Inference and analysis of cell-cell communication using CellChat
IDEA Alzheimer’s Data Analysis Bootcamp - Summer 2022
Haowen He
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 either user assigned cell labels (i.e., label-based mode) or a low-dimensional representation of the single-cell data (i.e., label-free mode). For the latter, CellChat automatically groups cells by building a shared neighbor graph based on the cell-cell distance in the low-dimensional space or the pseudotemporal trajectory space.
Load data and create a CellChat object
For the gene expression data matrix, genes should be in rows with rownames and cells in columns with colnames. If input is a Seurat object, we can either use the default cell identities of Seurat object, or use other meta information as cell groups.
# Data pre-processing
seurat.all <- readRDS("FinalMergedData-downsampled.rds")
# Create cellchat object
cellchat <- createCellChat(object = seurat.all, group.by = "ident", assay = "RNA")## Create a CellChat object from a Seurat object## The `meta.data` slot in the Seurat object is used as cell meta information## Set cell identities for the new CellChat object## The cell groups used for CellChat analysis are Ast End ExDp1 ExDp2 ExM ExM-U ExN Glia IP InCGE InMGE OPC PgG2M PgS UN oRG vRGSet the ligand-receptor interaction database
The database CellChatDB is a manually curated database of literature-supported ligand-receptor interactions in both human and mouse. CellChatDB in mouse contains 2,021 validated molecular interactions, including 60% of secrete autocrine/paracrine signaling interactions, 21% of extracellular matrix (ECM)-receptor interactions and 19% of cell-cell contact interactions. CellChatDB in human contains 1,939 validated molecular interactions, including 61.8% of paracrine/autocrine signaling interactions, 21.7% of extracellular matrix (ECM)-receptor interactions and 16.5% of cell-cell contact interactions.
CellChatDB <- CellChatDB.human # use CellChatDB.mouse if running on mouse data
showDatabaseCategory(CellChatDB)
# Show the structure of the database
dplyr::glimpse(CellChatDB$interaction)## Rows: 1,939
## Columns: 11
## $ interaction_name <chr> "TGFB1_TGFBR1_TGFBR2", "TGFB2_TGFBR1_TGFBR2", "TGFB…
## $ pathway_name <chr> "TGFb", "TGFb", "TGFb", "TGFb", "TGFb", "TGFb", "TG…
## $ ligand <chr> "TGFB1", "TGFB2", "TGFB3", "TGFB1", "TGFB1", "TGFB2…
## $ receptor <chr> "TGFbR1_R2", "TGFbR1_R2", "TGFbR1_R2", "ACVR1B_TGFb…
## $ agonist <chr> "TGFb agonist", "TGFb agonist", "TGFb agonist", "TG…
## $ antagonist <chr> "TGFb antagonist", "TGFb antagonist", "TGFb antagon…
## $ co_A_receptor <chr> "", "", "", "", "", "", "", "", "", "", "", "", "",…
## $ co_I_receptor <chr> "TGFb inhibition receptor", "TGFb inhibition recept…
## $ evidence <chr> "KEGG: hsa04350", "KEGG: hsa04350", "KEGG: hsa04350…
## $ annotation <chr> "Secreted Signaling", "Secreted Signaling", "Secret…
## $ interaction_name_2 <chr> "TGFB1 - (TGFBR1+TGFBR2)", "TGFB2 - (TGFBR1+TGFBR2)…# use a subset of CellChatDB for cell-cell communication analysis
# CellChatDB.use <- subsetDB(CellChatDB, search = "Secreted Signaling") # use Secreted Signaling
# use all CellChatDB for cell-cell communication analysis
CellChatDB.use <- CellChatDB # simply use the default CellChatDB
# set the used database in the object
cellchat@DB <- CellChatDB.use
cellchat <- subsetData(cellchat) # subset the expression data of signaling genes for saving computation cost
future::plan("multiprocess", workers = 4) # do parallel## Warning: Strategy 'multiprocess' is deprecated in future (>= 1.20.0)
## [2020-10-30]. Instead, explicitly specify either 'multisession' (recommended) or
## 'multicore'. In the current R session, 'multiprocess' equals 'multisession'.## Warning in supportsMulticoreAndRStudio(...): [ONE-TIME WARNING] Forked
## processing ('multicore') is not supported when running R from RStudio
## because it is considered unstable. For more details, how to control forked
## processing or not, and how to silence this warning in future R sessions, see ?
## parallelly::supportsMulticorePart 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.
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.
Compute the communication probability and infer cellular communication network
cellchat <- identifyOverExpressedGenes(cellchat)
cellchat <- identifyOverExpressedInteractions(cellchat)
cellchat <- projectData(cellchat, PPI.human)
cellchat <- computeCommunProb(cellchat, raw.use = TRUE)
# Filter out the cell-cell communication if there are only few number of cells in certain cell groups
cellchat <- filterCommunication(cellchat, min.cells = 10)
cellchat <- computeCommunProbPathway(cellchat)
cellchat <- aggregateNet(cellchat)
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")
mat <- cellchat@net$weight
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.
It provides several ways for visualizing cell-cell communication network, including hierarchical plot, circle plot, Chord diagram, and bubble plot.
It provides an easy-to-use tool for extracting and visualizing high-order information of the inferred networks. For example, it allows ready prediction of major signaling inputs and outputs for cell populations and how these populations and signals coordinate together for functions.
It can quantitatively characterize and compare the inferred cell-cell communication networks using an integrated approach by combining social network analysis, pattern recognition, and manifold learning approaches.
Visualize each signaling pathway using Hierarchy plot, Circle plot or Chord diagram
pathways.show <- c("NCAM")
par(mfrow=c(1,1))
netVisual_aggregate(cellchat, signaling = pathways.show, layout = "circle")
vertex.receiver = seq(1,4)
netVisual_aggregate(cellchat, signaling = pathways.show, vertex.receiver = vertex.receiver, layout = "hierarchy")
par(mfrow=c(1,1))
netVisual_aggregate(cellchat, signaling = pathways.show, layout = "chord")
par(mfrow=c(1,1))
netVisual_heatmap(cellchat, signaling = pathways.show, color.heatmap = "Reds")## Do heatmap based on a single object