CellChat is an R package designed for inference, analysis, and visualization of cell-cell communication from single-cell and spatially resolved transcriptomics. CellChat aims to enable users to identify and interpret cell-cell communication within an easily interpretable framework, with the emphasis of clear, attractive, and interpretable visualizations.
More information can be found at https:/jinworks/CellChat
Tested on Windows 11 12th Gen Intel(R) i7-1265U with R version 4.2.1.
CellChat R package can be easily installed from Github using devtools:
devtools::install_github("jinworks/CellChat")
Please make sure you have installed the correct version of NMF and circlize package. See instruction below.
If you run into issues when when installing dependencies (NMF, circlize, ComplexHeatmap, and UMAP-learn), please follow the troubleshooting instructions found here.
Some users might have issues when installing CellChat pacakge due to different operating systems and new R version. Please check the following solutions:
- Installation on Mac OX with R > 3.6: Please re-install Xquartz.
- Installation on Windows, Linux and Centos: Please check the solution for Windows and Linux.
First, Prepare required input data for CellChat analysis. Input data can be downloaded from the following link here.
First, load in the nessecary R libraries.
library(CellChat)
library(patchwork)
options(stringsAsFactors = FALSE)
# reticulate::use_python("/Users/suoqinjin/anaconda3/bin/python", required=T)
Here we load the scRNA-seq data matrix and its associated cell meta data.
ptm = Sys.time()
# This is a combined data from two biological conditions: normal and diseases
load("data/data_humanSkin_CellChat.rda")
# Normalized data matrix
data.input = data_humanSkin$data
# A dataframe with rownames containing cell mata data
meta = data_humanSkin$meta
# Extract the cell names from disease data
cell.use = rownames(meta)[meta$condition == "LS"]
# Subset the input data for CelChat analysis
data.input = data.input[, cell.use]
meta = meta[cell.use, ]
# Check the cell labels
unique(meta$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
There are many ways to initialize the CellChat object (data matrix, Seurat, SingleCellExperiment or AnnData object), but in this case, we will be starting from a count data matrix.
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
Before we can employ CellChat to infer cell-cell communication, the ligand-receptor interaction database needs to be set to identify over-expressed ligands or receptors.
# When analyzing human samples, we use the database 'CellChatDB.human'
CellChatDB <- CellChatDB.human
showDatabaseCategory(CellChatDB)
# Use a subset of CellChatDB for cell-cell communication analysis
CellChatDB.use <- subsetDB(CellChatDB, search = "Secreted Signaling", key = "annotation") # use Secreted Signaling
# Set the used database in the object
cellchat@DB <- CellChatDB.use
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.
# Subset the expression data of signaling genes for saving computation cost. This step is necessary even if using the whole database
cellchat <- subsetData(cellchat)
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
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.
ptm = Sys.time()
cellchat <- computeCommunProb(cellchat, type = "triMean")
# Filter out the cell-cell communication if there are only few cells in certain cell groups
cellchat <- filterCommunication(cellchat, min.cells = 10)
CellChat computes the communication probability on signaling pathway level by summarizing the communication probabilities of all ligands-receptors interactions associated with each signaling pathway.
cellchat <- computeCommunProbPathway(cellchat)
CellChat calculates the aggregated cell-cell communication network by counting the number of links or summarizing the communication probability.
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")
Upon infering the cell-cell communication network, CellChat provides various functionality for further data exploration, analysis, and visualization.
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
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 and netVisual_chord_gene.
In the bubble plot below, we show all the significant interactions (L-R pairs) from some cell groups (defined by 'sources.use') to other cell groups (defined by 'targets.use')
# Bubble Plot
netVisual_bubble(cellchat, sources.use = 4, targets.use = c(5:11), remove.isolate = FALSE)
> Comparing communications on a single object
In the chord diagram, we do the same as above, showing all the significant interactions (L-R pairs) from some cell groups (defined by 'sources.use') to other cell groups (defined by 'targets.use') Here, we show all the interactions sending from Inflam.FIB
# Chord diagram
netVisual_chord_gene(cellchat, sources.use = 4, targets.use = c(5:11), lab.cex = 0.5,legend.pos.y = 30)
Additionally, we can 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)
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.
plotGeneExpression(cellchat, signaling = "CXCL", enriched.only = TRUE, type = "violin")
- Suoqin Jin et al., CellChat for systematic analysis of cell-cell communication from single-cell and spatially resolved transcriptomics, bioRxiv 2023 [CellChat v2]
- Suoqin Jin et al., Inference and analysis of cell-cell communication using CellChat, Nature Communications 2021 [CellChat v1] Citation:1717
- https:/jinworks/CellChat
- https://figshare.com/articles/dataset/scRNA-seq_data_of_human_skin_from_patients_with_atopic_dermatitis/24470719
- https://htmlpreview.github.io/?https:/jinworks/CellChat/blob/master/tutorial/CellChat-vignette.html
- https://htmlpreview.github.io/?https:/jinworks/CellChat/blob/master/tutorial/CellChat_analysis_of_spatial_transcriptomics_data.html