CellChat简介
CellChat的特点
全面的数据：大多数的细胞通讯分析方法通常只考虑配体/受体基因对，往往忽略了多亚基复合物受体和其他信号辅助因子。CellChat的作者人工挑选了2021个经过验证的细胞通讯关系，构建了新的细胞通讯参考数据库——CellChatDB。它不仅考虑了多亚基受体情况，还收录了其他重要的信号辅助因子：可溶性激动剂，拮抗剂，共刺激和共抑制膜结合受体。 高深的算法：CellChat在分析过程中使用了多种分析方法，不仅有常见的秩和检验、置换检验、SNN、KNN、UMAP，还有非负矩阵分解、社会网络分析、质量作用定律等不常用的方法。能灵活运用这么多方法分析数据，足以说明作者具有深厚的算法功底。 丰富的可视化结果：CellChat提供丰富且美观的可视化结果，有网络图、桑基图、热图、气泡图、散点图等多种图形。
安装CellChat
安装依赖项
## R环境安装NMF和ComplexHeatmap包
devtools::install_github("renozao/NMF@devel")
devtools::install_github("jokergoo/ComplexHeatmap")
## shell环境安装
pip install umap-learn #如果报错，可使用conda安装
#conda install umap-learn安装CellChat
devtools::install_github("sqjin/CellChat")CellChat实践
数据来源
为了方便大家对比不同的分析方法，我们继续使用《单细胞分析之NicheNet》的数据。数据下载链接：https://zenodo.org/record/3531889/files/seuratObj.rds
初始分析
library(CellChat)
library(tidyverse)
library(ggalluvial)
rm(list=ls())
options(stringsAsFactors = FALSE)
##提取表达矩阵和细胞分类信息
scRNA <- readRDS(url("https://zenodo.org/record/3531889/files/seuratObj.rds"))
scRNA <- UpdateSeuratObject(scRNA)
# CellChat要求输入标准化后的表达数据
data.input <- GetAssayData(scRNA, assay = "RNA", slot = "data")
identity <- subset(scRNA@meta.data, select = "celltype")
##创建cellchat对象
cellchat <- createCellChat(data = data.input)
cellchat <- addMeta(cellchat, meta = identity, meta.name = "labels")
cellchat <- setIdent(cellchat, ident.use = "labels")
groupSize <- as.numeric(table(cellchat@idents)) # 后面有用
##设置参考数据库
# 选择合适的物种，可选CellChatDB.human, CellChatDB.mouse
CellChatDB <- CellChatDB.mouse
# 使用"Secreted Signaling"用于细胞通讯分析
CellChatDB.use <- subsetDB(CellChatDB, search = "Secreted Signaling")
# 将数据库传递给cellchat对象
cellchat@DB <- CellChatDB.use
##配体-受体分析
# 提取数据库支持的数据子集
cellchat <- subsetData(cellchat)
# 识别过表达基因
cellchat <- identifyOverExpressedGenes(cellchat)
# 识别配体-受体对
cellchat <- identifyOverExpressedInteractions(cellchat)
# 将配体、受体投射到PPI网络
cellchat <- projectData(cellchat, PPI.mouse)推断细胞通讯网络
##推测细胞通讯网络 cellchat <- computeCommunProb(cellchat) cellchat <- computeCommunProbPathway(cellchat) cellchat <- aggregateNet(cellchat)
细胞通讯网络系统分析及可视化
levels(cellchat@idents) #查看细胞顺序 vertex.receiver = c(3, 6) #指定靶细胞的索引 cellchat@netP$pathways #查看富集到的信号通路 pathways.show <- "CCL" #指定需要展示的通路 # Hierarchy plot png(filename = "sig_pathway_hierarchy.png", width = 1000, height = 650) netVisual_aggregate(cellchat, signaling = pathways.show, vertex.receiver = vertex.receiver, vertex.size = groupSize) dev.off() # Circle plot png(filename = "sig_pathway_cricle.png", width = 650, height = 600) netVisual_aggregate(cellchat, signaling = pathways.show, layout = "circle", vertex.size = groupSize) dev.off() # 计算配体-受体对信号网络的贡献度 png(filename = "sig_pathway_L-R.png", width = 800, height = 600) netAnalysis_contribution(cellchat, signaling = pathways.show) dev.off() # 分析细胞在信号网络中角色 cellchat <- netAnalysis_signalingRole(cellchat, slot.name = "netP") png(filename = "sig_pathway_role.png", width = 800, height = 600) netVisual_signalingRole(cellchat, signaling = pathways.show) dev.off()
sig_pathway_hierarchy
sig_pathway_cricle
sig_pathway_L-R
sig_pathway_role
##细胞通讯模式和信号网络
nPatterns = 5 #默认为5
cellchat <- identifyCommunicationPatterns(cellchat, pattern = "outgoing", k = nPatterns)
# river plot
p = netAnalysis_river(cellchat, pattern = "outgoing")
ggsave("com_pattern_outgoing_river.png", p, width = 12, height = 6)
# dot plot
p = netAnalysis_dot(cellchat, pattern = "outgoing")
ggsave("com_pattern_outgoing_dot.png", p, width = 9, height = 6)
nPatterns = 5
cellchat <- identifyCommunicationPatterns(cellchat, pattern = "incoming", k = nPatterns)
# river plot
p = netAnalysis_river(cellchat, pattern = "incoming")
ggsave("com_pattern_incoming_river.png", p, width = 12, height = 6)
# dot plot
p = netAnalysis_dot(cellchat, pattern = "incoming")
ggsave("com_pattern_incoming_dot.png", p, width = 9, height = 6)com_pattern_outgoing_river
com_pattern_outgoing_dot
com_pattern_incoming_river
om_pattern_incoming_dot
##信号网络聚类
# 按功能相似性聚类
cellchat <- computeNetSimilarity(cellchat, type = "functional")
cellchat <- netEmbedding(cellchat, type = "functional")
cellchat <- netClustering(cellchat, type = "functional")
# Visualization in 2D-space
p = netVisual_embedding(cellchat, type = "functional")
ggsave("custer_pathway_function.png", p, width = 9, height = 6)
p = netVisual_embeddingZoomIn(cellchat, type = "functional")
ggsave("custer_pathway_function2.png", p, width = 8, height = 6)
# 按结构相似性聚类
cellchat <- computeNetSimilarity(cellchat, type = "structural")
cellchat <- netEmbedding(cellchat, type = "structural")
cellchat <- netClustering(cellchat, type = "structural")
# Visualization in 2D-space
p = netVisual_embedding(cellchat, type = "structural")
ggsave("custer_pathway_structure.png", p, width = 9, height = 6)
p = netVisual_embeddingZoomIn(cellchat, type = "structural")
ggsave("custer_pathway_structure2.png", p, width = 8, height = 6)
save(cellchat, file = "cellchat.rds")custer_pathway_function
custer_pathway_structure
