library(CellChat)
library(patchwork)
library(Seurat)
options(Seurat.object.assay.version = "v3")
library(SeuratData)

# Load data
load("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

## Create a CellChat object
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

## Add cell information into meta slot of the object (Optional)
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
##  [1] "APOE+ FIB"    "FBN1+ FIB"    "COL11A1+ FIB" "Inflam. FIB"  "cDC1"        
##  [6] "cDC2"         "LC"           "Inflam. DC"   "TC"           "Inflam. TC"  
## [11] "CD40LG+ TC"   "NKT"
groupSize <- as.numeric(table(cellchat@idents))  # number of cells in each cell group

## Set the ligand-receptor interaction database
CellChatDB <- CellChatDB.human  # use CellChatDB.mouse if running on mouse data

##
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)…
CellChatDB.use <- subsetDB(CellChatDB, search = "Secreted Signaling")  # use Secreted Signaling
# set the used database in the object
cellchat@DB <- CellChatDB.use

## Preprocessing the expression data for cell-cell communication analysis
## 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("multicore", workers = 1)  # do parallel
cellchat <- identifyOverExpressedGenes(cellchat)
cellchat <- identifyOverExpressedInteractions(cellchat)
# 计算通信概率，推断细胞通信网络
cellchat <- computeCommunProb(cellchat)
## triMean is used for calculating the average gene expression per cell group. 
## [1] ">>> Run CellChat on sc/snRNA-seq data <<< [2023-05-15 13:29:47]"
## [1] ">>> CellChat inference is done. Parameter values are stored in `object@options$parameter` <<< [2023-05-15 13:32:15]"
cellchat <- filterCommunication(cellchat, min.cells = 10)
# 在信号通路水平推断细胞间的通讯
cellchat <- computeCommunProbPathway(cellchat)

## Calculate the aggregated cell-cell communication network
cellchat <- aggregateNet(cellchat)
groupSize <- as.numeric(table(cellchat@idents))

细胞间通信网络系统分析

识别细胞群的信号作用以及主要的信号作用

CellChat通过计算每个细胞群的几个网络中心性度量，可以随时识别细胞间通信网络中的主要发送者、接收者、中介者和影响者。具体而言，我们在加权定向网络中使用了度量，包括出度、入度、流间性和信息中心性，分别确定了细胞间通信的主要发送者、接收者、中介者和影响者。在权重为计算的通信概率的加权有向网络中，输出度(计算为从一个细胞组发出的信令的通信概率的总和)和输入度(计算为从一个细胞组传入的信令的通信概率的总和)可分别用于识别信令网络中占优势的细胞发送者和接收器。

pathways.show <- c("CXCL")
# 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)

在二维空间中可视化主要的发送者(源)和接收者(目标)

# 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

识别对某些细胞群的输出或输入信号贡献最大的信号

# 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

识别和可视化分泌细胞的外向通信模式

library(NMF)
library(ggalluvial)
selectK(cellchat, pattern = "outgoing")

当输出模式数为3时，Cophenetic值和Silhouette值都开始突然下降。

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

river plot

netAnalysis_river(cellchat, pattern = "outgoing")

dot plot

netAnalysis_dot(cellchat, pattern = "outgoing")

识别和可视化目标细胞的传入通信模式

selectK(cellchat, pattern = "incoming")

当输入模式数为4时，相干值开始下降。

nPatterns = 4
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")

信令网络的流形与分类学习分析

此外，CellChat能够量化所有重要信号通路之间的相似性，然后根据它们的细胞通信网络相似性对它们进行分组。可以根据功能或结构相似性进行分组。

功能相似性: 高度的功能相似性表明主要的发送者和接受者是相似的，这可以解释为两个信号通路或两个配体受体对表现出相似和/或冗余的作用。功能相似性分析要求两个数据集之间的细胞种群组成相同。

结构相似性: 使用结构相似性来比较它们的信令网络结构，而不考虑发送方和接收方的相似性。

基于功能相似性来识别信号组

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

基于结构相似性识别信令组

cellchat <- computeNetSimilarity(cellchat, type = "structural")
cellchat <- netEmbedding(cellchat, type = "structural")
## Manifold learning of the signaling networks for a single dataset
# > Manifold learning of the signaling networks for a single dataset
cellchat <- netClustering(cellchat, type = "structural", do.parallel = FALSE)
## Classification learning of the signaling networks for a single dataset
# > 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)

Reference

Suoqin Jin et al., Inference and analysis of cell-cell communication using CellChat. Nature Communications, 12:1088 (2021). https://www.nature.com/articles/s41467-021-21246-9

这个软件包代码量还是很多的，需要具有一定 R 语言编程基础，并不是看起来那么简单，所有好多老师想直接自己学习教程来分析，但是实质上没有基础还是很难实现，每步报错都不知道该怎样处理，是最崩溃的！！！

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SCS【26】单细胞细胞间通信第二部分通信网络的系统分析（Cel

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