This vignette outlines the steps of inference, analysis and visualization of cell-cell communication network for a single dataset using CellChat. We showcase CellChat’s diverse functionalities by applying it to a scRNA-seq data on cells from lesional (LS, diseased) human skin from patients.

CellChat requires gene expression data of cells as the user input and models the probability of cell-cell communication by integrating gene expression with prior knowledge of the interactions between signaling ligands, receptors and their cofactors.

Upon infering the intercellular communication network, CellChat provides functionality for further data exploration, analysis, and visualization.

Load the required libraries

library(CellChat)
library(patchwork)
options(stringsAsFactors = FALSE)
# reticulate::use_python("/Users/suoqinjin/anaconda3/bin/python", required=T)

Part I: Data input & processing and initialization of CellChat object

CellChat requires two user inputs: one is the gene expression data of cells, and the other is the user assigned cell labels.

Prepare required input data for CellChat analysis

For the gene expression data matrix, genes should be in rows with rownames and cells in columns with colnames. Normalized data (e.g., library-size normalization and then log-transformed with a pseudocount of 1) is required as input for CellChat analysis. If user provides count data, we provide a normalizeData function to account for library size and then do log-transformed.

For the cell group information, a dataframe with rownames is required as input for CellChat.

In addition to taking a count data matrix as an input, we also provide instructions for how to prepare CellChat input files from other existing single-cell analysis toolkits, including Seurat, SingleCellExperiment and Scanpy. Please start to prepare the input data by following option A when the normalized count data and meta data are available, option B when the Seurat object is available, option C when the SingleCellExperiment object is available, and option D when the Anndata object is available. See details in the tutorial on Interface_with_other_single-cell_analysis_toolkits.

(A) Starting from a count data matrix

ptm = Sys.time()
# Here we load a scRNA-seq data matrix and its associated cell meta data
# This is a combined data from two biological conditions: normal and diseases
load("/Users/suoqinjin/Library/CloudStorage/OneDrive-Personal/works/CellChat/tutorial/data_humanSkin_CellChat.rda")
data.input = data_humanSkin$data # normalized data matrix
meta = data_humanSkin$meta # a dataframe with rownames containing cell mata data
cell.use = rownames(meta)[meta$condition == "LS"] # extract the cell names from disease data

# Subset the input data for CelChat analysis
data.input = data.input[, cell.use]
meta = meta[cell.use, ]
# meta = data.frame(labels = meta$labels[cell.use], row.names = colnames(data.input)) # manually create a dataframe consisting of the cell labels
unique(meta$labels) # check the cell labels
#>  [1] Inflam. FIB  FBN1+ FIB    APOE+ FIB    COL11A1+ FIB cDC2        
#>  [6] LC           Inflam. DC   cDC1         CD40LG+ TC   Inflam. TC  
#> [11] TC           NKT         
#> 12 Levels: APOE+ FIB FBN1+ FIB COL11A1+ FIB Inflam. FIB cDC1 cDC2 ... NKT

(B) Starting from a Seurat object

data.input <- seurat_object[["RNA"]]@data # normalized data matrix
# For Seurat version >= “5.0.0”, get the normalized data via `seurat_object[["RNA"]]$data`
labels <- Idents(seurat_object)
meta <- data.frame(labels = labels, row.names = names(labels)) # create a dataframe of the cell labels

(C) Starting from a SingleCellExperiment object

data.input <- SingleCellExperiment::logcounts(object) # normalized data matrix
meta <- as.data.frame(SingleCellExperiment::colData(object)) # extract a dataframe of the cell labels
meta$labels <- meta[["sce.clusters"]]

(D) Starting from an Anndata object

# read the data into R using anndata R package
install.packages("anndata")
library(anndata)
ad <- read_h5ad("scanpy_object.h5ad")
# access count data matrix
counts <- t(as.matrix(ad$X))
# normalize the count data if the normalized data is not available in the .h5ad file
library.size <- Matrix::colSums(counts)
data.input <- as(log1p(Matrix::t(Matrix::t(counts)/library.size) * 10000), "dgCMatrix")
# access meta data
meta <- ad$obs 
meta$labels <- meta[["ad_clusters"]]

Create a CellChat object

USERS can create a new CellChat object from a data matrix, Seurat, SingleCellExperiment or AnnData object. If input is a Seurat or SingleCellExperiment object, the meta data in the object will be used by default and USER must provide group.by to define the cell groups. e.g, group.by = “ident” for the default cell identities in Seurat object.

Create a CellChat object by following option A when taking the digital gene expression matrix and cell label information as input, option B when taking a Seurat object as input, option C when taking a SingleCellExperiment object as input, and option D when taking a AnnData object as input.

NB: If USERS load previously calculated CellChat object (version < 0.5.0), please update the object via updateCellChat

(A) Starting from the digital gene expression matrix and cell label information

cellchat <- createCellChat(object = data.input, meta = meta, group.by = "labels")
#> [1] "Create a CellChat object from a data matrix"
#> Set cell identities for the new CellChat object 
#> The cell groups used for CellChat analysis are  APOE+ FIB, FBN1+ FIB, COL11A1+ FIB, Inflam. FIB, cDC1, cDC2, LC, Inflam. DC, TC, Inflam. TC, CD40LG+ TC, NKT

(B) Starting from a Seurat object

cellChat <- createCellChat(object = seurat.obj, group.by = "ident", assay = "RNA")

(C) Starting from a SingleCellExperiment object

cellChat <- createCellChat(object = sce.obj, group.by = "sce.clusters")

(D) Starting from an AnnData object

sce <- zellkonverter::readH5AD(file = "adata.h5ad")
# retrieve all the available assays within sce object
assayNames(sce)
# added a new assay entry "logcounts" if not available
counts <- assay(sce, "X") # 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)
# extract a cell meta data
meta <- as.data.frame(SingleCellExperiment::colData(sce)) #
cellChat <- createCellChat(object = sce, group.by = "sce.clusters")

If cell mata information is not added when creating CellChat object, USERS can also add it later using addMeta, and set the default cell identities using setIdent.

cellchat <- addMeta(cellchat, meta = meta)
cellchat <- setIdent(cellchat, ident.use = "labels") # set "labels" as default cell identity
levels(cellchat@idents) # show factor levels of the cell labels
groupSize <- as.numeric(table(cellchat@idents)) # number of cells in each cell group

Set the ligand-receptor interaction database

Before users can employ CellChat to infer cell-cell communication, they need to set the ligand-receptor interaction database and identify over-expressed ligands or receptors.

Our database CellChatDB is a manually curated database of literature-supported ligand-receptor interactions in both human and mouse. CellChatDB v2 contains ~3,300 validated molecular interactions, including ~40% of secrete autocrine/paracrine signaling interactions, ~17% of extracellular matrix (ECM)-receptor interactions, ~13% of cell-cell contact interactions and ~30% non-protein signaling. Compared to CellChatDB v1, CellChatDB v2 adds more than 1000 protein and non-protein interactions such as metabolic and synaptic signaling. It should be noted that for molecules that are not directly related to genes measured in scRNA-seq, CellChat v2 estimates the expression of ligands and receptors using those molecules’ key mediators or enzymes for potential communication mediated by non-proteins.

CellChatDB v2 also adds additional functional annotations of ligand-receptor pairs, such as UniProtKB keywords (including biological process, molecular function, functional class, disease, etc), subcellular location and relevance to neurotransmitter.

Users can update CellChatDB by adding their own curated ligand-receptor pairs. Please check the tutorial on updating the ligand-receptor interaction database CellChatDB.

When analyzing human samples, use the database CellChatDB.human; when analyzing mouse samples, use the database CellChatDB.mouse. CellChatDB categorizes ligand-receptor pairs into different types, including “Secreted Signaling”, “ECM-Receptor”, “Cell-Cell Contact” and “Non-protein Signaling”. By default, the “Non-protein Signaling” are not used.

CellChatDB <- CellChatDB.human # use CellChatDB.mouse if running on mouse data
showDatabaseCategory(CellChatDB)

# Show the structure of the database
dplyr::glimpse(CellChatDB$interaction)
#> Rows: 3,234
#> Columns: 28
#> $ interaction_name         <chr> "TGFB1_TGFBR1_TGFBR2", "TGFB2_TGFBR1_TGFBR2",…
#> $ pathway_name             <chr> "TGFb", "TGFb", "TGFb", "TGFb", "TGFb", "TGFb…
#> $ ligand                   <chr> "TGFB1", "TGFB2", "TGFB3", "TGFB1", "TGFB1", …
#> $ receptor                 <chr> "TGFbR1_R2", "TGFbR1_R2", "TGFbR1_R2", "ACVR1…
#> $ agonist                  <chr> "TGFb agonist", "TGFb agonist", "TGFb agonist…
#> $ antagonist               <chr> "TGFb antagonist", "TGFb antagonist", "TGFb a…
#> $ co_A_receptor            <chr> "", "", "", "", "", "", "", "", "", "", "", "…
#> $ co_I_receptor            <chr> "TGFb inhibition receptor", "TGFb inhibition …
#> $ evidence                 <chr> "KEGG: hsa04350", "KEGG: hsa04350", "KEGG: hs…
#> $ annotation               <chr> "Secreted Signaling", "Secreted Signaling", "…
#> $ interaction_name_2       <chr> "TGFB1 - (TGFBR1+TGFBR2)", "TGFB2 - (TGFBR1+T…
#> $ is_neurotransmitter      <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL…
#> $ ligand.symbol            <chr> "TGFB1", "TGFB2", "TGFB3", "TGFB1", "TGFB1", …
#> $ ligand.family            <chr> "TGF-beta", "TGF-beta", "TGF-beta", "TGF-beta…
#> $ ligand.location          <chr> "Extracellular matrix, Secreted, Extracellula…
#> $ ligand.keyword           <chr> "Disease variant, Signal, Reference proteome,…
#> $ ligand.secreted_type     <chr> "growth factor", "growth factor", "cytokine;g…
#> $ ligand.transmembrane     <lgl> FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALS…
#> $ receptor.symbol          <chr> "TGFBR2, TGFBR1", "TGFBR2, TGFBR1", "TGFBR2, …
#> $ receptor.family          <chr> "Protein kinase superfamily, TKL Ser/Thr prot…
#> $ receptor.location        <chr> "Cell membrane, Secreted, Membrane raft, Cell…
#> $ receptor.keyword         <chr> "Membrane, Secreted, Disulfide bond, Kinase, …
#> $ receptor.surfaceome_main <chr> "Receptors", "Receptors", "Receptors", "Recep…
#> $ receptor.surfaceome_sub  <chr> "Act.TGFB;Kinase", "Act.TGFB;Kinase", "Act.TG…
#> $ receptor.adhesome        <chr> "", "", "", "", "", "", "", "", "", "", "", "…
#> $ receptor.secreted_type   <chr> "", "", "", "", "", "", "", "", "", "", "", "…
#> $ receptor.transmembrane   <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRU…
#> $ version                  <chr> "CellChatDB v1", "CellChatDB v1", "CellChatDB…

# use a subset of CellChatDB for cell-cell communication analysis
CellChatDB.use <- subsetDB(CellChatDB, search = "Secreted Signaling", key = "annotation") # use Secreted Signaling

# Only uses the Secreted Signaling from CellChatDB v1
#  CellChatDB.use <- subsetDB(CellChatDB, search = list(c("Secreted Signaling"), c("CellChatDB v1")), key = c("annotation", "version"))

# use all CellChatDB except for "Non-protein Signaling" for cell-cell communication analysis
# CellChatDB.use <- subsetDB(CellChatDB)


# use all CellChatDB for cell-cell communication analysis
# CellChatDB.use <- CellChatDB # simply use the default CellChatDB. We do not suggest to use it in this way because CellChatDB v2 includes "Non-protein Signaling" (i.e., metabolic and synaptic signaling). 

# set the used database in the object
cellchat@DB <- CellChatDB.use

Preprocessing the expression data for cell-cell communication analysis

To infer the cell state-specific communications, CellChat identifies over-expressed ligands or receptors in one cell group and then identifies over-expressed ligand-receptor interactions if either ligand or receptor are over-expressed.

We also provide a function to project gene expression data onto protein-protein interaction (PPI) network. Specifically, a diffusion process is used to smooth genes’ expression values based on their neighbors’ defined in a high-confidence experimentally validated protein-protein network. 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. One might be concerned about the possible artifact introduced by this diffusion process, however, it will only introduce very weak communications. By default CellChat uses the raw data (i.e., object@data.signaling) instead of the projected data. To use the projected data, users should run the function projectData before running computeCommunProb, and then set raw.use = FALSE when running computeCommunProb.

# subset the expression data of signaling genes for saving computation cost
cellchat <- subsetData(cellchat) # This step is necessary even if using the whole database
future::plan("multisession", workers = 4) # do parallel
cellchat <- identifyOverExpressedGenes(cellchat)
cellchat <- identifyOverExpressedInteractions(cellchat)
#> The number of highly variable ligand-receptor pairs used for signaling inference is 692

execution.time = Sys.time() - ptm
print(as.numeric(execution.time, units = "secs"))
#> [1] 13.20763
# project gene expression data onto PPI (Optional: when running it, USER should set `raw.use = FALSE` in the function `computeCommunProb()` in order to use the projected data)
# cellchat <- projectData(cellchat, PPI.human)

Part II: Inference of cell-cell communication network

CellChat infers the biologically significant cell-cell communication by assigning each interaction with a probability value and peforming a permutation test. CellChat models the probability of cell-cell communication by integrating gene expression with prior known knowledge of the interactions between signaling ligands, receptors and their cofactors using the law of mass action.

CAUTION: The number of inferred ligand-receptor pairs clearly depends on the method for calculating the average gene expression per cell group. By default, CellChat uses a statistically robust mean method called ‘trimean’, which produces fewer interactions than other methods. However, we find that CellChat performs well at predicting stronger interactions, which is very helpful for narrowing down on interactions for further experimental validations. In computeCommunProb, we provide an option for using other methods, such as 5% and 10% truncated mean, to calculating the average gene expression. Of note, ‘trimean’ approximates 25% truncated mean, implying that the average gene expression is zero if the percent of expressed cells in one group is less than 25%. To use 10% truncated mean, USER can set type = "truncatedMean" and trim = 0.1. To determine a proper value of trim, CellChat provides a function computeAveExpr, which can help to check the average expression of signaling genes of interest, e.g, computeAveExpr(cellchat, features = c("CXCL12","CXCR4"), type = "truncatedMean", trim = 0.1). Therefore, if well-known signaling pathways in the studied biological process are not predicted, users can try truncatedMean with lower values of trim to change the method for calculating the average gene expression per cell group.

When analyzing unsorted single-cell transcriptomes, under the assumption that abundant cell populations tend to send collectively stronger signals than the rare cell populations, CellChat can also consider the effect of cell proportion in each cell group in the probability calculation. USER can set population.size = TRUE.

Compute the communication probability and infer cellular communication network

ptm = Sys.time()
cellchat <- computeCommunProb(cellchat, type = "triMean")
#> triMean is used for calculating the average gene expression per cell group. 
#> [1] ">>> Run CellChat on sc/snRNA-seq data <<< [2024-02-14 00:32:35.767285]"
#> [1] ">>> CellChat inference is done. Parameter values are stored in `object@options$parameter` <<< [2024-02-14 00:33:13.121225]"

The key parameter for this analysis is type, the method for computing the average gene expression per cell group. By default type = "triMean", producing fewer but stronger interactions. When setting type = "truncatedMean", a value should be assigned to trim, producing more interactions. Please check above in detail on the method for calculating the average gene expression per cell group.

Users can filter out the cell-cell communication if there are only few cells in certain cell groups. By default, the minimum number of cells required in each cell group for cell-cell communication is 10.

cellchat <- filterCommunication(cellchat, min.cells = 10)

Extract the inferred cellular communication network as a data frame

We provide a function subsetCommunication to easily access the inferred cell-cell communications of interest. For example,

  • df.net <- subsetCommunication(cellchat) returns a data frame consisting of all the inferred cell-cell communications at the level of ligands/receptors. Set slot.name = "netP" to access the the inferred communications at the level of signaling pathways

  • df.net <- subsetCommunication(cellchat, sources.use = c(1,2), targets.use = c(4,5)) gives the inferred cell-cell communications sending from cell groups 1 and 2 to cell groups 4 and 5.

  • df.net <- subsetCommunication(cellchat, signaling = c("WNT", "TGFb")) gives the inferred cell-cell communications mediated by signaling WNT and TGFb.

Infer the cell-cell communication at a signaling pathway level

CellChat computes the communication probability on signaling pathway level by summarizing the communication probabilities of all ligands-receptors interactions associated with each signaling pathway.

NB: The inferred intercellular communication network of each ligand-receptor pair and each signaling pathway is stored in the slot ‘net’ and ‘netP’, respectively.

cellchat <- computeCommunProbPathway(cellchat)

Calculate the aggregated cell-cell communication network

CellChat calculates the aggregated cell-cell communication network by counting the number of links or summarizing the communication probability. Users can also calculate the aggregated network among a subset of cell groups by setting sources.use and targets.use.

cellchat <- aggregateNet(cellchat)
execution.time = Sys.time() - ptm
print(as.numeric(execution.time, units = "secs"))
#> [1] 38.73308

CellChat can also visualize the aggregated cell-cell communication network. For example, showing the number of interactions or the total interaction strength (weights) between any two cell groups using circle plot.

ptm = Sys.time()
groupSize <- as.numeric(table(cellchat@idents))
par(mfrow = c(1,2), xpd=TRUE)
netVisual_circle(cellchat@net$count, vertex.weight = groupSize, weight.scale = T, label.edge= F, title.name = "Number of interactions")
netVisual_circle(cellchat@net$weight, vertex.weight = groupSize, weight.scale = T, label.edge= F, title.name = "Interaction weights/strength")

Due to the complicated cell-cell communication network, we can examine the signaling sent from each cell group. Here we also control the parameter edge.weight.max so that we can compare edge weights between differet networks.

mat <- cellchat@net$weight
par(mfrow = c(3,4), xpd=TRUE)
for (i in 1:nrow(mat)) {
  mat2 <- matrix(0, nrow = nrow(mat), ncol = ncol(mat), dimnames = dimnames(mat))
  mat2[i, ] <- mat[i, ]
  netVisual_circle(mat2, vertex.weight = groupSize, weight.scale = T, edge.weight.max = max(mat), title.name = rownames(mat)[i])
}

Part III: Visualization of cell-cell communication network

Upon infering the cell-cell communication network, CellChat provides various functionality for further data exploration, analysis, and visualization. Specifically:

Visualize each signaling pathway using Hierarchy plot, Circle plot or Chord diagram

Hierarchy plot: USER should define vertex.receiver, which is a numeric vector giving the index of the cell groups as targets in the left part of hierarchy plot. This hierarchical plot consist of two components: the left portion shows autocrine and paracrine signaling to certain cell groups of interest (i.e, the defined vertex.receiver), and the right portion shows autocrine and paracrine signaling to the remaining cell groups in the dataset. Thus, hierarchy plot provides an informative and intuitive way to visualize autocrine and paracrine signaling communications between cell groups of interest. For example, when studying the cell-cell communication between fibroblasts and immune cells, USER can define vertex.receiver as all fibroblast cell groups.

Chord diagram: CellChat provides two functions netVisual_chord_cell and netVisual_chord_gene for visualizing cell-cell communication with different purposes and different levels. netVisual_chord_cell is used for visualizing the cell-cell communication between different cell groups (where each sector in the chord diagram is a cell group), and netVisual_chord_gene is used for visualizing the cell-cell communication mediated by mutiple ligand-receptors or signaling pathways (where each sector in the chord diagram is a ligand, receptor or signaling pathway.)

Explnations of edge color/weight, node color/size/shape: In all visualization plots, edge colors are consistent with the sources as sender, and edge weights are proportional to the interaction strength. Thicker edge line indicates a stronger signal. In the Hierarchy plot and Circle plot, circle sizes are proportional to the number of cells in each cell group. In the hierarchy plot, solid and open circles represent source and target, respectively. In the Chord diagram, the inner thinner bar colors represent the targets that receive signal from the corresponding outer bar. The inner bar size is proportional to the signal strength received by the targets. Such inner bar is helpful for interpreting the complex chord diagram. Note that there exist some inner bars without any chord for some cell groups, please just igore it because this is an issue that has not been addressed by circlize package.

Visualization of cell-cell communication at different levels: One can visualize the inferred communication network of signaling pathways using netVisual_aggregate, and visualize the inferred communication networks of individual L-R pairs associated with that signaling pathway using netVisual_individual.

Here we take input of one signaling pathway as an example. All the signaling pathways showing significant communications can be accessed by cellchat@netP$pathways.

pathways.show <- c("CXCL") 
# Hierarchy plot
# Here we define `vertex.receive` so that the left portion of the hierarchy plot shows signaling to fibroblast and the right portion shows signaling to immune cells 
vertex.receiver = seq(1,4) # a numeric vector. 
netVisual_aggregate(cellchat, signaling = pathways.show,  vertex.receiver = vertex.receiver)
# Circle plot
par(mfrow=c(1,1))
netVisual_aggregate(cellchat, signaling = pathways.show, layout = "circle")

# Chord diagram
par(mfrow=c(1,1))
netVisual_aggregate(cellchat, signaling = pathways.show, layout = "chord")

# Heatmap
par(mfrow=c(1,1))
netVisual_heatmap(cellchat, signaling = pathways.show, color.heatmap = "Reds")
#> Do heatmap based on a single object

For the chord diagram, CellChat has an independent function netVisual_chord_cell to flexibly visualize the signaling network by adjusting different parameters in the circlize package. For example, we can define a named char vector group to create multiple-group chord diagram, e.g., grouping cell clusters into different cell types.

# Chord diagram
group.cellType <- c(rep("FIB", 4), rep("DC", 4), rep("TC", 4)) # grouping cell clusters into fibroblast, DC and TC cells
names(group.cellType) <- levels(cellchat@idents)
netVisual_chord_cell(cellchat, signaling = pathways.show, group = group.cellType, title.name = paste0(pathways.show, " signaling network"))
#> Plot the aggregated cell-cell communication network at the signaling pathway level

Compute the contribution of each ligand-receptor pair to the overall signaling pathway and visualize cell-cell communication mediated by a single ligand-receptor pair

netAnalysis_contribution(cellchat, signaling = pathways.show)

We can also visualize the cell-cell communication mediated by a single ligand-receptor pair. We provide a function extractEnrichedLR to extract all the significant interactions (L-R pairs) and related signaling genes for a given signaling pathway.

pairLR.CXCL <- extractEnrichedLR(cellchat, signaling = pathways.show, geneLR.return = FALSE)
LR.show <- pairLR.CXCL[1,] # show one ligand-receptor pair
# Hierarchy plot
vertex.receiver = seq(1,4) # a numeric vector
netVisual_individual(cellchat, signaling = pathways.show,  pairLR.use = LR.show, vertex.receiver = vertex.receiver)
#> [[1]]
# Circle plot
netVisual_individual(cellchat, signaling = pathways.show, pairLR.use = LR.show, layout = "circle")

#> [[1]]
# Chord diagram
netVisual_individual(cellchat, signaling = pathways.show, pairLR.use = LR.show, layout = "chord")

#> [[1]]

Automatically save the plots of the all inferred network for quick exploration

In practical use, USERS can use ‘for … loop’ to automatically save the all inferred network for quick exploration using netVisual. netVisual supports an output in the formats of svg, png and pdf.

# Access all the signaling pathways showing significant communications
pathways.show.all <- cellchat@netP$pathways
# check the order of cell identity to set suitable vertex.receiver
levels(cellchat@idents)
vertex.receiver = seq(1,4)
for (i in 1:length(pathways.show.all)) {
  # Visualize communication network associated with both signaling pathway and individual L-R pairs
  netVisual(cellchat, signaling = pathways.show.all[i], vertex.receiver = vertex.receiver, layout = "hierarchy")
  # Compute and visualize the contribution of each ligand-receptor pair to the overall signaling pathway
  gg <- netAnalysis_contribution(cellchat, signaling = pathways.show.all[i])
  ggsave(filename=paste0(pathways.show.all[i], "_L-R_contribution.pdf"), plot=gg, width = 3, height = 2, units = 'in', dpi = 300)
}

Visualize cell-cell communication mediated by multiple ligand-receptors or signaling pathways

CellChat can also show all the significant interactions mediated by L-R pairs and signaling pathways, and interactions provided by users from some cell groups to other cell groups using the function netVisual_bubble (option A) and netVisual_chord_gene (option B).

(A) Bubble plot

We can also show all the significant interactions (L-R pairs) from some cell groups to other cell groups using netVisual_bubble.

# (1) 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)
#> Comparing communications on a single object

# (2) 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"), remove.isolate = FALSE)
#> Comparing communications on a single object

# (3) show all the significant interactions (L-R pairs) based on user's input (defined by `pairLR.use`)
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)
#> Comparing communications on a single object

# set the order of interacting cell pairs on x-axis
# (4) 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)
# (5) 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)
# (6) 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)
# (7) 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)
# (8) 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)

(B) Chord diagram

Similar to Bubble plot, CellChat provides a function netVisual_chord_gene for drawing Chord diagram to

  • show all the interactions (L-R pairs or signaling pathways) from some cell groups to other cell groups. Two special cases: one is showing all the interactions sending from one cell groups and the other is showing all the interactions received by one cell group.

  • show the interactions inputted by USERS or certain signaling pathways defined by USERS

# show all the significant interactions (L-R pairs) from some cell groups (defined by 'sources.use') to other cell groups (defined by 'targets.use')
# show all the interactions sending from Inflam.FIB
netVisual_chord_gene(cellchat, sources.use = 4, targets.use = c(5:11), lab.cex = 0.5,legend.pos.y = 30)

# show all the interactions received by Inflam.DC
netVisual_chord_gene(cellchat, sources.use = c(1,2,3,4), targets.use = 8, legend.pos.x = 15)

# show all the significant interactions (L-R pairs) associated with certain signaling pathways
netVisual_chord_gene(cellchat, sources.use = c(1,2,3,4), targets.use = c(5:11), signaling = c("CCL","CXCL"),legend.pos.x = 8)

# show all the significant signaling pathways from some cell groups (defined by 'sources.use') to other cell groups (defined by 'targets.use')
netVisual_chord_gene(cellchat, sources.use = c(1,2,3,4), targets.use = c(5:11), slot.name = "netP", legend.pos.x = 10)

NB: Please ignore the note when generating the plot such as “Note: The first link end is drawn out of sector ‘MIF’.”. If the gene names are overlapped, you can adjust the argument small.gap by decreasing the value.

Plot the signaling gene expression distribution using violin/dot plot

CellChat can plot the gene expression distribution of signaling genes related to L-R pairs or signaling pathways using a Seurat wrapper function plotGeneExpression if the Seurat R package has been installed. This function provides three types of visualizztion, including “violin”, “dot”, “bar”. Alternatively, users can extract the signaling genes related to the inferred L-R pairs or signaling pathway using extractEnrichedLR, and then plot gene expression using Seurat or other packages.

plotGeneExpression(cellchat, signaling = "CXCL", enriched.only = TRUE, type = "violin")
#> The legacy packages maptools, rgdal, and rgeos, underpinning the sp package,
#> which was just loaded, were retired in October 2023.
#> Please refer to R-spatial evolution reports for details, especially
#> https://r-spatial.org/r/2023/05/15/evolution4.html.
#> It may be desirable to make the sf package available;
#> package maintainers should consider adding sf to Suggests:.
#> Scale for y is already present.
#> Adding another scale for y, which will replace the existing scale.
#> Scale for y is already present.
#> Adding another scale for y, which will replace the existing scale.
#> Scale for y is already present.
#> Adding another scale for y, which will replace the existing scale.

print(as.numeric(execution.time, units = "secs"))
#> [1] 38.73308

By default, plotGeneExpression only shows the expression of signaling genes related to the inferred significant communications. USERS can show the expression of all signaling genes related to one signaling pathway by

plotGeneExpression(cellchat, signaling = "CXCL", enriched.only = FALSE)
execution.time = Sys.time() - ptm

Part IV: Systems analysis of cell-cell communication network

To facilitate the interpretation of the complex intercellular communication networks, CellChat quantitively measures networks through methods abstracted from graph theory, pattern recognition and manifold learning.

Identify signaling roles (e.g., dominant senders, receivers) of cell groups as well as the major contributing signaling

CellChat allows ready identification of dominant senders, receivers, mediators and influencers in the intercellular communication network by computing several network centrality measures for each cell group. Specifically, we used measures in weighted-directed networks, including out-degree, in-degree, flow betweenesss and information centrality, to respectively identify dominant senders, receivers, mediators and influencers for the intercellular communications. In a weighteddirected network with the weights as the computed communication probabilities, the outdegree, computed as the sum of communication probabilities of the outgoing signaling from a cell group, and the in-degree, computed as the sum of the communication probabilities of the incoming signaling to a cell group, can be used to identify the dominant cell senders and receivers of signaling networks, respectively. For the definition of flow betweenness and information centrality, please check our published paper and related reference.

Users can visualize the centrality scores on a heatmap (option A) and a 2D plot (option B). CellChat can also answer the question on which signals contribute the most to outgoing or incoming signaling of certain cell groups (option C).

(A) Compute and visualize the network centrality scores

ptm = Sys.time()
# Compute the network centrality scores
cellchat <- netAnalysis_computeCentrality(cellchat, slot.name = "netP") # the slot 'netP' means the inferred intercellular communication network of signaling pathways
# Visualize the computed centrality scores using heatmap, allowing ready identification of major signaling roles of cell groups
netAnalysis_signalingRole_network(cellchat, signaling = pathways.show, width = 8, height = 2.5, font.size = 10)

(B) Visualize dominant senders (sources) and receivers (targets) in a 2D space

CellChat also provides another intuitive way to visualize the dominant senders (sources) and receivers (targets) in a 2D space using scatter plot. 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.

# Signaling role analysis on the aggregated cell-cell communication network from all signaling pathways
gg1 <- netAnalysis_signalingRole_scatter(cellchat)
#> Signaling role analysis on the aggregated cell-cell communication network from all signaling pathways
# Signaling role analysis on the cell-cell communication networks of interest
gg2 <- netAnalysis_signalingRole_scatter(cellchat, signaling = c("CXCL", "CCL"))
#> Signaling role analysis on the cell-cell communication network from user's input
gg1 + gg2

(C) Identify signals contributing the most to outgoing or incoming signaling of certain cell groups

We can also answer the question on which signals contributing most to outgoing or incoming signaling of certain cell groups. 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.

# Signaling role analysis on the aggregated cell-cell communication network from all signaling pathways
ht1 <- netAnalysis_signalingRole_heatmap(cellchat, pattern = "outgoing")
ht2 <- netAnalysis_signalingRole_heatmap(cellchat, pattern = "incoming")
ht1 + ht2

# Signaling role analysis on the cell-cell communication networks of interest
ht <- netAnalysis_signalingRole_heatmap(cellchat, signaling = c("CXCL", "CCL"))

Identify global communication patterns to explore how multiple cell types and signaling pathways coordinate together

In addition to exploring detailed communications for individual pathways, an important question is how multiple cell groups and signaling pathways coordinate to function. CellChat employs a pattern recognition method to identify the global communication patterns.

As the number of patterns increases, there might be redundant patterns, making it difficult to interpret the communication patterns. We chose five patterns as default. Generally, it is biologically meaningful with the number of patterns greater than 2. In addition, we also provide a function selectK to infer the number of patterns, which is 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.

This analysis can be done for outgoing (option A) and incoming (option B) signaling patterns. Outgoing patterns reveal how the sender cells (i.e., cells as signal source) coordinate with each other as well as how they coordinate with certain signaling pathways to drive communication. Incoming patterns show how the target cells (i.e., cells as signal receivers) coordinate with each other as well as how they coordinate with certain signaling pathways to respond to incoming signals.

(A) Identify and visualize outgoing communication pattern of secreting cells

Outgoing patterns reveal how the sender cells (i.e. cells as signal source) coordinate with each other as well as how they coordinate with certain signaling pathways to drive communication.

For outgoing (or incoming) patterns, the cell group pattern matrix W outputted from the matrix factorization of outgoing (or incoming) cell-cell communication probability indicates how these cell groups coordinate to send (or receive) signals and the signaling pathway pattern matrix H indicates how these signaling pathways work together to send (or receive) signals. To intuitively show the associations of latent patterns with cell groups and ligand-receptor pairs or signaling pathways, we used a river (alluvial) plot. We first normalized each row of W and each column of H to be [0,1], and then set the elements in W and H to be zero if they are less than a threshold (by default: 0.5). Such thresholding allows to uncover the most enriched cell groups and signaling pathways associated with each inferred pattern. These thresholded matrices W and H are used as inputs for creating an alluvial plot.

Moreover, to directly relate cell groups with their enriched signaling pathways, we set the elements in W and H to be zero if they are less than a threshold (by default: 1/R) where R is the number of latent patterns. By using a less strict threshold, more enriched signaling pathways associated each cell group might be obtained. Using a contribution score of each cell group to each signaling pathway computed by multiplying W by H, 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. Users can also decrease the parameter cutoff to show more enriched signaling pathways associated each cell group.

Load required package for the communication pattern analysis

library(NMF)
#> Loading required package: registry
#> Loading required package: rngtools
#> Loading required package: cluster
#> NMF - BioConductor layer [OK] | Shared memory capabilities [NO: bigmemory] | Cores 2/2
#>   To enable shared memory capabilities, try: install.extras('
#> NMF
#> ')
#> 
#> Attaching package: 'NMF'
#> The following objects are masked from 'package:igraph':
#> 
#>     algorithm, compare
library(ggalluvial)

Here we run selectK to infer the number of patterns.

selectK(cellchat, pattern = "outgoing")

Both Cophenetic and Silhouette values begin to drop suddenly when the number of outgoing patterns is 6.

nPatterns = 6
cellchat <- identifyCommunicationPatterns(cellchat, pattern = "outgoing", k = nPatterns)

# river plot
netAnalysis_river(cellchat, pattern = "outgoing")
#> Please make sure you have load `library(ggalluvial)` when running this function

# dot plot
netAnalysis_dot(cellchat, pattern = "outgoing")

(B) Identify and visualize incoming communication pattern of target cells

Incoming patterns show how the target cells (i.e. cells as signal receivers) coordinate with each other as well as how they coordinate with certain signaling pathways to respond to incoming signals.

selectK(cellchat, pattern = "incoming")

Cophenetic values begin to drop when the number of incoming patterns is 3.

nPatterns = 3
cellchat <- identifyCommunicationPatterns(cellchat, pattern = "incoming", k = nPatterns)

# river plot
netAnalysis_river(cellchat, pattern = "incoming")
#> Please make sure you have load `library(ggalluvial)` when running this function

# dot plot
netAnalysis_dot(cellchat, pattern = "incoming")

Manifold and classification learning analysis of signaling networks

Further, CellChat is able to quantify the similarity between all significant signaling pathways and then group them based on their cellular communication network similarity. Grouping can be done either based on the functional or structural similarity.

Functional similarity: High degree of functional similarity indicates major senders and receivers are similar, and it can be interpreted as the two signaling pathways or two ligand-receptor pairs exhibit similar and/or redundant roles. The functional similarity analysis requires the same cell population composition between two datasets.

Structural similarity: A structural similarity was used to compare their signaling network structure, without considering the similarity of senders and receivers.

Identify signaling groups based on their functional similarity

cellchat <- computeNetSimilarity(cellchat, type = "functional")
cellchat <- netEmbedding(cellchat, type = "functional")
#> Manifold learning of the signaling networks for a single dataset
cellchat <- netClustering(cellchat, type = "functional")
#> Classification learning of the signaling networks for a single dataset
# Visualization in 2D-space
netVisual_embedding(cellchat, type = "functional", label.size = 3.5)

# netVisual_embeddingZoomIn(cellchat, type = "functional", nCol = 2)

Identify signaling groups based on structure similarity

cellchat <- computeNetSimilarity(cellchat, type = "structural")
cellchat <- netEmbedding(cellchat, type = "structural")
#> Manifold learning of the signaling networks for a single dataset
cellchat <- netClustering(cellchat, type = "structural")
#> Classification learning of the signaling networks for a single dataset
# Visualization in 2D-space
netVisual_embedding(cellchat, type = "structural", label.size = 3.5)

netVisual_embeddingZoomIn(cellchat, type = "structural", nCol = 2)

execution.time = Sys.time() - ptm
print(as.numeric(execution.time, units = "secs"))
#> [1] 147.8175

Part V: Save the CellChat object

saveRDS(cellchat, file = "cellchat_humanSkin_LS.rds")

Part VI: Explore the cell-cell communication through the Interactive CellChat Explorer

For CellChat analysis of single-cell transcriptomics, please make sure the object@dr contains a low-dimensional space of the data such as “umap” and “tsne” in order to produce the feature plot of signaling genes. A new reduced space can be add via the function addReduction.

runCellChatApp(cellchat)
sessionInfo()
#> R version 4.3.1 (2023-06-16)
#> Platform: aarch64-apple-darwin20 (64-bit)
#> Running under: macOS Ventura 13.5
#> 
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0
#> 
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#> 
#> time zone: Asia/Shanghai
#> tzcode source: internal
#> 
#> attached base packages:
#> [1] parallel  stats     graphics  grDevices utils     datasets  methods  
#> [8] base     
#> 
#> other attached packages:
#>  [1] doParallel_1.0.17   iterators_1.0.14    foreach_1.5.2      
#>  [4] ggalluvial_0.12.5   NMF_0.26            cluster_2.1.4      
#>  [7] rngtools_1.5.2      registry_0.5-1      patchwork_1.1.3    
#> [10] CellChat_2.1.2      Biobase_2.60.0      BiocGenerics_0.46.0
#> [13] ggplot2_3.4.3       igraph_1.5.1        dplyr_1.1.3        
#> 
#> loaded via a namespace (and not attached):
#>   [1] RcppAnnoy_0.0.21       splines_4.3.1          later_1.3.1           
#>   [4] tibble_3.2.1           polyclip_1.10-4        ggnetwork_0.5.12      
#>   [7] fastDummies_1.7.3      lifecycle_1.0.3        rstatix_0.7.2.999     
#>  [10] rprojroot_2.0.3        globals_0.16.2         lattice_0.21-8        
#>  [13] MASS_7.3-60            backports_1.4.1        magrittr_2.0.3        
#>  [16] plotly_4.10.2          sass_0.4.7             rmarkdown_2.24        
#>  [19] jquerylib_0.1.4        yaml_2.3.7             httpuv_1.6.11         
#>  [22] Seurat_5.0.1           sctransform_0.4.1      spam_2.9-1            
#>  [25] spatstat.sparse_3.0-2  sp_2.1-0               reticulate_1.31       
#>  [28] cowplot_1.1.1          pbapply_1.7-2          RColorBrewer_1.1-3    
#>  [31] abind_1.4-5            Rtsne_0.16             purrr_1.0.2           
#>  [34] presto_1.0.0           circlize_0.4.16        IRanges_2.34.1        
#>  [37] S4Vectors_0.38.1       ggrepel_0.9.3          irlba_2.3.5.1         
#>  [40] spatstat.utils_3.0-3   listenv_0.9.0          goftest_1.2-3         
#>  [43] RSpectra_0.16-1        spatstat.random_3.1-5  fitdistrplus_1.1-11   
#>  [46] parallelly_1.36.0      svglite_2.1.1          leiden_0.4.3          
#>  [49] codetools_0.2-19       tidyselect_1.2.0       shape_1.4.6           
#>  [52] farver_2.1.1           matrixStats_1.0.0      stats4_4.3.1          
#>  [55] spatstat.explore_3.2-1 jsonlite_1.8.7         GetoptLong_1.0.5      
#>  [58] BiocNeighbors_1.18.0   ellipsis_0.3.2         progressr_0.14.0      
#>  [61] ggridges_0.5.4         survival_3.5-7         systemfonts_1.0.4     
#>  [64] tools_4.3.1            ragg_1.2.5             sna_2.7-1             
#>  [67] ica_1.0-3              Rcpp_1.0.11            glue_1.6.2            
#>  [70] gridExtra_2.3          here_1.0.1             xfun_0.40             
#>  [73] withr_2.5.0            BiocManager_1.30.22    fastmap_1.1.1         
#>  [76] fansi_1.0.4            digest_0.6.33          R6_2.5.1              
#>  [79] mime_0.12              textshaping_0.3.6      colorspace_2.1-0      
#>  [82] scattermore_1.2        tensor_1.5             spatstat.data_3.0-1   
#>  [85] utf8_1.2.3             tidyr_1.3.0            generics_0.1.3        
#>  [88] data.table_1.14.9      FNN_1.1.3.2            httr_1.4.7            
#>  [91] htmlwidgets_1.6.2      uwot_0.1.16            pkgconfig_2.0.3       
#>  [94] gtable_0.3.4           ComplexHeatmap_2.15.4  lmtest_0.9-40         
#>  [97] htmltools_0.5.6        carData_3.0-5          dotCall64_1.0-2       
#> [100] clue_0.3-64            SeuratObject_5.0.1     scales_1.2.1          
#> [103] tidyverse_2.0.0        png_0.1-8              knitr_1.43            
#> [106] rstudioapi_0.15.0      reshape2_1.4.4         rjson_0.2.21          
#> [109] nlme_3.1-163           coda_0.19-4            statnet.common_4.9.0  
#> [112] cachem_1.0.8           zoo_1.8-12             GlobalOptions_0.1.2   
#> [115] stringr_1.5.0          KernSmooth_2.23-22     miniUI_0.1.1.1        
#> [118] pillar_1.9.0           grid_4.3.1             vctrs_0.6.3           
#> [121] RANN_2.6.1             promises_1.2.1         ggpubr_0.6.0          
#> [124] car_3.1-2              xtable_1.8-4           evaluate_0.21         
#> [127] magick_2.8.1           cli_3.6.1              compiler_4.3.1        
#> [130] rlang_1.1.1            crayon_1.5.2           future.apply_1.11.0   
#> [133] ggsignif_0.6.4         labeling_0.4.3         plyr_1.8.8            
#> [136] stringi_1.7.12         deldir_1.0-9           viridisLite_0.4.2     
#> [139] network_1.18.1         gridBase_0.4-7         BiocParallel_1.34.2   
#> [142] munsell_0.5.0          lazyeval_0.2.2         spatstat.geom_3.2-4   
#> [145] Matrix_1.6-5           RcppHNSW_0.5.0         future_1.33.0         
#> [148] shiny_1.7.5            highr_0.10             ROCR_1.0-11           
#> [151] broom_1.0.5            bslib_0.5.1