
edge.curved

Specifies whether to draw curved edges, or not. This can be a logical or a numeric vector or scalar. First the vector is replicated to have the same length as the number of edges in the graph. Then it is interpreted for each edge separately. A numeric value specifies the curvature of the edge; zero curvature means straight edges, negative values means the edge bends clockwise, positive values the opposite. TRUE means curvature 0.5, FALSE means curvature zero
shape

The shape of the vertex, currently “circle”, “square”, “csquare”, “rectangle”, “crectangle”, “vrectangle”, “pie” (see vertex.shape.pie), ‘sphere’, and “none” are supported, and only by the plot.igraph command. “none” does not draw the vertices at all, although vertex label are plotted (if given). See shapes for details about vertex shapes and vertex.shape.pie for using pie charts as vertices.
layout

The layout specification. It must be a call to a layout specification function.
margin

The amount of empty space below, over, at the left and right of the plot, it is a numeric vector of length four. Usually values between 0 and 0.5 are meaningful, but negative values are also possible, that will make the plot zoom in to a part of the graph. If it is shorter than four then it is recycled.
arrow.width

The width of arrows
arrow.size

the size of arrow

Value

an object of class "recordedplot"

2D visualization of the learned manifold of signaling networks

Description

2D visualization of the learned manifold of signaling networks

Usage

netVisual_embedding(
  object,
  slot.name = "netP",
  type = c("functional", "structural"),
  color.use = NULL,
  pathway.labeled = NULL,
  top.label = 1,
  pathway.remove = NULL,
  pathway.remove.show = TRUE,
  dot.size = c(2, 6),
  label.size = 2,
  dot.alpha = 0.5,
  xlabel = "Dim 1",
  ylabel = "Dim 2",
  title = NULL,
  font.size = 10,
  font.size.title = 12,
  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"
color.use

defining the color for each cell group
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
font.size

font size of the text
font.size.title

font size of the title
do.label

label the each point
show.legend

whether show the legend
show.axes

whether show the axes

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

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

Zoom into the 2D visualization of the learned manifold learning of the signaling networks

Description

Zoom into the 2D visualization of the learned manifold learning of the signaling networks

Usage

netVisual_embeddingZoomIn(
  object,
  slot.name = "netP",
  type = c("functional", "structural"),
  color.use = NULL,
  pathway.remove = NULL,
  nCol = 1,
  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"
color.use

defining the color for each cell group
pathway.remove

a character vector defining the signaling to remove
nCol

the number of columns of the plot
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

Visualization of network using heatmap

Description

This heatmap can be used to 1) show differential number of interactions or interaction strength in the cell-cell communication network between two datasets; 2) the number of interactions or interaction strength in a single dataset; 3) the inferred cell-cell communication network in a single dataset, defined by 'signaling'. Please see @Details below for detailed explanations of this heatmap plot.

Usage

netVisual_heatmap(
  object,
  comparison = c(1, 2),
  measure = c("count", "weight"),
  signaling = NULL,
  slot.name = c("netP", "net"),
  color.use = NULL,
  color.heatmap = NULL,
  title.name = NULL,
  width = NULL,
  height = NULL,
  ylim.top = NULL,
  ylim.right = NULL,
  font.size = 8,
  font.size.title = 10,
  cluster.rows = FALSE,
  cluster.cols = FALSE,
  sources.use = NULL,
  targets.use = NULL,
  remove.isolate = FALSE,
  row.show = NULL,
  col.show = NULL
)

Arguments

object

A merged CellChat object or a single CellChat object
comparison

a numerical vector giving the datasets for comparison in object.list; e.g., comparison = c(1,2)
measure

"count" or "weight". "count": comparing the number of interactions; "weight": comparing the total interaction weights (strength)
signaling

a character vector giving the name of signaling networks in a single CellChat object
slot.name

the slot name of object. Set is to be "netP" if input signaling is a pathway name; Set is to be "net" if input signaling is a ligand-receptor pair
color.use

the character vector defining the color of each cell group
color.heatmap

A vector of two colors corresponding to max/min values, or a color name in brewer.pal only when the data in the heatmap do not contain negative values. By default, color.heatmap = c('#2166ac','#b2182b') when taking a merged CellChat object as input; color.heatmap = "Reds" when taking a single CellChat object as input.
title.name

the name of the title
width

width of heatmap
height

height of heatmap
ylim.top

set the range of the top barplot (e.g., ylim.top = c(0, 4))
ylim.right

set the range of the right barplot (e.g., ylim.right = c(0, 5))
font.size

fontsize in heatmap
font.size.title

font size of the title
cluster.rows

whether cluster rows
cluster.cols

whether cluster columns
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.
remove.isolate

whether remove the isolate nodes in the communication network
row.show, col.show

a vector giving the index or the name of row or columns to show in the heatmap

Details

When show differential number of interactions or interaction strength in the cell-cell communication network between two datasets, the width of edges represent the relative number of interactions or interaction strength. Red (or blue) colored edges represent increased (or decreased) signaling in the second dataset compared to the first one.

The top colored bar plot represents the sum of absolute values displayed in each column of the heatmap. The right colored bar plot represents the sum of absolute values in each row.

Value

an object of ComplexHeatmap

Hierarchy plot of cell-cell communications sending to cell groups in vertex.receiver

Description

The width of edges represent the strength of the communication.

Usage

netVisual_hierarchy1(
  net,
  vertex.receiver,
  color.use = NULL,
  title.name = NULL,
  sources.use = NULL,
  targets.use = NULL,
  remove.isolate = FALSE,
  top = 1,
  weight.scale = FALSE,
  vertex.weight = 20,
  vertex.weight.max = NULL,
  vertex.size.max = NULL,
  edge.weight.max = NULL,
  edge.width.max = 8,
  alpha.edge = 0.6,
  label.dist = 2.8,
  space.v = 1.5,
  space.h = 1.6,
  shape = NULL,
  label.edge = FALSE,
  edge.curved = 0,
  margin = 0.2,
  vertex.label.cex = 0.6,
  vertex.label.color = "black",
  arrow.width = 1,
  arrow.size = 0.2,
  edge.label.color = "black",
  edge.label.cex = 0.5,
  vertex.size = NULL
)

Arguments

net

a weighted matrix defining the signaling network
vertex.receiver

a numeric vector giving the index of the cell groups as targets in the first hierarchy plot
color.use

the character vector defining the color of each cell group
title.name

alternative signaling pathway name to show on the 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.
remove.isolate

whether remove the isolate nodes in the communication network
top

the fraction of interactions to show
weight.scale

whether rescale the edge weights
vertex.weight

The weight of vertex: either a scale value or a vector
vertex.weight.max

the maximum weight of vertex; defualt = max(vertex.weight)
vertex.size.max

the maximum vertex size for visualization
edge.weight.max

the maximum weight of edge; defualt = max(net)
edge.width.max

The maximum edge width for visualization
alpha.edge

the transprency of edge
label.dist

the distance between labels and dot position
space.v

the space between different columns in the plot
space.h

the space between different rows in the plot
shape

The shape of the vertex, currently “circle”, “square”, “csquare”, “rectangle”, “crectangle”, “vrectangle”, “pie” (see vertex.shape.pie), ‘sphere’, and “none” are supported, and only by the plot.igraph command. “none” does not draw the vertices at all, although vertex label are plotted (if given). See shapes for details about vertex shapes and vertex.shape.pie for using pie charts as vertices.
label.edge

whether label edge
edge.curved

Specifies whether to draw curved edges, or not. This can be a logical or a numeric vector or scalar. First the vector is replicated to have the same length as the number of edges in the graph. Then it is interpreted for each edge separately. A numeric value specifies the curvature of the edge; zero curvature means straight edges, negative values means the edge bends clockwise, positive values the opposite. TRUE means curvature 0.5, FALSE means curvature zero
margin

The amount of empty space below, over, at the left and right of the plot, it is a numeric vector of length four. Usually values between 0 and 0.5 are meaningful, but negative values are also possible, that will make the plot zoom in to a part of the graph. If it is shorter than four then it is recycled.
vertex.label.cex

The label size of vertex
vertex.label.color

The color of label for vertex
arrow.width

The width of arrows
arrow.size

the size of arrow
edge.label.color

The color for single arrow
edge.label.cex

The size of label for arrows
vertex.size

Deprecated. Use 'vertex.weight'

Value

an object of class "recordedplot"

Hierarchy plot of cell-cell communication sending to cell groups not in vertex.receiver

Description

This function loads the significant interactions as a weighted matrix, and colors represent different types of cells as a structure. The width of edges represent the strength of the communication.

Usage

netVisual_hierarchy2(
  net,
  vertex.receiver,
  color.use = NULL,
  title.name = NULL,
  sources.use = NULL,
  targets.use = NULL,
  remove.isolate = FALSE,
  top = 1,
  weight.scale = FALSE,
  vertex.weight = 20,
  vertex.weight.max = NULL,
  vertex.size.max = NULL,
  edge.weight.max = NULL,
  edge.width.max = 8,
  alpha.edge = 0.6,
  label.dist = 2.8,
  space.v = 1.5,
  space.h = 1.6,
  shape = NULL,
  label.edge = FALSE,
  edge.curved = 0,
  margin = 0.2,
  vertex.label.cex = 0.6,
  vertex.label.color = "black",
  arrow.width = 1,
  arrow.size = 0.2,
  edge.label.color = "black",
  edge.label.cex = 0.5,
  vertex.size = NULL
)

Arguments

net

a weighted matrix defining the signaling network
vertex.receiver

a numeric vector giving the index of the cell groups as targets in the first hierarchy plot
color.use

the character vector defining the color of each cell group
title.name

alternative signaling pathway name to show on the 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.
remove.isolate

whether remove the isolate nodes in the communication network
top

the fraction of interactions to show
weight.scale

whether rescale the edge weights
vertex.weight

The weight of vertex: either a scale value or a vector
vertex.weight.max

the maximum weight of vertex; defualt = max(vertex.weight)
vertex.size.max

the maximum vertex size for visualization
edge.weight.max

the maximum weight of edge; defualt = max(net)
edge.width.max

The maximum edge width for visualization
alpha.edge

the transprency of edge
label.dist

the distance between labels and dot position
space.v

the space between different columns in the plot
space.h

the space between different rows in the plot
shape

The shape of the vertex, currently “circle”, “square”, “csquare”, “rectangle”, “crectangle”, “vrectangle”, “pie” (see vertex.shape.pie), ‘sphere’, and “none” are supported, and only by the plot.igraph command. “none” does not draw the vertices at all, although vertex label are plotted (if given). See shapes for details about vertex shapes and vertex.shape.pie for using pie charts as vertices.
label.edge

Whether or not shows the label of edges (number of connections between different cell types)
edge.curved

Specifies whether to draw curved edges, or not. This can be a logical or a numeric vector or scalar. First the vector is replicated to have the same length as the number of edges in the graph. Then it is interpreted for each edge separately. A numeric value specifies the curvature of the edge; zero curvature means straight edges, negative values means the edge bends clockwise, positive values the opposite. TRUE means curvature 0.5, FALSE means curvature zero
margin

The amount of empty space below, over, at the left and right of the plot, it is a numeric vector of length four. Usually values between 0 and 0.5 are meaningful, but negative values are also possible, that will make the plot zoom in to a part of the graph. If it is shorter than four then it is recycled.
vertex.label.cex

The label size of vertex
vertex.label.color

The color of label for vertex
arrow.width

The width of arrows
arrow.size

the size of arrow
edge.label.color

The color for single arrow
edge.label.cex

The size of label for arrows
vertex.size

Deprecated. Use 'vertex.weight'

Value

an object of class "recordedplot"

Visualize the inferred signaling network of individual L-R pairs

Description

Visualize the inferred signaling network of individual L-R pairs

Usage

netVisual_individual(
  object,
  signaling,
  signaling.name = NULL,
  pairLR.use = NULL,
  color.use = NULL,
  vertex.receiver = NULL,
  sources.use = NULL,
  targets.use = NULL,
  top = 1,
  remove.isolate = FALSE,
  vertex.weight = 1,
  vertex.weight.max = NULL,
  vertex.size.max = NULL,
  vertex.label.cex = 0.8,
  weight.scale = TRUE,
  edge.weight.max = NULL,
  edge.width.max = 8,
  graphics.init = TRUE,
  layout = c("circle", "hierarchy", "chord", "spatial"),
  height = 5,
  thresh = 0.05,
  alpha.image = 0.15,
  point.size = 1.5,
  group = NULL,
  cell.order = NULL,
  small.gap = 1,
  big.gap = 10,
  scale = FALSE,
  reduce = -1,
  show.legend = FALSE,
  legend.pos.x = 20,
  legend.pos.y = 20,
  nCol = NULL,
  ...
)

Arguments

object

CellChat object
signaling

a signaling pathway name
signaling.name

alternative signaling pathway name to show on the plot
pairLR.use

a char vector or a data frame consisting of one column named "interaction_name", defining the L-R pairs of interest
color.use

the character vector defining the color of each cell group
vertex.receiver

a numeric vector giving the index of the cell groups as targets in the first hierarchy 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.
top

the fraction of interactions to show
remove.isolate

whether remove the isolate nodes in the communication network
vertex.weight

The weight of vertex: either a scale value or a vector.

Default is a scale value being 1, indicating all vertex is plotted in the same size;

Set 'vertex.weight' as a vector to plot vertex in different size; setting 'vertex.weight = NULL' will have vertex with different size that are portional to the number of cells in each cell group.
vertex.weight.max

the maximum weight of vertex; defualt = max(vertex.weight)
vertex.size.max

the maximum vertex size for visualization
vertex.label.cex

The label size of vertex in the network
weight.scale

whether scale the edge weight
edge.weight.max

the maximum weight of edge; defualt = max(net)
edge.width.max

The maximum edge width for visualization
graphics.init

whether do graphics initiation using par(...). If graphics.init=FALSE, USERS can use par() in a more fexible way
layout

"hierarchy", "circle" or "chord"
height

height of plot
thresh

threshold of the p-value for determining significant interaction Parameters below are set for "spatial" diagram. Please also check the function 'netVisual_spatial' for more parameters.
alpha.image

the transparency of individual spots
point.size

the size of spots

Parameters below are set for "chord" diagram. Please also check the function 'netVisual_chord_cell' for more parameters.
group

A named group labels for making multiple-group Chord diagrams. The sector names should be used as the names in the vector. The order of group controls the sector orders and if group is set as a factor, the order of levels controls the order of groups.
cell.order

a char vector defining the cell type orders (sector orders)
small.gap

Small gap between sectors.
big.gap

Gap between the different sets of sectors, which are defined in the 'group' parameter
scale

scale each sector to same width; default = FALSE; however, it is set to be TRUE when remove.isolate = TRUE
reduce

if the ratio of the width of certain grid compared to the whole circle is less than this value, the grid is removed on the plot. Set it to value less than zero if you want to keep all tiny grid.
show.legend

whether show the figure legend
legend.pos.x, legend.pos.y

adjust the legend position
nCol

number of columns when displaying the figures using "circle" or "chord"
...

other parameters (e.g.,vertex.label.cex, vertex.label.color, alpha.edge, label.edge, edge.label.color, edge.label.cex, edge.curved, text.x, text.y) passing to 'netVisual_hierarchy1','netVisual_hierarchy2','netVisual_circle'. NB: some parameters might be not supported

Value

an object of class "recordedplot"

Spatial plot of cell-cell communication network

Description

Autocrine interactions are omitted on this plot. Group centroids may be not accurate for some data due to complex geometry. The width of edges represent the strength of the communication.

Usage

netVisual_spatial(
  net,
  coordinates,
  meta,
  sample.use = NULL,
  color.use = NULL,
  title.name = NULL,
  sources.use = NULL,
  targets.use = NULL,
  idents.use = NULL,
  remove.isolate = FALSE,
  remove.loop = TRUE,
  top = 1,
  weight.scale = FALSE,
  vertex.weight = 20,
  vertex.weight.max = NULL,
  vertex.size.max = NULL,
  vertex.label.cex = 5,
  vertex.label.color = "black",
  edge.weight.max = NULL,
  edge.width.max = 8,
  edge.curved = 0.2,
  alpha.edge = 0.6,
  arrow.angle = 5,
  arrow.size = 0.2,
  alpha.image = 0.15,
  point.size = 1.5,
  legend.size = 5
)

Arguments

net

A weighted matrix representing the connections
coordinates

a data matrix in which each row gives the spatial locations/coordinates of each cell/spot
meta

a data frame with at least two columns named 'labels' and 'samples'. 'meta$labels' is a vector giving the group label of each cell/spot. 'meta$samples' is a factor vector defining the sample labels of each dataset. The length should be the same as the number of rows in 'coordinates'.
sample.use

the sample used for visualization, which should be the element in 'meta$samples'.
color.use

Colors represent different cell groups
title.name

the name of the title
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.
idents.use

a vector giving the index or the name of cell groups of interest.
remove.isolate

whether remove the isolate nodes in the communication network
remove.loop

whether remove the self-loop in the communication network. Default: TRUE
top

the fraction of interactions to show
weight.scale

whether scale the weight
vertex.weight

The weight of vertex: either a scale value or a vector
vertex.weight.max

the maximum weight of vertex; defualt = max(vertex.weight)
vertex.size.max

the maximum vertex size for visualization
vertex.label.cex

The label size of vertex
vertex.label.color

The color of label for vertex
edge.weight.max

the maximum weight of edge; defualt = max(net)
edge.width.max

The maximum edge width for visualization
edge.curved

Specifies whether to draw curved edges, or not. This can be a logical or a numeric vector or scalar. First the vector is replicated to have the same length as the number of edges in the graph. Then it is interpreted for each edge separately. A numeric value specifies the curvature of the edge; zero curvature means straight edges, negative values means the edge bends clockwise, positive values the opposite. TRUE means curvature 0.5, FALSE means curvature zero
alpha.edge

the transprency of edge
arrow.angle

The width of arrows
arrow.size

the size of arrow
alpha.image

the transparency of individual spots
point.size

the size of spots
legend.size

the size of legend

Value

an object of ggplot

compute nnd

Description

compute nnd

Usage

nnd(g)

Arguments

g

a graph objecct

compute the node distance matrix

Description

compute the node distance matrix

Usage

node_distance(g)

Arguments

g

a graph objecct

Normalize data using a scaling factor

Description

Normalize data using a scaling factor

Usage

normalizeData(data.raw, scale.factor = 10000, do.log = TRUE, do.sparse = TRUE)

Arguments

data.raw

input raw data
scale.factor

the scaling factor used for each cell
do.log

whether to do log transformation with pseudocount 1
do.sparse

whether to use sparse format

Plot pie chart

Description

Plot pie chart

Usage

pieChart(df, label.size = 2.5, color.use = NULL, title = "")

Arguments

df

a dataframe
label.size

a character
color.use

the name of the variable in CellChatDB interaction_input
title

the title of plot

A Seurat wrapper function for plotting gene expression using violin plot, dot plot or bar plot

Description

This function create a Seurat object from an input CellChat object, and then plot gene expression distribution using a modified violin plot or dot plot based on Seurat's function or a bar plot. Please check StackedVlnPlot,dotPlot and barPlotfor detailed description of the arguments.

Usage

plotGeneExpression(
  object,
  features = NULL,
  signaling = NULL,
  enriched.only = TRUE,
  type = c("violin", "dot", "bar"),
  color.use = NULL,
  group.by = NULL,
  ...
)

Arguments

object

CellChat object
features

Features to plot gene expression
signaling

a char vector containing signaling pathway names for searching
enriched.only

whether only return the identified enriched signaling genes in the database. Default = TRUE, returning the significantly enriched signaling interactions
type

violin plot or dot plot
color.use

defining the color for each cell group
group.by

Name of one metadata columns to group (color) cells. Default is the defined cell groups in CellChat object
...

other arguments passing to either VlnPlot or DotPlot from Seurat package

Details

USER can extract the signaling genes related to the inferred L-R pairs or signaling pathway using extractEnrichedLR, and then plot gene expression using Seurat package.

Human Protein-Protein interactions

Description

An adjacency matrix of the high confidence known experimental interactions between human proteins on STRINGdb.

Usage

PPI.human

Format

A square matrix where A_ij=1 if protein i interacts with protein j

Source

http://www.string-db.org/

Mouse Protein-Protein interactions

Description

An adjacency matrix of the high confidence known experimental interactions between mouse proteins on STRINGdb.

Usage

PPI.mouse

Format

A square matrix where A_ij=1 if protein i interacts with protein j

Source

http://www.string-db.org/

Preprocessing multi-omics data and preparing the L-R database

Description

Preprocessing multi-omics data and preparing the L-R database

Usage

preProcMultiomics(data.list, db, cutoff = 0.5, do.sparse = TRUE)

Arguments

data.list

a list consisting of multi-omics data (e.g., RNA & ADT)
db

one of the CellChatDB databases: CellChatDB.human, CellChatDB.mouse, CellChatDB.zebrafish
cutoff

the cutoff value of low protein expression
do.sparse

whether to use sparse format

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,
  paired.test = TRUE,
  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 Wilcoxon test to determine whether there is significant difference between two datasets. Default = FALSE
paired.test

a logical indicating whether you want a paired test. Paired test is applicable to compare two datasets with the same cellular compositions.
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

Rank ligand-receptor interactions for any pair of two cell groups

Description

Rank ligand-receptor interactions for any pair of two cell groups

Usage

rankNetPairwise(object, LR.use = NULL)

Arguments

object

CellChat object
LR.use

ligand-receptor interactions used in inferring communication network

Rank the similarity of the shared signaling pathways based on their joint manifold learning

Description

Rank the similarity of the shared signaling pathways based on their joint manifold learning

Usage

rankSimilarity(
  object,
  slot.name = "netP",
  type = c("functional", "structural"),
  comparison1 = NULL,
  comparison2 = c(1, 2),
  x.rotation = 90,
  title = NULL,
  color.use = NULL,
  bar.w = NULL,
  font.size = 8
)

Arguments

object

CellChat object
slot.name

the slot name of object that is used to compute centrality measures of signaling networks
type

"functional","structural"
comparison1

a numerical vector giving the datasets for comparison. This should be the same as 'comparison' in 'computeNetSimilarityPairwise'
comparison2

a numerical vector with two elements giving the datasets for comparison.

If there are more than 2 datasets defined in 'comparison1', 'comparison2' can be defined to indicate which two datasets used for computing the distance. e.g., comparison2 = c(1,3) indicates the first and third datasets defined in 'comparison1' will be used for comparison.
x.rotation

rotation of x-labels
title

main title of the plot
color.use

defining the color
bar.w

the width of bar plot
font.size

font size

Generate a Shiny App for interactive exploration of CellChat's outputs

Description

Generate a Shiny App for interactive exploration of CellChat's outputs

Usage

runCellChatApp(object, ...)

Arguments

object

CellChat object
...

Other parameters of 'shinyApp' function from shiny R package

Value

A Shiny app object on the basis of one CellChat object

Dimension reduction using PCA

Description

Dimension reduction using PCA

Usage

runPCA(data.use, do.fast = T, dimPC = 50, seed.use = 42, weight.by.var = T)

Arguments

data.use

input data (samples in rows, features in columns)
do.fast

whether do fast PCA
dimPC

the number of components to keep
seed.use

set a seed
weight.by.var

whether use weighted pc.scores

Run UMAP

Description

Run UMAP

Usage

runUMAP(
  data.use,
  n_neighbors = 30L,
  n_components = 2L,
  metric = "correlation",
  n_epochs = NULL,
  learning_rate = 1,
  min_dist = 0.3,
  spread = 1,
  set_op_mix_ratio = 1,
  local_connectivity = 1L,
  repulsion_strength = 1,
  negative_sample_rate = 5,
  a = NULL,
  b = NULL,
  seed.use = 42L,
  metric_kwds = NULL,
  angular_rp_forest = FALSE,
  verbose = FALSE
)

Arguments

data.use

input data matrix
n_neighbors

This determines the number of neighboring points used in local approximations of manifold structure. Larger values will result in more global structure being preserved at the loss of detailed local structure. In general this parameter should often be in the range 5 to 50.
n_components

The dimension of the space to embed into.
metric

This determines the choice of metric used to measure distance in the input space.
n_epochs

the number of training epochs to be used in optimizing the low dimensional embedding. Larger values result in more accurate embeddings. If NULL is specified, a value will be selected based on the size of the input dataset (200 for large datasets, 500 for small).
learning_rate

The initial learning rate for the embedding optimization.
min_dist

This controls how tightly the embedding is allowed compress points together. Larger values ensure embedded points are moreevenly distributed, while smaller values allow the algorithm to optimise more accurately with regard to local structure. Sensible values are in the range 0.001 to 0.5.
spread

he effective scale of embedded points. In combination with min.dist this determines how clustered/clumped the embedded points are.
set_op_mix_ratio

Interpolate between (fuzzy) union and intersection as the set operation used to combine local fuzzy simplicial sets to obtain a global fuzzy simplicial sets.
local_connectivity

The local connectivity required - i.e. the number of nearest neighbors that should be assumed to be connected at a local level. The higher this value the more connected the manifold becomes locally. In practice this should be not more than the local intrinsic dimension of the manifold.
repulsion_strength

Weighting applied to negative samples in low dimensional embedding optimization. Values higher than one will result in greater weight being given to negative samples.
negative_sample_rate

The number of negative samples to select per positive sample in the optimization process. Increasing this value will result in greater repulsive force being applied, greater optimization cost, but slightly more accuracy.
a

More specific parameters controlling the embedding. If NULL, these values are set automatically as determined by min. dist and spread.
b

More specific parameters controlling the embedding. If NULL, these values are set automatically as determined by min. dist and spread.
seed.use

Set a random seed. By default, sets the seed to 42.
metric_kwds, angular_rp_forest, verbose

other parameters used in UMAP

Scale the data

Description

Scale the data

Usage

scaleData(data.use, do.center = T)

Arguments

data.use

input data
do.center

whether center the values

Scale a data matrix

Description

Scale a data matrix

Usage

scaleMat(x, scale, na.rm = TRUE)

Arguments

x

data matrix
scale

the method to scale the data
na.rm

whether remove na

Generate colors from a customed color palette

Description

Generate colors from a customed color palette

Usage

scPalette(n)

Arguments

n

number of colors

Value

A color palette for plotting

Subset the ligand-receptor interactions for given specific signals in CellChatDB

Description

Subset the ligand-receptor interactions for given specific signals in CellChatDB

Usage

searchPair(
  signaling = c(),
  pairLR.use,
  key = c("pathway_name", "ligand"),
  matching.exact = FALSE,
  pair.only = TRUE
)

Arguments

signaling

a character vector
pairLR.use

a dataframe containing ligand-receptor interactions
key

the keyword to match
matching.exact

whether perform exact matching
pair.only

whether only return ligand-receptor pairs without cofactors

Select the number of the patterns for running 'identifyCommunicationPatterns'

Description

We infer the number of patterns based on two metrics that have been implemented in the NMF R package, including Cophenetic and Silhouette. Both metrics measure the stability for a particular number of patterns based on a hierarchical clustering of the consensus matrix. For a range of the number of patterns, a suitable number of patterns is the one at which Cophenetic and Silhouette values begin to drop suddenly.

Usage

selectK(
  object,
  slot.name = "netP",
  pattern = c("outgoing", "incoming"),
  title.name = NULL,
  do.facet = TRUE,
  k.range = seq(2, 10),
  nrun = 30,
  seed.use = 10
)

Arguments

object

CellChat object
slot.name

the slot name of object that is used to compute centrality measures of signaling networks
pattern

"outgoing" or "incoming"
title.name

title of plot
do.facet

whether use facet plot showing the two measures
k.range

a range of the number of patterns
nrun

number of runs when performing NMF
seed.use

seed when performing NMF

Value

a ggplot object

Set the default identity of cells

Description

Set the default identity of cells

Usage

setIdent(object, ident.use = NULL, levels = NULL, display.warning = TRUE)

Arguments

object

CellChat object
ident.use

the name of the variable in object.meta;
levels

set the levels of factor
display.warning

whether display the warning message

show method for CellChat

Description

show method for CellChat

Usage

## S4 method for signature 'CellChat'
show(object)

Arguments

object

object
CellChat

object
show

show the object

Show the description of CellChatDB databse

Description

Show the description of CellChatDB databse

Usage

showDatabaseCategory(CellChatDB, nrow = 1)

Arguments

CellChatDB

CellChatDB databse
nrow

the number of rows in the plot

Downsampling single cell data using geometric sketching algorithm

Description

USERs need to install the python package 'pip install geosketch' (https://github.com/brianhie/geosketch)

Usage

sketchData(object, percent, idents = NULL, do.PCA = TRUE, dimPC = 30)

Arguments

object

A data matrix (should have row names; samples in rows, features in columns) or a Seurat object.

When object is a PCA or UMAP space, please set 'do.PCA = FALSE'

When object is a data matrix (cells in rows and genes in columns), it is better to use the highly variable genes. PCA will be done on this input data matrix.
percent

the percent of data to sketch
idents

A vector of identity classes to keep for sketching
do.PCA

whether doing PCA on the input data
dimPC

the number of components to use

Value

A vector of cell names to use for downsampling

Smooth the gene expression data

Description

A diffusion process is used to smooth genes’ expression values based on their neighbors’ defined in a high-confidence experimentally validated protein-protein network.

Usage

smoothData(
  object,
  method = c("netSmooth"),
  adj = NULL,
  alpha = 0.5,
  normalizeAdjMatrix = c("rows", "columns")
)

Arguments

object

CellChat object
method

When method = "netSmooth", smoothing a gene’s expression values based on its neighbors defined in a high-confidence experimentally validated protein-protein network.
adj

adjacency matrix of protein-protein interaction network to use
alpha

numeric in [0,1] alpha = 0: no smoothing; a larger value alpha results in increasing levels of smoothing.
normalizeAdjMatrix

how to normalize the adjacency matrix possible values are 'rows' (in-degree) and 'columns' (out-degree)

Details

This function is useful when analyzing single-cell data with shallow sequencing depth because the projection reduces the dropout effects of signaling genes, in particular for possible zero expression of subunits of ligands/receptors

Value

a smoothed gene expression matrix

Visualize spatial cell groups

Description

This function takes a CellChat object as input, and then plot cell groups of interest.

Usage

spatialDimPlot(
  object,
  color.use = NULL,
  group.by = NULL,
  sample.use = NULL,
  sources.use = NULL,
  targets.use = NULL,
  idents.use = NULL,
  alpha = 1,
  shape.by = 16,
  title.name = NULL,
  point.size = 2.4,
  legend.size = 5,
  legend.text.size = 8,
  legend.position = "right",
  ncol = 1,
  byrow = FALSE
)

Arguments

object

cellchat object
color.use

defining the color for each cell group
group.by

Name of one metadata columns to group (color) cells. Default is the defined cell groups in CellChat object
sample.use

the sample name used for visualization, which should be the element in 'object@meta$samples'.
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
idents.use

a vector giving the index or the name of cell groups of interest
alpha

the transparency of individual spot
shape.by

the shape of individual spot
title.name

title name
point.size

the size of spots
legend.size

the size of legend
legend.text.size

the text size on the legend
legend.position

legend position
ncol

number of columns of the legend text
byrow

arrange the legend text byrow or not

A spatial feature plots

Description

This function takes a CellChat object as input, and then plot gene expression distribution over spots/cells on the image.

Usage

spatialFeaturePlot(
  object,
  features = NULL,
  signaling = NULL,
  pairLR.use = NULL,
  sample.use = NULL,
  enriched.only = TRUE,
  thresh = 0.05,
  do.group = TRUE,
  color.heatmap = "Spectral",
  n.colors = 8,
  direction = -1,
  do.binary = FALSE,
  cutoff = NULL,
  color.use = NULL,
  alpha = 1,
  point.size = 0.8,
  legend.size = 3,
  legend.text.size = 8,
  shape.by = 16,
  ncol = NULL,
  show.legend = TRUE,
  show.legend.combined = FALSE
)

Arguments

object

cellchat object
features

a char vector containing features to visualize. 'features' can be genes or column names of 'object@meta'.
signaling

signalling names to visualize
pairLR.use

a data frame consisting of one column named "interaction_name", defining the L-R pairs of interest
sample.use

the sample used for visualization, which should be the element in 'object@meta$samples'.
enriched.only

whether only return the identified enriched signaling genes in the database. Default = TRUE, returning the significantly enriched signaling interactions
thresh

threshold of the p-value for determining significant interaction when visualizing links at the level of ligands/receptors;
do.group

set 'do.group = TRUE' when only showing enriched signaling based on cell group-level communication; set 'do.group = FALSE' when only showing enriched signaling based on individual cell-level communication
color.heatmap

A character string or vector indicating the colormap option to use. It can be the avaibale color palette in brewer.pal() or viridis_pal() (e.g., "Spectral","viridis")
n.colors, direction

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.
do.binary, cutoff

whether binarizing the expression using a given cutoff
color.use

defining the color for cells/spots expressing ligand only, expressing receptor only, expressing both ligand & receptor and cells/spots without expression of given ligands and receptors
alpha

the transparency of individual spot
point.size

the size of cell slot
legend.size

the size of legend
legend.text.size

the text size on the legend
shape.by

the shape of individual spot
ncol

number of columns if plotting multiple plots
show.legend

whether show each figure legend
show.legend.combined

whether show the figure legend for the last plot

Stacked Violin plot

Description

Stacked Violin plot

Usage

StackedVlnPlot(
  object,
  features,
  idents = NULL,
  split.by = NULL,
  color.use = NULL,
  colors.ggplot = FALSE,
  angle.x = 90,
  vjust.x = NULL,
  hjust.x = NULL,
  show.text.y = TRUE,
  line.size = NULL,
  pt.size = 0,
  plot.margin = margin(0, 0, 0, 0, "cm"),
  ...
)

Arguments

object

seurat object
features

Features to plot (gene expression, metrics)
idents

Which classes to include in the plot (default is all)
split.by

Name of a metadata column to split plot by;
color.use

defining the color for each cell group
colors.ggplot

whether use ggplot color scheme; default: colors.ggplot = FALSE
angle.x

angle for x-axis text rotation
vjust.x

adjust x axis text
hjust.x

adjust x axis text
show.text.y

whther show y-axis text
line.size

line width in the violin plot
pt.size

size of the dots
plot.margin

adjust the white space between each plot
...

Extra parameters passed to VlnPlot from Seurat package

Value

ggplot2 object

Subset CellChat object using a portion of cells

Description

Subset CellChat object using a portion of cells

Usage

subsetCellChat(
  object,
  cells.use = NULL,
  idents.use = NULL,
  group.by = NULL,
  invert = FALSE,
  thresh = 0.05
)

Arguments

object

A CellChat object (either an object from a single dataset or a merged objects from multiple datasets)
cells.use

a char vector giving the cell barcodes to subset. If cells.use = NULL, USER must define 'idents.use'
idents.use

a subset of cell groups used for analysis
group.by

cell group information; default is 'object@idents'; otherwise it should be one of the column names of the meta slot
invert

whether invert the idents.use
thresh

threshold of the p-value for determining significant interaction. A parameter as an input of the function 'computeCommunProbPathway'

Subset the inferred cell-cell communications of interest

Description

NB: If all arguments are NULL, it returns a data frame consisting of all the inferred cell-cell communications

Usage

subsetCommunication(
  object = NULL,
  net = NULL,
  slot.name = "net",
  sources.use = NULL,
  targets.use = NULL,
  signaling = NULL,
  pairLR.use = NULL,
  thresh = 0.05,
  datasets = NULL,
  ligand.pvalues = NULL,
  ligand.logFC = NULL,
  ligand.pct.1 = NULL,
  ligand.pct.2 = NULL,
  receptor.pvalues = NULL,
  receptor.logFC = NULL,
  receptor.pct.1 = NULL,
  receptor.pct.2 = NULL
)

Arguments

object

CellChat object
net

Alternative input is a data frame with at least with three columns defining the cell-cell communication network ("source","target","interaction_name")
slot.name

the slot name of object: slot.name = "net" when extracting the inferred communications at the level of ligands/receptors; slot.name = "netP" when extracting the inferred communications at the level of signaling pathways
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
thresh

threshold of the p-value for determining significant interaction
datasets

select the inferred cell-cell communications from a particular 'datasets' when inputing a data frame 'net'
ligand.pvalues, ligand.logFC, ligand.pct.1, ligand.pct.2

set threshold for ligand genes

ligand.pvalues: threshold for pvalues in the differential expression gene analysis (DEG)

ligand.logFC: threshold for logFoldChange in the DEG analysis; When ligand.logFC > 0, keep upgulated genes; otherwise, kepp downregulated genes

ligand.pct.1: threshold for the percent of expressed genes in the defined 'positive' cell group. keep genes with percent greater than ligand.pct.1

ligand.pct.2: threshold for the percent of expressed genes in the cells except for the defined 'positive' cell group
receptor.pvalues, receptor.logFC, receptor.pct.1, receptor.pct.2

set threshold for receptor genes

Value

If input object is created from a single dataset, a data frame of the inferred cell-cell communications of interest, consisting of source, target, interaction_name, pathway_name, prob and other information

If input object is a merged object from multiple datasets, it will return a list and each element is a data frame for one dataset

Examples

## Not run: 
# access all the inferred cell-cell communications
df.net <- subsetCommunication(cellchat)

# access all the inferred cell-cell communications at the level of signaling pathways
df.net <- subsetCommunication(cellchat, slot.name = "netP")

# Subset to certain cells with sources.use and targets.use
df.net <- subsetCommunication(cellchat, sources.use = c(1,2), targets.use = c(4,5))

# Subset to certain signaling, e.g., WNT and TGFb
df.net <- subsetCommunication(cellchat, signaling = c("WNT", "TGFb"))

## End(Not run)

Subset the inferred cell-cell communications of interest

Description

NB: If all arguments are NULL, it returns a data frame consisting of all the inferred cell-cell communications

Usage

subsetCommunication_internal(
  net,
  LR,
  cells.level,
  slot.name = "net",
  sources.use = NULL,
  targets.use = NULL,
  signaling = NULL,
  pairLR.use = NULL,
  thresh = 0.05,
  datasets = NULL,
  ligand.pvalues = NULL,
  ligand.logFC = NULL,
  ligand.pct.1 = NULL,
  ligand.pct.2 = NULL,
  receptor.pvalues = NULL,
  receptor.logFC = NULL,
  receptor.pct.1 = NULL,
  receptor.pct.2 = NULL
)

Arguments

net, LR, cells.level

net is object@net or a data frame; LR: object@LR$LRsig; cells.level: levels(object@idents)
slot.name

the slot name of object: slot.name = "net" when extracting the inferred communications at the level of ligands/receptors; slot.name = "netP" when extracting the inferred communications at the level of signaling pathways
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
thresh

threshold of the p-value for determining significant interaction
datasets

select the inferred cell-cell communications from a particular 'datasets' when inputing a data frame 'net'
ligand.pvalues, ligand.logFC, ligand.pct.1, ligand.pct.2

set threshold for ligand genes

ligand.pvalues: threshold for pvalues in the differential expression gene analysis (DEG)

ligand.logFC: threshold for logFoldChange in the DEG analysis; When ligand.logFC > 0, keep upgulated genes; otherwise, kepp downregulated genes

ligand.pct.1: threshold for the percent of expressed genes in the defined 'positive' cell group. keep genes with percent greater than ligand.pct.1

ligand.pct.2: threshold for the percent of expressed genes in the cells except for the defined 'positive' cell group
receptor.pvalues, receptor.logFC, receptor.pct.1, receptor.pct.2

set threshold for receptor genes

Value

A data frame of the inferred cell-cell communications of interest, consisting of source, target, interaction_name, pathway_name, prob and other information

Subset the expression data of signaling genes for saving computation cost

Description

Subset the expression data of signaling genes for saving computation cost

Usage

subsetData(object, features = NULL)

Arguments

object

CellChat object
features

default = NULL: subset the expression data of signaling genes in CellChatDB.use

Value

An updated CellChat object by assigning a subset of the data into the slot 'data.signaling'

Subset CellChatDB databse by only including interactions of interest

Description

Subset CellChatDB databse by only including interactions of interest

Usage

subsetDB(CellChatDB, search = c(), key = "annotation", non_protein = FALSE)

Arguments

CellChatDB

CellChatDB databse
search

a character vector, which is a subset of c("Secreted Signaling","ECM-Receptor","Cell-Cell Contact","Non-protein Signaling"); Setting search = NULL & non_protein = FALSE will return all signaling except for "Non-protein Signaling".

When 'key' is a vector, the 'search' should be a list with the size being 'length(key)', where each element is a character vector.
key

a character vector and each element should be one of the column names of the interaction_input from CellChatDB.
non_protein

whether to use the non-protein signaling for CellChat analysis. By default, non_protein = FALSE because most of non-protein signaling are the special synaptic signaling interactions that can only be used when inferring neuron-neuron communication.

Compute the average expression per cell group when the percent of expressing cells per cell group larger than a threshold

Description

Compute the average expression per cell group when the percent of expressing cells per cell group larger than a threshold

Usage

thresholdedMean(x, trim = 0.1, na.rm = TRUE)

Arguments

x

a numeric vector
trim

the percent of expressing cells per cell group to be considered as zero
na.rm

whether remove na

Compute the Tukey's trimean

Description

Compute the Tukey's trimean

Usage

triMean(x, na.rm = TRUE)

Arguments

x

a numeric vector
na.rm

whether remove na

Update the cell-cell communication array from a customized cell-cell-communication scores between different cell groups

Description

Users may also check the 'updateCellChatDB' function for integrating other resources or utilizing a custom database

Usage

updateCCC_score(object, net)

Arguments

object

CellChat object
net

a data frame with at least five columns named as 'source','target','ligand','receptor' and 'score', which defines the customized cell-cell-communication scores between different cell groups. a p-value column named 'pval', and additional columns named 'interaction_name' and 'interaction_name_2' can be also provided.

Value

a CellChat object with updated slot 'net' and slot 'DB' if db is not NULL.

Update a single CellChat object

Description

Update a single previously calculated CellChat object for spatial transcriptomics data analysis (version < 2.1.0)

Usage

updateCellChat(object)

Arguments

object

CellChat object

Details

Update a single previously calculated CellChat object (version < 1.6.0)

version < 0.5.0: '[email protected]' is now '[email protected]$features'; 'object@net$sum' is now 'object@net$weight' if 'aggregateNet' has been run.

version 1.6.0: a 'object@images' slot is added and 'datatype' is added in 'object@options$datatype'

version 2.1.0: a column named 'slices' is added in 'meta' data for spatial transcriptomics data analysis.

version 2.1.1: 'images$scale.factors' is changed to 'images$spatial.factors' for spatial transcriptomics data analysis.

version 2.1.2: the column 'slices' in 'object@meta' is renamed as 'samples' in order to identify consistent signaling across samples for cell-cell communication analysis.

version 2.1.3: the slot '[email protected]' is renamed as '[email protected]'.

Value

a updated CellChat object

Update CellChatDB by integrating new L-R pairs from other resources or adding more information

Description

Update CellChatDB by integrating new L-R pairs from other resources or adding more information

Usage

updateCellChatDB(
  db,
  gene_info = NULL,
  other_info = NULL,
  gene_info_columnNew = NULL,
  trim.pathway = FALSE,
  merged = FALSE,
  species_target = NULL
)

Arguments

db

a data frame of the customized ligand-receptor database with at least two columns named as 'ligand' and 'receptor'. We highly suggest users to provide a column of pathway information named 'pathway_name' associated with each L-R pair. Other optional columns include 'interaction_name' and 'interaction_name_2'. The default columns of CellChatDB can be checked via 'colnames(CellChatDB.human$interaction)'.
gene_info

a data frame with at least one column named as 'Symbol'. "When setting gene_info = NULL, the input 'species_target' should be provided: either 'human' or 'mouse'.
other_info

a list consisting of other information including a dataframe named as 'complex' and a dataframe named as 'cofactor'. This additional information is not necessary. If other_info is provided, the 'complex' and 'cofactor' are dataframes with defined rownames.
gene_info_columnNew

a data frame with at least two columns named as 'Symbol' and 'AntibodyName', which will add a new column named 'AntibodyName' into 'db$geneInfo'.
trim.pathway

whether to delete the interactions with missing pathway names when the column 'pathway_name' is provided in 'db'.
merged

whether merging the input database with the existing CellChatDB. setting merged = TRUE, the input 'species_target' should be provided: either 'human' or 'mouse'.
species_target

the target species for output: either 'human' or 'mouse'.

Value

a list consisting of the customized L-R database for further CellChat analysis

Examples

## Not run: 
# integrating new L-R pairs from other resources or utilizing a custom database `db.user`
db.new <- updateCellChatDB(db = db.user, gene_info = gene_info)
db.new <- updateCellChatDB(db = db.user, gene_info = NULL, species_target = "human")
# Alternatively, users can integrate the customized L-R pairs into the built-in CellChatDB
db.new <- updateCellChatDB(db = db.user, merged = TRUE, species_target = "human")
# Add new columns (e.g., AntibodyName) into gene_info
db.new.human <- updateCellChatDB(db = CellChatDB.human$interaction, gene_info = CellChatDB.human$geneInfo, other_info=list(complex = CellChatDB.human$complex, cofactor = CellChatDB.human$cofactor),gene_info_columnNew = gene_info_columnNew)

# Users can now use this new database in CellChat analysis
cellchat@DB <- db.new

## End(Not run)

Update and re-order the cell group names after running 'computeCommunProb'

Description

Update and re-order the cell group names after running 'computeCommunProb'

Usage

updateClusterLabels(
  object,
  old.cluster.name = NULL,
  new.cluster.name = NULL,
  new.order = NULL,
  new.cluster.metaname = "new.labels"
)

Arguments

object

CellChat object
old.cluster.name

A vector defining old cell group labels in 'object@idents'; Default = NULL, which will use 'levels(object@idents)'
new.cluster.name

A vector defining new cell group labels to rename
new.order

reset order of cell group labels
new.cluster.metaname

assign a name of the new labels, which will be the column name of new labels in 'object@meta'

Value

An updated CellChat object