【cellchat】Inference and analysis of cell-cell communication-基础篇，一个condition

cellchat需要两个输入：基因表达矩阵 + cell label。表达矩阵行为基因列为细胞，标准化+log。cell label为dataframe,行名为细胞，第一列为cell label.

从seurat v3 object中提取cell chat 所需要的两个输入文件：

data.input <- GetAssayData(seurat_object, assay = "RNA", slot = "data") # normalized data matrix
labels <- Idents(seurat_object)
meta <- data.frame(group = labels, row.names = names(labels)) # create a dataframe of the cell labels

library(CellChat)
library(patchwork)
options(stringsAsFactors = FALSE)

 

• part I: data input and preprocessing, initialization of the cellchat object 

（1）load data

# Here we load a scRNA-seq data matrix and its associated cell meta data
#load(url("https://ndownloader.figshare.com/files/25950872")) 
# This is a combined data from two biological conditions: normal and diseases

load("/Users/suoqinjin/Documents/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

# Prepare 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

 （2）create a cellchat object

cellchat <- createCellChat(object = data.input, meta = meta, group.by = "labels")

（3）set the ligand-receptor interaction database

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

# Show the structure of the database
dplyr::glimpse(CellChatDB$interaction)

# 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

 （4）preprocessing the gene expression data

# 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("multiprocess", workers = 4) # do parallel

cellchat <- identifyOverExpressedGenes(cellchat)
cellchat <- identifyOverExpressedInteractions(cellchat)
# project gene expression data onto PPI network (optional)
cellchat <- projectData(cellchat, PPI.human)

 

•  Part II: Inference of cell-cell communication network

在核心功能computeCommuProb中，可以设置不同的阈值。默认为trimean(25%),也可以设置成10%、5%等。这个阈值什么意思？当某个cell group中表达某个pair的细胞比例低于此值时，他们的平均表达量就都是零（不会出现在结果里），调低阈值能发现更多通讯，但相应的噪音也更大。调整方法：type = "truncatedMean", trim = 0.1

（1）compute the communication probability and infer the celluar communication network 

cellchat <- computeCommunProb(cellchat)

# Filter out the cell-cell communication if there are only few number of cells in certain cell groups
cellchat <- filterCommunication(cellchat, min.cells = 10)

（2） extract the infered celluar communication network as a dataframe

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.

（3）infer the communication network at a signaling pathway level

注：前面以pair为单位的结果存储在“net” slot, 而pathway推断结果在“netP”slot.

cellchat <- computeCommunProbPathway(cellchat)

（4）calculate the aggregated communication network

USER can also calculate the aggregated network among a subset of cell groups by setting sources.use and targets.use.

cellchat <- aggregateNet(cellchat)

两种可视化思路：一种线条粗细反应数量，一种反映weights.

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")

 可视化从每种细胞类型发出的

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 the communication network

（1）visualize each signaling pathway 

所有显著的通讯通路都可以通过cellchat@netP$pathways查看

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")
#> Note: The first link end is drawn out of sector 'Inflam. FIB'.

# Heatmap
par(mfrow=c(1,1))
netVisual_heatmap(cellchat, signaling = pathways.show, color.heatmap = "Reds")

 

 

（2）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 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)

# Circle plot
netVisual_individual(cellchat, signaling = pathways.show, pairLR.use = LR.show, layout = "circle")

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

 （3）visualize cell communications by multiple L-R pairs or pathways

气泡图

# 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"), remove.isolate = FALSE)

# 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)

 和弦图（和bubble图一样，既可以展示用户指定的target与sender之间的通讯关系，也可以展示用户指定的pair和pathway）

# 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)

 （4）plot the signaling gene expression distrobution by dot or violin plots

#来自seurat的wrapper功能

plotGeneExpression(cellchat, signaling = "CXCL")

 

• part IV : systems analysis of cell-cell communication network

这是什么意思呢？

a. 根据已经构建好的通讯网络，发现其中的major sources, targets, influencers, mediators.

b. 对某些细胞类型，预测其关键的incoming and outgoing signals，包括coordinated responses among different cell types

c. group signaling pathways

d. delineate conserved and context-specific signaling pathways

（1）identify signaling roles of cell groups as well as the major contributing signaling (dominant senders and receivers)

# 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)

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

# Signaling role analysis on the aggregated cell-cell communication network from all signaling pathways

gg1 <- netAnalysis_signalingRole_scatter(cellchat)

# Signaling role analysis on the cell-cell communication networks of interest

gg2 <- netAnalysis_signalingRole_scatter(cellchat, signaling = c("CXCL", "CCL"))

gg1 + gg2

 identify signals contributing most to the incoming and outgoing signalings of certain cell types

# 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"))

 

（2）identify global communication patterns to explore how multiple cell types and signaling pathways coordinate together

default patterns number: 5；可以用select K功能寻找最佳K值，选当曲线开始突然下降时的K。

identify outgoing communication patterns of secreting cells

library(NMF)
library(ggalluvial)

selectK(cellchat, pattern = "outgoing")

 k选择3

nPatterns = 3

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

 

# river plot
netAnalysis_river(cellchat, pattern = "outgoing")

 

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

 

identify incoming communication patterns of target cells功能都一样，把outgoing改成incoming就行

（3）manifold and classication learning analysis of signaling networks

可以对所有显著的signaling pathway进行grouping分析，依据通路之间的相似性，这种相似性既可以是功能上的“functional”,也可以是结构上的“structural”.

functional similarity: 主要的sender和receiver相同

structural similarity: 网络的结构相似，不考虑sender和receiver的相似性

identify signaling groups based on their functional similarity

cellchat <- computeNetSimilarity(cellchat, type = "functional")
cellchat <- netEmbedding(cellchat, type = "functional")


cellchat <- netClustering(cellchat, type = "functional")

# 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")

cellchat <- netClustering(cellchat, type = "structural")

# Visualization in 2D-space
netVisual_embedding(cellchat, type = "structural", label.size = 3.5)

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

 

 

• part V : save the cellchat object

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

这篇文章是关于单个dataset单个条件的,如果涉及到代表不同conditions的datasets 之间的差异分析，可以看另一个教程。

GitHub & BitBucket HTML Preview