前言
我们知道，细胞间信息传递方式一个是细胞表面配受体的相互作用，另一个通过细胞产生的可溶性小分子，即细胞因子。在单细胞数据分析中下游，有时候我们想看某几种细胞类型之间的相互作用，就有人推荐我们做一个配受体分析。那什么是配受体？我们在文章Cell-Cell Interaction Database|| 单细胞配受体库你还在文章的附录里找吗？中提到配受体其实是细胞的特定蛋白，蛋白追溯到基因表达上就是基因对。
Inference and analysis of cell-cell communication using CellChat
Suoqin Jin, Christian F. Guerrero-Juarez, Lihua Zhang, Ivan Chang, Peggy Myung, Maksim V. Plikus, Qing Nie
bioRxiv 2020.07.21.214387; doi: https://doi.org/10.1101/2020.07.21.214387
今天，我们就用CellChat来分析一下我们的PBMC数据，看看配受体分析的一般流程。
除了从任何给定的scRNA-seq数据推断细胞间通信外，CellChat还提供了进一步的数据探索、分析和可视化功能。
- 它能够分析细胞与细胞间的通讯，以获得细胞发展轨迹上的连续状态。
- 该方法结合社会网络分析、模式识别和多种学习方法，可以定量地描述和比较推断出的细胞间通信网络。
- 它提供了一个易于使用的工具来提取和可视化推断网络信息。例如，它可以随时预测所有细胞群的主要信号输入和输出，以及这些细胞群和信号如何协调在一起实现功能。
- 它提供了几个可视化输出，以方便用户引导的直观数据解释。
devtools::install_github("sqjin/CellChat")
CellChat需要两个输入:
- 一个是细胞的基因表达数据，
- 另一个是细胞标签(即细胞标签)。
对于基因表达数据矩阵，基因应该在带有行名的行中，cell应该在带有名称的列中。CellChat分析的输入是均一化的数据(Seurat@assay$RNA@data)。如果用户提供counts数据，可以用normalizeData函数来均一化。对于细胞的信息，需要一个带有rownames的数据格式作为CellChat的输入。
这两个文件在我们熟悉的Seurat对象中是很容易找到的，一个是均一化之后的数据，一个是细胞类型在metadata中。那么就让我们开始chat之旅吧。
数据配置
首先，我们加载包和引入实例数据。
library(CellChat)
library(ggplot2)
library(ggalluvial)
library(svglite)
library(Seurat)
library(SeuratData)
options(stringsAsFactors = FALSE)
我们用Seurat给出的pbmc3k.final数据集，大部分的计算已经存在其对象中了：
pbmc3k.final
An object of class Seurat
13714 features across 2638 samples within 1 assay
Active assay: RNA (13714 features, 2000 variable features)
2 dimensional reductions calculated: pca, umap
pbmc3k.final@commands$FindClusters # 你也看一看作者的其他命令，Seurat是记录其分析过程的。
Command: FindClusters(pbmc3k.final, resolution = 0.5)
Time: 2020-04-30 12:54:53
graph.name : RNA_snn
modularity.fxn : 1
resolution : 0.5
method : matrix
algorithm : 1
n.start : 10
n.iter : 10
random.seed : 0
group.singletons : TRUE
verbose : TRUE
按照我们刚才说的，我们在Seurat对象中提出CellChat需要的数据：
data.input <- pbmc3k.final@assays$RNA@data
identity = data.frame(group =pbmc3k.final$seurat_annotations , row.names = names(pbmc3k.final$seurat_annotations)) # create a dataframe consisting of the cell labels
unique(identity$group) # check the cell labels
[1] Memory CD4 T B CD14+ Mono NK CD8 T Naive CD4 T FCGR3A+ Mono DC Platelet
Levels: Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
创建一个Cell Chat对象。
cellchat <- createCellChat(data = data.input)
cellchat
An object of class CellChat
13714 genes.
2638 cells.
summary(cellchat)
Length Class Mode
1 CellChat S4
S4 类学会了吗？
在学习单细胞数据分析工具的时候，在知道了要干嘛之后，第二步就是看数据格式，俗称：单细胞数据格式。我们在听说你的单细胞对象需要一个思维导图？，曾给出一个简单的可视化数据结构的方法：导图。
library(mindr)
(out <- capture.output(str(cellchat)))
out2 <- paste(out, collapse="\n")
mm(gsub("\\.\\.@","# ",gsub("\\.\\. ","#",out2)),type ="text")
当然，我们可以用str来看，就是有点冗长：
> str(cellchat)
Formal class 'CellChat' [package "CellChat"] with 14 slots
..@ data.raw : num[0 , 0 ]
..@ data :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
.. .. ..@ i : int [1:2238732] 29 73 80 148 163 184 186 227 229 230 ...
.. .. ..@ p : int [1:2639] 0 779 2131 3260 4220 4741 5522 6304 7094 7626 ...
.. .. ..@ Dim : int [1:2] 13714 2638
.. .. ..@ Dimnames:List of 2
.. .. .. ..$ : chr [1:13714] "AL627309.1" "AP006222.2" "RP11-206L10.2" "RP11-206L10.9" ...
.. .. .. ..$ : chr [1:2638] "AAACATACAACCAC" "AAACATTGAGCTAC" "AAACATTGATCAGC" "AAACCGTGCTTCCG" ...
.. .. ..@ x : num [1:2238732] 1.64 1.64 2.23 1.64 1.64 ...
.. .. ..@ factors : list()
..@ data.signaling: num[0 , 0 ]
..@ data.scale : num[0 , 0 ]
..@ data.project : num[0 , 0 ]
..@ net : list()
..@ netP : list()
..@ meta :'data.frame': 0 obs. of 0 variables
Formal class 'data.frame' [package "methods"] with 4 slots
.. .. ..@ .Data : list()
.. .. ..@ names : chr(0)
.. .. ..@ row.names: int(0)
.. .. ..@ .S3Class : chr "data.frame"
..@ idents :Formal class 'factor' [package "methods"] with 3 slots
.. .. ..@ .Data : int(0)
.. .. ..@ levels : chr(0)
.. .. ..@ .S3Class: chr "factor"
..@ DB : list()
..@ LR : list()
..@ var.features : logi(0)
..@ dr : list()
..@ options : list()
我们把metadata信息加到CellChat对象中，这个写法跟Seurat很像啊。
cellchat <- addMeta(cellchat, meta = identity, meta.name = "labels")
cellchat <- setIdent(cellchat, ident.use = "labels") # set "labels" as default cell identity
levels(cellchat@idents) # show factor levels of the cell labels
[1] "Naive CD4 T" "Memory CD4 T" "CD14+ Mono" "B" "CD8 T" "FCGR3A+ Mono" "NK"
groupSize <- as.numeric(table(cellchat@idents)) # number of cells in each cell group
[1] 697 483 480 344 271 162 155 32 14
导入配受体数据库
CellChat提供了人和小鼠的配受体数据库，分别可以用CellChatDB.human,CellChatDB.mouse来导入。来看一下这个数据库的结构吧。
CellChatDB <- CellChatDB.human
(out3 <- capture.output(str(CellChatDB)))
out4 <- paste(out3, collapse="\n")
mm(gsub("\\$","# ",gsub("\\.\\. ","#",out4)),type ="text")
这个数据库的信息是很全面的：
> colnames(CellChatDB$interaction)
[1] "interaction_name" "pathway_name" "ligand" "receptor" "agonist" "antagonist" "co_A_receptor"
[8] "co_I_receptor" "evidence" "annotation" "interaction_name_2"
> CellChatDB$interaction[1:4,1:4]
interaction_name pathway_name ligand receptor
TGFB1_TGFBR1_TGFBR2 TGFB1_TGFBR1_TGFBR2 TGFb TGFB1 TGFbR1_R2
TGFB2_TGFBR1_TGFBR2 TGFB2_TGFBR1_TGFBR2 TGFb TGFB2 TGFbR1_R2
TGFB3_TGFBR1_TGFBR2 TGFB3_TGFBR1_TGFBR2 TGFb TGFB3 TGFbR1_R2
TGFB1_ACVR1B_TGFBR2 TGFB1_ACVR1B_TGFBR2 TGFb TGFB1 ACVR1B_TGFbR2
> head(CellChatDB$cofactor)
cofactor1 cofactor2 cofactor3 cofactor4 cofactor5 cofactor6 cofactor7 cofactor8 cofactor9 cofactor10 cofactor11 cofactor12
ACTIVIN antagonist FST
ACTIVIN inhibition receptor BAMBI
ANGPT inhibition receptor 1 TIE1
ANGPT inhibition receptor 2 PTPRB
BMP antagonist NBL1 GREM1 GREM2 CHRD NOG BMP3 LEFTY1 LEFTY2
BMP inhibition receptor BAMBI
cofactor13 cofactor14 cofactor15 cofactor16
ACTIVIN antagonist
ACTIVIN inhibition receptor
ANGPT inhibition receptor 1
ANGPT inhibition receptor 2
BMP antagonist
BMP inhibition receptor
> head(CellChatDB$complex)
subunit_1 subunit_2 subunit_3 subunit_4
Activin AB INHBA INHBB
Inhibin A INHA INHBA
Inhibin B INHA INHBB
IL12AB IL12A IL12B
IL23 complex IL12B IL23A
IL27 complex IL27 EBI3
> head(CellChatDB$geneInfo)
Symbol Name EntrezGene.ID Ensembl.Gene.ID MGI.ID Gene.group.name
HGNC:5 A1BG alpha-1-B glycoprotein 1 ENSG00000121410 MGI:2152878 Immunoglobulin like domain containing
HGNC:37133 A1BG-AS1 A1BG antisense RNA 1 503538 ENSG00000268895 Antisense RNAs
HGNC:24086 A1CF APOBEC1 complementation factor 29974 ENSG00000148584 MGI:1917115 RNA binding motif containing
HGNC:7 A2M alpha-2-macroglobulin 2 ENSG00000175899 MGI:2449119 C3 and PZP like, alpha-2-macroglobulin domain containing
HGNC:27057 A2M-AS1 A2M antisense RNA 1 144571 ENSG00000245105 Antisense RNAs
HGNC:23336 A2ML1 alpha-2-macroglobulin like 1 144568 ENSG00000166535 C3 and PZP like, alpha-2-macroglobulin domain containing
其实是记录了许多许多受配体相关的通路信息，不像有的配受体库只有一个基因对。这样，我们就可以更加扎实地把脚落到pathway上面了。在CellChat中，我们还可以先择特定的信息描述细胞间的相互作者，这个可以理解为从特定的侧面来刻画细胞间相互作用,比用一个大的配体库又精细了许多呢。
CellChatDB.use <- subsetDB(CellChatDB, search = "Secreted Signaling") # use Secreted Signaling for cell-cell communication analysis
cellchat@DB <- CellChatDB.use # set the used database in the object
有哪些可以选择的侧面呢？
> unique(CellChatDB$interaction$annotation)
[1] "Secreted Signaling" "ECM-Receptor" "Cell-Cell Contact"
预处理
对表达数据进行预处理，用于细胞间的通信分析。首先在一个细胞组中识别过表达的配体或受体，然后将基因表达数据投射到蛋白-蛋白相互作用(PPI)网络上。如果配体或受体过表达，则识别过表达配体和受体之间的相互作用。
cellchat <- subsetData(cellchat) # subset the expression data of signaling genes for saving computation cost
future::plan("multiprocess", workers = 4) # do parallel 这里似乎有一些bug，在Linux上居然不行。de了它。
cellchat <- identifyOverExpressedGenes(cellchat)
cellchat <- identifyOverExpressedInteractions(cellchat)
cellchat <- projectData(cellchat, PPI.human)
相互作用推断
然后，我们通过为每个相互作用分配一个概率值并进行置换检验来推断生物意义上的细胞-细胞通信。
# cellchat <- computeCommunProb(cellchat) 注意这个函数如果你可以用就用，这个是作者的。
mycomputeCommunProb <-edit(computeCommunProb) # computeCommunProb内部似乎有一些bug，同一套数据在window10上没事，到了Linux上有报错。发现是computeExpr_antagonist这个函数有问题，(matrix(1, nrow = 1, ncol = length((group))))，中应为(matrix(1, nrow = 1, ncol = length(unique(group))))？ 不然矩阵返回的不对。de了它。
environment(mycomputeCommunProb) <- environment(computeCommunProb)
cellchat <- mycomputeCommunProb(cellchat) # 这儿是我de过的。
关于这个bug。我在GitHub上向作者提出了，并在20200727得到答复：已经修订。大家遇到问题也可以直接在GitHub上提问和回复。下面是例子（与本文无关）：
进入GitHub仓库：https://github.com/sqjin/CellChat，点击Issues
就可以经行提交问题了，对话框是支持markerdown语法的。如我们的例子。
这个对话有两点值得我们学习：
- 提问者说的很清楚，代码具体到哪一行，而且给出了示例。
- 回答者很快检查代码，并做了回应。
好了，我们可以接着往下走了。
推测细胞间在信号通路水平上的通讯。我们还通过计算与每个信号通路相关的所有配体-受体相互作用的通信概率来推断信号通路水平上的通信概率。
注:推测的每个配体-受体对的细胞间通信网络和每个信号通路分别存储在“net”和“netP”槽中。
我们可以通过计算链路的数量或汇总通信概率来计算细胞间的聚合通信网络。
cellchat <- computeCommunProbPathway(cellchat)
cellchat <- aggregateNet(cellchat)
让我们看看这结果。
> cellchat@netP$pathways
[1] "TGFb" "NRG" "PDGF" "CCL" "CXCL" "MIF" "IL2" "IL6" "IL10" "IL1" "CSF"
[12] "IL16" "IFN-II" "LT" "LIGHT" "FASLG" "TRAIL" "BAFF" "CD40" "VISFATIN" "COMPLEMENT" "PARs"
[23] "FLT3" "ANNEXIN" "GAS" "GRN" "GALECTIN" "BTLA" "BAG"
> head(cellchat@LR$LRsig)
interaction_name pathway_name ligand receptor agonist antagonist co_A_receptor co_I_receptor
TGFB1_TGFBR1_TGFBR2 TGFB1_TGFBR1_TGFBR2 TGFb TGFB1 TGFbR1_R2 TGFb agonist TGFb antagonist TGFb inhibition receptor
TGFB1_ACVR1B_TGFBR2 TGFB1_ACVR1B_TGFBR2 TGFb TGFB1 ACVR1B_TGFbR2 TGFb agonist TGFb antagonist TGFb inhibition receptor
TGFB1_ACVR1C_TGFBR2 TGFB1_ACVR1C_TGFBR2 TGFb TGFB1 ACVR1C_TGFbR2 TGFb agonist TGFb antagonist TGFb inhibition receptor
TGFB1_ACVR1_TGFBR1 TGFB1_ACVR1_TGFBR1 TGFb TGFB1 ACVR1_TGFbR
WNT10A_FZD1_LRP5 WNT10A_FZD1_LRP5 WNT WNT10A FZD1_LRP5 WNT agonist WNT antagonist WNT activation receptor WNT inhibition receptor
WNT10A_FZD2_LRP5 WNT10A_FZD2_LRP5 WNT WNT10A FZD2_LRP5 WNT agonist WNT antagonist WNT activation receptor WNT inhibition receptor
evidence annotation interaction_name_2
TGFB1_TGFBR1_TGFBR2 KEGG: hsa04350 Secreted Signaling TGFB1 - (TGFBR1+TGFBR2)
TGFB1_ACVR1B_TGFBR2 PMID: 27449815 Secreted Signaling TGFB1 - (ACVR1B+TGFBR2)
TGFB1_ACVR1C_TGFBR2 PMID: 27449815 Secreted Signaling TGFB1 - (ACVR1C+TGFBR2)
TGFB1_ACVR1_TGFBR1 PMID: 29376829 Secreted Signaling TGFB1 - (ACVR1+TGFBR1)
WNT10A_FZD1_LRP5 KEGG: hsa04310; PMID: 23209157 Secreted Signaling WNT10A - (FZD1+LRP5)
WNT10A_FZD2_LRP5 KEGG: hsa04310; PMID: 23209159 Secreted Signaling WNT10A - (FZD2+LRP5)
可视化
在推断细胞-细胞通信网络的基础上，CellChat为进一步的探索、分析和可视化提供了各种功能。
通过结合社会网络分析、模式识别和多种学习方法的综合方法，t可以定量地描述和比较推断出的细胞-细胞通信网络。
它提供了一个易于使用的工具来提取和可视化推断网络的高阶信息。例如，它可以随时预测所有细胞群的主要信号输入和输出，以及这些细胞群和信号如何协调在一起实现功能。
你可以使用层次图或圈图可视化每个信号通路。 如果使用层次图可视化通信网络，请定义vertex.receiver，它是一个数字向量，给出作为第一个层次结构图中的目标的细胞组的索引。我们可以使用netVisual_aggregate来可视化信号路径的推断通信网络，并使用netVisual_individual来可视化与该信号路径相关的单个L-R对的通信网络。
在层次图中，实体圆和空心圆分别表示源和目标。圆的大小与每个细胞组的细胞数成比例。边缘颜色与信源一致。线越粗，信号越强。这里我们展示了一个MIF信号网络的例子。所有显示重要通信的信令路径都可以通过cellchat@netP$pathways访问。
>cellchat@netP$pathways
[1] "TGFb" "NRG" "PDGF" "CCL" "CXCL" "MIF" "IL2" "IL6" "IL10" "IL1"
[11] "CSF" "IL16" "IFN-II" "LT" "LIGHT" "FASLG" "TRAIL" "BAFF" "CD40" "VISFATIN"
[21] "COMPLEMENT" "PARs" "FLT3" "ANNEXIN" "GAS" "GRN" "GALECTIN" "BTLA" "BAG"
levels(cellchat@idents)
vertex.receiver = seq(1,4) # a numeric vector
# check the order of cell identity to set suitable vertex.receiver
#cellchat@LR$LRsig$pathway_name
#cellchat@LR$LRsig$antagonist
pathways.show <- "MIF"
# netVisual_aggregate(cellchat, signaling = pathways.show, vertex.receiver = vertex.receiver, vertex.size = groupSize) # 原函数
mynetVisual_aggregate(cellchat, signaling = pathways.show, vertex.receiver = vertex.receiver, vertex.size = groupSize) 原函数这里似乎有一个和igraph相关的小问题在不同igraph可能会表现bug，不巧我遇到了，de了它。
经典的配受体圈图：
mynetVisual_aggregate(cellchat, signaling = c("MIF"), layout = "circle", vertex.size = groupSize,pt.title=20,vertex.label.cex = 1.7)
计算和可视化每个配体-受体对整个信号通路的贡献度。
netAnalysis_contribution(cellchat, signaling = pathways.show)
识别细胞群的信号转导作用，通过计算每个细胞群的网络中心性指标，CellChat允许随时识别细胞间通信网络中的主要发送者、接收者、调解者和影响者。
cellchat <- netAnalysis_signalingRole(cellchat, slot.name = "netP") # the slot 'netP' means the inferred intercellular communication network of signaling pathways
···
netVisual_signalingRole(cellchat, signaling = pathways.show, width = 12, height = 2.5, font.size = 10)
···
识别特定细胞群的全局通信模式和主要信号。除了探索单个通路的详细通讯外，一个重要的问题是多个细胞群和信号通路如何协调运作。CellChat采用模式识别方法来识别全局通信模式以及每个小群的关键信号。
识别分泌细胞外向交流模式。随着模式数量的增加，可能会出现冗余的模式，使得解释通信模式变得困难。我们选择了5种模式作为默认模式。一般来说，当模式的数量大于2时就可以认为具有生物学意义。
nPatterns = 5
# 同样在这里遇到了bug，难道说是我没有安装好吗，de了它。
# cellchat <- myidentifyCommunicationPatterns(cellchat, pattern = "outgoing", k = nPatterns)
myidentifyCommunicationPatterns <- edit(identifyCommunicationPatterns)
environment(myidentifyCommunicationPatterns) <- environment(identifyCommunicationPatterns)
cellchat <- myidentifyCommunicationPatterns(cellchat, pattern = "outgoing", k = nPatterns)
# Visualize the communication pattern using river plot
netAnalysis_river(cellchat, pattern = "outgoing")
# Visualize the communication pattern using dot plot
netAnalysis_dot(cellchat, pattern = "outgoing")
识别目标细胞的传入(incoming)通信模式。
netAnalysis_river(cellchat, pattern = "incoming")
netAnalysis_dot(cellchat, pattern = "incoming")
作为结尾有大量的空间，我们得以先看看cellchat配受体推断的结构是如何的。
> head(cellchat@LR$LRsig)
interaction_name pathway_name ligand receptor agonist antagonist co_A_receptor co_I_receptor
TGFB1_TGFBR1_TGFBR2 TGFB1_TGFBR1_TGFBR2 TGFb TGFB1 TGFbR1_R2 TGFb agonist TGFb antagonist TGFb inhibition receptor
TGFB1_ACVR1B_TGFBR2 TGFB1_ACVR1B_TGFBR2 TGFb TGFB1 ACVR1B_TGFbR2 TGFb agonist TGFb antagonist TGFb inhibition receptor
TGFB1_ACVR1C_TGFBR2 TGFB1_ACVR1C_TGFBR2 TGFb TGFB1 ACVR1C_TGFbR2 TGFb agonist TGFb antagonist TGFb inhibition receptor
TGFB1_ACVR1_TGFBR1 TGFB1_ACVR1_TGFBR1 TGFb TGFB1 ACVR1_TGFbR
WNT10A_FZD1_LRP5 WNT10A_FZD1_LRP5 WNT WNT10A FZD1_LRP5 WNT agonist WNT antagonist WNT activation receptor WNT inhibition receptor
WNT10A_FZD2_LRP5 WNT10A_FZD2_LRP5 WNT WNT10A FZD2_LRP5 WNT agonist WNT antagonist WNT activation receptor WNT inhibition receptor
evidence annotation interaction_name_2
TGFB1_TGFBR1_TGFBR2 KEGG: hsa04350 Secreted Signaling TGFB1 - (TGFBR1+TGFBR2)
TGFB1_ACVR1B_TGFBR2 PMID: 27449815 Secreted Signaling TGFB1 - (ACVR1B+TGFBR2)
TGFB1_ACVR1C_TGFBR2 PMID: 27449815 Secreted Signaling TGFB1 - (ACVR1C+TGFBR2)
TGFB1_ACVR1_TGFBR1 PMID: 29376829 Secreted Signaling TGFB1 - (ACVR1+TGFBR1)
WNT10A_FZD1_LRP5 KEGG: hsa04310; PMID: 23209157 Secreted Signaling WNT10A - (FZD1+LRP5)
WNT10A_FZD2_LRP5 KEGG: hsa04310; PMID: 23209159 Secreted Signaling WNT10A - (FZD2+LRP5)
> head(cellchat@dr)
list()
> head(cellchat@data)
6 x 2638 sparse Matrix of class "dgCMatrix"
[[ suppressing 70 column names 'AAACATACAACCAC', 'AAACATTGAGCTAC', 'AAACATTGATCAGC' ... ]]
AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AP006222.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-206L10.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
RP11-206L10.9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
LINC00115 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
NOC2L . . . . . . . . . . . 1.646272 . . . . . . . . 1.398186 . . . . . . . . . . . . 1.89939 . . . . . . . 1.36907 1.721224 . . . . . . . . .
AL627309.1 . . . . . . . . . . . . . . . . . . ......
AP006222.2 . . . . . . . . . . . . . . . . . . ......
RP11-206L10.2 . . . . . . . . . . . . . . . . . . ......
RP11-206L10.9 . . . . . . . . . . . . . . . . . . ......
LINC00115 . . . . . . . . . . . . . . . . . . ......
NOC2L . . . 1.568489 1.678814 . 1.253835 . . 3.791113 . . . . . . . . ......
.....suppressing 2568 columns in show(); maybe adjust 'options(max.print= *, width = *)'
..............................
> head(cellchat@idents)
[1] Memory CD4 T B Memory CD4 T CD14+ Mono NK Memory CD4 T
Levels: Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
> head(cellchat@meta)
labels
AAACATACAACCAC Memory CD4 T
AAACATTGAGCTAC B
AAACATTGATCAGC Memory CD4 T
AAACCGTGCTTCCG CD14+ Mono
AAACCGTGTATGCG NK
AAACGCACTGGTAC Memory CD4 T
> head(cellchat@netP$pathways)
[1] "TGFb" "NRG" "PDGF" "CCL" "CXCL" "MIF"
> head(cellchat@netP$prob)
[1] 0 0 0 0 0 0
> head(cellchat@netP$centr)
$TGFb
$TGFb$outdeg
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
0.000000e+00 5.798502e-07 2.634094e-05 0.000000e+00 1.108822e-06 9.977646e-06 9.953461e-06 2.840617e-07 3.475282e-06
$TGFb$indeg
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
0.000000e+00 1.002762e-05 1.384499e-05 0.000000e+00 7.596075e-06 1.270618e-05 5.256794e-06 5.744824e-07 1.713913e-06
$TGFb$hub
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
0.00000000 0.02278982 1.00000000 0.00000000 0.04484954 0.37878876 0.37787064 0.01116456 0.13193619
$TGFb$authority
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
0.00000000 0.74712407 1.00000000 0.00000000 0.56314554 0.86435263 0.37969073 0.04280264 0.11659336
$TGFb$eigen
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
0.01217244 0.31304003 1.00000000 0.01217244 0.25802457 0.58202001 0.37843282 0.02320534 0.12622971
$TGFb$page_rank
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
0.02054795 0.13742492 0.21555291 0.02054795 0.11208641 0.31212523 0.09458943 0.02724384 0.05988138
$TGFb$betweenness
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
0 0 24 0 0 10 0 0 0
$TGFb$flowbet
[1] 0.000000e+00 4.342669e-06 2.862661e-05 0.000000e+00 6.752863e-06 2.460332e-05 1.254051e-05 1.032200e-06 6.967716e-06
$TGFb$info
[1] 0.00000000 0.16628670 0.19401551 0.00000000 0.12870372 0.18191312 0.16895822 0.03556505 0.12455769
$NRG
$NRG$outdeg
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
1.116774e-10 1.024289e-10 2.194763e-10 5.436629e-11 5.792191e-11 1.166520e-10 4.634672e-11 1.511780e-11 1.629172e-12
$NRG$indeg
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 7.256165e-10
$NRG$hub
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
0.508835533 0.466696996 1.000000000 0.247709130 0.263909627 0.531501345 0.211169583 0.068881216 0.007422998
$NRG$authority
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
4.163336e-17 4.163336e-17 4.163336e-17 4.163336e-17 4.163336e-17 4.163336e-17 4.163336e-17 4.163336e-17 1.000000e+00
$NRG$eigen
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
0.36342198 0.33332567 0.71422288 0.17691953 0.18849029 0.37961042 0.15082215 0.04919654 1.00000000
$NRG$page_rank
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
0.01666667 0.01666667 0.01666667 0.01666667 0.01666667 0.01666667 0.01666667 0.01666667 0.86666667
$NRG$betweenness
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
0 0 0 0 0 0 0 0 0
$NRG$flowbet
[1] 0 0 0 0 0 0 0 0 0
$NRG$info
[1] 0 0 0 0 0 0 0 0 0
$PDGF
$PDGF$outdeg
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
2.117157e-10 5.254122e-10 1.830680e-09 0.000000e+00 3.046756e-10 1.195279e-09 6.457814e-10 1.492427e-10 0.000000e+00
$PDGF$indeg
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
9.596760e-10 7.355168e-10 1.375790e-09 0.000000e+00 4.145239e-10 1.028332e-09 2.501300e-10 9.881712e-11 0.000000e+00
$PDGF$hub
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
0.09759699 0.32222056 1.00000000 0.00000000 0.18684898 0.65291566 0.35275497 0.08152314 0.00000000
$PDGF$authority
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
9.058608e-01 6.942716e-01 1.000000e+00 2.363558e-17 3.912788e-01 6.197010e-01 2.361036e-01 7.182571e-02 2.363558e-17
$PDGF$eigen
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
0.44237332 0.51181753 1.00000000 0.07396075 0.29250188 0.67517921 0.29135234 0.07823533 0.07396075
$PDGF$page_rank
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
0.15091590 0.12046482 0.24044555 0.02054795 0.07685927 0.27934926 0.05452706 0.03634225 0.02054795
$PDGF$betweenness
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
1 0 18 0 0 5 0 0 0
$PDGF$flowbet
[1] 8.857166e-10 1.204604e-09 4.049689e-09 0.000000e+00 8.517939e-10 3.745196e-09 1.048193e-09 4.458839e-10 0.000000e+00
$PDGF$info
[1] 0.16144948 0.14611532 0.20300365 0.00000000 0.10956327 0.17885050 0.14080069 0.06021709 0.00000000
$CCL
$CCL$outdeg
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
1.682814e-04 6.442088e-04 9.328993e-04 9.764691e-05 4.601953e-03 1.067399e-05 2.613615e-03 5.048297e-05 2.374245e-04
$CCL$indeg
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
1.013085e-03 1.208426e-03 4.952297e-04 5.869028e-04 3.900117e-03 1.125963e-04 1.773075e-03 7.483047e-05 1.929230e-04
$CCL$hub
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
0.050735945 0.193934101 0.210819077 0.029282445 1.000000000 0.003249727 0.551908511 0.013914236 0.052892139
$CCL$authority
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
0.30095289 0.35990610 0.14750945 0.17431275 1.00000000 0.03323390 0.45558530 0.02222082 0.04989215
$CCL$eigen
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
0.17214374 0.27750548 0.17861545 0.09964869 1.00000000 0.01772801 0.50285164 0.01802822 0.05152917
$CCL$page_rank
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
0.08937815 0.10366984 0.05186354 0.05878616 0.41583926 0.02465234 0.19773523 0.02202754 0.03604793
$CCL$betweenness
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
0 0 0 0 56 0 0 0 0
$CCL$flowbet
[1] 6.253950e-04 1.206020e-03 1.184412e-03 4.216339e-04 7.464863e-03 7.286026e-05 3.851205e-03 1.024123e-04 5.393918e-04
$CCL$info
[1] 0.13488584 0.13862093 0.12659975 0.11726949 0.15963716 0.03961851 0.15306688 0.04024833 0.09005310
$CXCL
$CXCL$outdeg
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 2.948861e-08
$CXCL$indeg
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
6.251119e-09 5.660697e-09 4.984283e-09 2.735102e-09 2.997064e-09 3.851281e-09 2.461799e-09 4.823805e-10 6.488065e-11
$CXCL$hub
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
0 0 0 0 0 0 0 0 1
$CXCL$authority
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
1.00000000 0.90554935 0.79734257 0.43753795 0.47944431 0.61609465 0.39381731 0.07716707 0.01037905
$CXCL$eigen
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
0.5394037 0.4884566 0.4300895 0.2360096 0.2586140 0.3323237 0.2124265 0.0416242 1.0000000
$CXCL$page_rank
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
0.1198308 0.1181000 0.1161172 0.1095240 0.1102919 0.1127960 0.1087229 0.1029205 0.1016966
$CXCL$betweenness
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
0 0 0 0 0 0 0 0 0
$CXCL$flowbet
[1] 0 0 0 0 0 0 0 0 0
$CXCL$info
[1] 0.12994155 0.12702636 0.12305974 0.10129559 0.10488823 0.11427509 0.09707279 0.03583427 0.16660638
$MIF
$MIF$outdeg
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
0.0012989751 0.0039272021 0.0006234461 0.0006401726 0.0005135156 0.0002049902 0.0003848437 0.0001321595 0.0000000000
$MIF$indeg
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
0.0005188736 0.0008184262 0.0007859180 0.0035144980 0.0009227472 0.0008137752 0.0001170739 0.0002339928 0.0000000000
$MIF$hub
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
4.252550e-01 1.000000e+00 2.238501e-01 2.095786e-01 1.680262e-01 7.360549e-02 1.160678e-01 4.315756e-02 2.774719e-18
$MIF$authority
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
2.769140e-01 4.020539e-01 2.249636e-01 1.000000e+00 3.209851e-01 2.590427e-01 6.228011e-02 7.151140e-02 4.690529e-18
$MIF$eigen
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
0.40785736 1.00000000 0.28217435 0.81714092 0.31062247 0.21639268 0.11882053 0.07323643 0.01492405
$MIF$page_rank
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
0.02128513 0.02564654 0.12732754 0.50874503 0.11392566 0.11715499 0.01911107 0.04839913 0.01840491
$MIF$betweenness
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
0 10 0 17 14 0 11 0 0
$MIF$flowbet
[1] 0.0010772004 0.0004430504 0.0013253722 0.0018828736 0.0013830050 0.0007361476 0.0002572374 0.0005064761 0.0000000000
$MIF$info
[1] 0.10896205 0.16504074 0.11589344 0.17947163 0.13549734 0.10051455 0.12311142 0.07150883 0.00000000
每个pattern有outgoing和ingoing两种。
> head(cellchat@netP$pattern$outgoing$pattern$cell)
CellGroup Pattern Contribution
1 Naive CD4 T Pattern 1 9.182571e-01
2 Memory CD4 T Pattern 1 8.643879e-01
3 CD14+ Mono Pattern 1 6.958107e-04
4 B Pattern 1 8.943340e-01
5 CD8 T Pattern 1 8.497941e-02
6 FCGR3A+ Mono Pattern 1 2.351798e-05
> head(cellchat@netP$pattern$outgoing$pattern$signaling)
Pattern Signaling Contribution
1 Pattern 1 TGFb 1.509635e-08
2 Pattern 2 TGFb 5.851347e-01
3 Pattern 3 TGFb 2.021400e-01
4 Pattern 4 TGFb 4.466321e-08
5 Pattern 5 TGFb 2.127253e-01
6 Pattern 1 NRG 3.333424e-01
> head(cellchat@netP$pattern$outgoing$data)
TGFb NRG PDGF CCL CXCL MIF IL2 IL6 IL10 IL1 CSF IL16 IFN-II
Naive CD4 T 0.00000000 0.5088355 0.1156487 0.036567375 0 0.33076349 1.000000000 0.21361180 0.017388599 1.043256e-04 0.0006363636 0 0.004454402
Memory CD4 T 0.02201327 0.4666970 0.2870039 0.139985939 0 1.00000000 0.948036204 0.22211580 1.000000000 1.150654e-04 0.0006048585 0 0.004707477
CD14+ Mono 1.00000000 1.0000000 1.0000000 0.202718122 0 0.15875069 0.000000000 0.09461735 0.005818249 1.000000e+00 0.0010788329 0 0.005461241
B 0.00000000 0.2477091 0.0000000 0.021218579 0 0.16300984 0.009150461 0.02181469 0.003863723 2.876928e-05 0.0002110580 0 0.001720322
CD8 T 0.04209499 0.2639096 0.1664276 1.000000000 0 0.13075865 0.475620565 0.12534217 0.527133566 4.519162e-05 0.0003131413 0 0.003303116
FCGR3A+ Mono 0.37878860 0.5315013 0.6529157 0.002319449 0 0.05219751 0.000000000 0.03752352 0.253673778 7.630358e-05 1.0000000000 0 0.004745991
LT LIGHT FASLG TRAIL BAFF CD40 VISFATIN COMPLEMENT PARs FLT3 ANNEXIN GAS
Naive CD4 T 1.0000000 0.0000000 0.12801302 0.00000000 1.987539e-04 0.0052298348 0 1.0000000 0 1.0000000000 0.3515932720 0.02399186
Memory CD4 T 0.8516886 1.0000000 0.85744830 0.09989685 2.286423e-04 1.0000000000 0 0.9403386 0 0.6925428133 1.0000000000 0.03584303
CD14+ Mono 0.0512085 0.0000000 1.00000000 1.00000000 1.000000e+00 0.0080996253 0 0.8803694 0 0.0006179983 0.7171291990 0.02706222
B 0.5629699 0.0000000 0.06312626 0.00000000 8.393504e-05 0.0003093270 0 0.3587101 0 0.0003490343 0.0003780528 0.01054186
CD8 T 0.1842115 0.0000000 0.08407400 0.00000000 6.513411e-05 0.0008636328 0 0.5033253 1 0.0004055095 0.4595993742 0.01898338
FCGR3A+ Mono 0.0832080 0.2745868 0.63644930 0.93360412 3.279022e-01 0.0044454725 1 0.3187685 0 0.0002367928 0.2119665274 0.01193921
GRN GALECTIN BTLA BAG
Naive CD4 T 0.0000000 0.0000000 0.0000000 1.0000000
Memory CD4 T 0.0000000 0.0000000 1.0000000 0.9388102
CD14+ Mono 1.0000000 0.8983294 0.0000000 0.7920962
B 0.0000000 0.0000000 0.5998942 0.4454517
CD8 T 0.0000000 0.0000000 0.0000000 0.4831780
FCGR3A+ Mono 0.1277283 1.0000000 0.2785847 0.3247730
> cellchat@net
$prob
, , TGFB1_TGFBR1_TGFBR2
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
Naive CD4 T 1.222691e-11 1.692462e-09 2.264589e-09 4.620186e-12 1.291360e-09 1.960243e-09 8.655394e-10 9.634429e-11 2.629338e-10
Memory CD4 T 2.270338e-09 3.142597e-07 4.204920e-07 8.578932e-10 2.397814e-07 3.639734e-07 1.607142e-07 1.788942e-08 4.882008e-08
CD14+ Mono 2.719456e-08 3.763876e-06 5.036034e-06 1.027602e-08 2.871745e-06 4.358185e-06 1.924748e-06 2.142640e-07 5.844517e-07
B 3.582287e-12 4.958639e-10 6.634879e-10 1.353639e-12 3.783474e-10 5.743193e-10 2.535890e-10 2.822731e-11 7.703534e-11
CD8 T 1.736672e-09 2.403890e-07 3.216497e-07 6.562368e-10 1.834175e-07 2.784145e-07 1.229360e-07 1.368429e-08 3.734375e-08
FCGR3A+ Mono 1.030133e-08 1.425741e-06 1.907620e-06 3.892565e-09 1.087800e-06 1.650808e-06 7.290809e-07 8.116246e-08 2.213748e-07
NK 1.027623e-08 1.422259e-06 1.902958e-06 3.883081e-09 1.085141e-06 1.646755e-06 7.272983e-07 8.096435e-08 2.208291e-07
DC 1.112404e-09 1.539695e-07 2.060130e-07 4.203442e-10 1.174768e-07 1.783002e-07 7.873805e-08 8.764877e-09 2.391283e-08
Platelet 3.590036e-09 4.966840e-07 6.644667e-07 1.356569e-09 3.789035e-07 5.745492e-07 2.539314e-07 2.827603e-08 7.699129e-08
, , TGFB1_ACVR1B_TGFBR2
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
Naive CD4 T 8.440868e-12 3.075855e-10 4.274550e-10 3.315884e-12 2.229500e-10 3.681800e-10 1.570298e-10 1.834846e-11 4.756356e-11
Memory CD4 T 1.567331e-09 5.711352e-08 7.937122e-08 6.157055e-10 4.139808e-08 6.836461e-08 2.915780e-08 3.407004e-09 8.831697e-09
CD14+ Mono 1.877381e-08 6.841044e-07 9.506996e-07 7.375044e-09 4.958626e-07 8.188301e-07 3.492476e-07 4.080909e-08 1.057772e-07
B 2.473037e-12 9.011756e-11 1.252374e-10 9.715001e-13 6.532072e-11 1.078708e-10 4.600719e-11 5.375799e-12 1.393535e-11
CD8 T 1.198914e-09 4.368838e-08 6.071417e-08 4.709778e-10 3.166702e-08 5.229470e-08 2.230394e-08 2.606152e-09 6.755695e-09
FCGR3A+ Mono 7.111536e-09 2.591388e-07 3.601247e-07 2.793674e-09 1.878326e-07 3.101709e-07 1.322948e-07 1.545849e-08 4.006799e-08
NK 7.094210e-09 2.585072e-07 3.592468e-07 2.786868e-09 1.873748e-07 3.094142e-07 1.319723e-07 1.542082e-08 3.997016e-08
DC 7.679496e-10 2.798377e-08 3.888915e-08 3.016789e-10 2.028365e-08 3.349550e-08 1.428628e-08 1.669323e-09 4.327040e-09
Platelet 2.478389e-09 9.030435e-08 1.254923e-07 9.736029e-10 6.545434e-08 1.080686e-07 4.610002e-08 5.386992e-09 1.395861e-08
, , TGFB1_ACVR1C_TGFBR2
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
Naive CD4 T 0 0 0 0 0 0 0 0 0
Memory CD4 T 0 0 0 0 0 0 0 0 0
CD14+ Mono 0 0 0 0 0 0 0 0 0
B 0 0 0 0 0 0 0 0 0
CD8 T 0 0 0 0 0 0 0 0 0
FCGR3A+ Mono 0 0 0 0 0 0 0 0 0
NK 0 0 0 0 0 0 0 0 0
DC 0 0 0 0 0 0 0 0 0
Platelet 0 0 0 0 0 0 0 0 0
, , TGFB1_ACVR1_TGFBR1
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
Naive CD4 T 1.189109e-11 3.873823e-10 4.965448e-10 4.544316e-12 2.846539e-10 4.232163e-10 1.881368e-10 2.208094e-11 6.074030e-11
Memory CD4 T 2.207939e-09 7.192901e-08 9.219821e-08 8.437887e-10 5.285441e-08 7.858227e-08 3.493314e-08 4.099982e-09 1.127813e-08
CD14+ Mono 2.644406e-08 8.614599e-07 1.104207e-06 1.010590e-08 6.330077e-07 9.410925e-07 4.183726e-07 4.910372e-08 1.350587e-07
B 3.484106e-12 1.135035e-10 1.454883e-10 1.331490e-12 8.340397e-11 1.240029e-10 5.512432e-11 6.469743e-12 1.779699e-11
CD8 T 1.688886e-09 5.501955e-08 7.052374e-08 6.454268e-10 4.042910e-08 6.010862e-08 2.672085e-08 3.136136e-09 8.626777e-09
FCGR3A+ Mono 1.001622e-08 3.262944e-07 4.182392e-07 3.827814e-09 2.397636e-07 3.564543e-07 1.584664e-07 1.859898e-08 5.115538e-08
NK 9.993118e-09 3.255413e-07 4.172736e-07 3.818983e-09 2.392101e-07 3.556306e-07 1.581005e-07 1.855606e-08 5.103702e-08
DC 1.081809e-09 3.524210e-08 4.517296e-08 4.134256e-10 2.589626e-08 3.850073e-08 1.711561e-08 2.008818e-09 5.525462e-09
Platelet 3.490750e-09 1.137069e-07 1.457441e-07 1.334030e-09 8.355043e-08 1.241924e-07 5.521981e-08 6.481446e-09 1.781969e-08
, , WNT10A_FZD1_LRP5
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
Naive CD4 T 0 0 0 0 0 0 0 0 0
Memory CD4 T 0 0 0 0 0 0 0 0 0
CD14+ Mono 0 0 0 0 0 0 0 0 0
B 0 0 0 0 0 0 0 0 0
CD8 T 0 0 0 0 0 0 0 0 0
FCGR3A+ Mono 0 0 0 0 0 0 0 0 0
NK 0 0 0 0 0 0 0 0 0
DC 0 0 0 0 0 0 0 0 0
Platelet 0 0 0 0 0 0 0 0 0
, , WNT10A_FZD2_LRP5
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
Naive CD4 T 0 0 0 0 0 0 0 0 0
Memory CD4 T 0 0 0 0 0 0 0 0 0
CD14+ Mono 0 0 0 0 0 0 0 0 0
B 0 0 0 0 0 0 0 0 0
CD8 T 0 0 0 0 0 0 0 0 0
FCGR3A+ Mono 0 0 0 0 0 0 0 0 0
NK 0 0 0 0 0 0 0 0 0
DC 0 0 0 0 0 0 0 0 0
Platelet 0 0 0 0 0 0 0 0 0
, , WNT10A_FZD3_LRP5
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
Naive CD4 T 0 0 0 0 0 0 0 0 0
Memory CD4 T 0 0 0 0 0 0 0 0 0
CD14+ Mono 0 0 0 0 0 0 0 0 0
B 0 0 0 0 0 0 0 0 0
CD8 T 0 0 0 0 0 0 0 0 0
FCGR3A+ Mono 0 0 0 0 0 0 0 0 0
NK 0 0 0 0 0 0 0 0 0
DC 0 0 0 0 0 0 0 0 0
Platelet 0 0 0 0 0 0 0 0 0
, , WNT10A_FZD6_LRP5
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
Naive CD4 T 0 0 0 0 0 0 0 0 0
Memory CD4 T 0 0 0 0 0 0 0 0 0
CD14+ Mono 0 0 0 0 0 0 0 0 0
B 0 0 0 0 0 0 0 0 0
CD8 T 0 0 0 0 0 0 0 0 0
FCGR3A+ Mono 0 0 0 0 0 0 0 0 0
NK 0 0 0 0 0 0 0 0 0
DC 0 0 0 0 0 0 0 0 0
Platelet 0 0 0 0 0 0 0 0 0
, , WNT10B_FZD1_LRP5
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
Naive CD4 T 0 0 0 0 0 0 0 0 0
Memory CD4 T 0 0 0 0 0 0 0 0 0
CD14+ Mono 0 0 0 0 0 0 0 0 0
B 0 0 0 0 0 0 0 0 0
CD8 T 0 0 0 0 0 0 0 0 0
FCGR3A+ Mono 0 0 0 0 0 0 0 0 0
NK 0 0 0 0 0 0 0 0 0
DC 0 0 0 0 0 0 0 0 0
Platelet 0 0 0 0 0 0 0 0 0
, , WNT10B_FZD2_LRP5
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
Naive CD4 T 0 0 0 0 0 0 0 0 0
Memory CD4 T 0 0 0 0 0 0 0 0 0
CD14+ Mono 0 0 0 0 0 0 0 0 0
B 0 0 0 0 0 0 0 0 0
CD8 T 0 0 0 0 0 0 0 0 0
FCGR3A+ Mono 0 0 0 0 0 0 0 0 0
NK 0 0 0 0 0 0 0 0 0
DC 0 0 0 0 0 0 0 0 0
Platelet 0 0 0 0 0 0 0 0 0
, , WNT10B_FZD3_LRP5
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
Naive CD4 T 0 0 0 0 0 0 0 0 0
Memory CD4 T 0 0 0 0 0 0 0 0 0
CD14+ Mono 0 0 0 0 0 0 0 0 0
B 0 0 0 0 0 0 0 0 0
CD8 T 0 0 0 0 0 0 0 0 0
FCGR3A+ Mono 0 0 0 0 0 0 0 0 0
NK 0 0 0 0 0 0 0 0 0
DC 0 0 0 0 0 0 0 0 0
Platelet 0 0 0 0 0 0 0 0 0
, , WNT10B_FZD6_LRP5
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
Naive CD4 T 0 0 0 0 0 0 0 0 0
Memory CD4 T 0 0 0 0 0 0 0 0 0
CD14+ Mono 0 0 0 0 0 0 0 0 0
B 0 0 0 0 0 0 0 0 0
CD8 T 0 0 0 0 0 0 0 0 0
FCGR3A+ Mono 0 0 0 0 0 0 0 0 0
NK 0 0 0 0 0 0 0 0 0
DC 0 0 0 0 0 0 0 0 0
Platelet 0 0 0 0 0 0 0 0 0
, , WNT16_FZD1_LRP5
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
Naive CD4 T 0 0 0 0 0 0 0 0 0
Memory CD4 T 0 0 0 0 0 0 0 0 0
CD14+ Mono 0 0 0 0 0 0 0 0 0
[ reached getOption("max.print") -- omitted 6 row(s) and 114 matrix slice(s) ]
$pval
, , TGFB1_TGFBR1_TGFBR2
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
Naive CD4 T 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.99 0.56
Memory CD4 T 1.00 0.67 0.39 1.00 0.33 0.00 0.15 0.44 0.01
CD14+ Mono 0.87 0.00 0.00 0.80 0.00 0.00 0.00 0.00 0.00
B 1.00 1.00 0.98 1.00 0.99 0.95 0.95 0.99 0.69
CD8 T 1.00 0.36 0.04 0.99 0.07 0.00 0.00 0.44 0.00
FCGR3A+ Mono 0.73 0.00 0.00 0.67 0.00 0.00 0.00 0.00 0.00
NK 0.74 0.00 0.00 0.70 0.00 0.00 0.00 0.01 0.00
DC 0.70 0.20 0.21 0.68 0.22 0.00 0.10 0.26 0.01
Platelet 0.52 0.00 0.00 0.48 0.00 0.00 0.00 0.00 0.00
, , TGFB1_ACVR1B_TGFBR2
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
Naive CD4 T 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.99 0.94
Memory CD4 T 1.00 0.73 0.39 1.00 0.48 0.00 0.24 0.46 0.02
CD14+ Mono 0.87 0.00 0.00 0.78 0.00 0.00 0.00 0.00 0.00
B 1.00 1.00 0.99 1.00 0.99 0.97 0.96 0.99 0.92
CD8 T 0.92 0.39 0.04 0.93 0.16 0.00 0.00 0.45 0.00
FCGR3A+ Mono 0.71 0.00 0.00 0.67 0.00 0.00 0.00 0.00 0.00
NK 0.75 0.00 0.00 0.70 0.00 0.00 0.00 0.01 0.00
DC 0.66 0.21 0.21 0.64 0.23 0.00 0.10 0.26 0.01
Platelet 0.42 0.00 0.00 0.42 0.00 0.00 0.00 0.00 0.00
, , TGFB1_ACVR1C_TGFBR2
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
Naive CD4 T 1 1 1 1 1 1 1 1 1
Memory CD4 T 1 1 1 1 1 1 1 1 1
CD14+ Mono 1 1 1 1 1 1 1 1 1
B 1 1 1 1 1 1 1 1 1
CD8 T 1 1 1 1 1 1 1 1 1
FCGR3A+ Mono 1 1 1 1 1 1 1 1 1
NK 1 1 1 1 1 1 1 1 1
DC 1 1 1 1 1 1 1 1 1
Platelet 1 1 1 1 1 1 1 1 1
, , TGFB1_ACVR1_TGFBR1
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
Naive CD4 T 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.99 0.95
Memory CD4 T 1.00 0.75 0.46 1.00 0.38 0.00 0.22 0.47 0.02
CD14+ Mono 0.88 0.00 0.00 0.80 0.00 0.00 0.00 0.00 0.00
B 1.00 1.00 1.00 1.00 0.99 0.97 0.98 0.99 0.91
CD8 T 0.92 0.38 0.05 0.92 0.05 0.00 0.00 0.46 0.00
FCGR3A+ Mono 0.71 0.00 0.00 0.67 0.00 0.00 0.00 0.00 0.00
NK 0.76 0.00 0.00 0.71 0.00 0.00 0.00 0.02 0.00
DC 0.66 0.21 0.23 0.63 0.23 0.00 0.12 0.25 0.01
Platelet 0.40 0.00 0.00 0.40 0.00 0.00 0.00 0.00 0.00
, , WNT10A_FZD1_LRP5
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
Naive CD4 T 1 1 1 1 1 1 1 1 1
Memory CD4 T 1 1 1 1 1 1 1 1 1
CD14+ Mono 1 1 1 1 1 1 1 1 1
B 1 1 1 1 1 1 1 1 1
CD8 T 1 1 1 1 1 1 1 1 1
FCGR3A+ Mono 1 1 1 1 1 1 1 1 1
NK 1 1 1 1 1 1 1 1 1
DC 1 1 1 1 1 1 1 1 1
Platelet 1 1 1 1 1 1 1 1 1
, , WNT10A_FZD2_LRP5
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
Naive CD4 T 1 1 1 1 1 1 1 1 1
Memory CD4 T 1 1 1 1 1 1 1 1 1
CD14+ Mono 1 1 1 1 1 1 1 1 1
B 1 1 1 1 1 1 1 1 1
CD8 T 1 1 1 1 1 1 1 1 1
FCGR3A+ Mono 1 1 1 1 1 1 1 1 1
NK 1 1 1 1 1 1 1 1 1
DC 1 1 1 1 1 1 1 1 1
Platelet 1 1 1 1 1 1 1 1 1
, , WNT10A_FZD3_LRP5
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
Naive CD4 T 1 1 1 1 1 1 1 1 1
Memory CD4 T 1 1 1 1 1 1 1 1 1
CD14+ Mono 1 1 1 1 1 1 1 1 1
B 1 1 1 1 1 1 1 1 1
CD8 T 1 1 1 1 1 1 1 1 1
FCGR3A+ Mono 1 1 1 1 1 1 1 1 1
NK 1 1 1 1 1 1 1 1 1
DC 1 1 1 1 1 1 1 1 1
Platelet 1 1 1 1 1 1 1 1 1
, , WNT10A_FZD6_LRP5
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
Naive CD4 T 1 1 1 1 1 1 1 1 1
Memory CD4 T 1 1 1 1 1 1 1 1 1
CD14+ Mono 1 1 1 1 1 1 1 1 1
B 1 1 1 1 1 1 1 1 1
CD8 T 1 1 1 1 1 1 1 1 1
FCGR3A+ Mono 1 1 1 1 1 1 1 1 1
NK 1 1 1 1 1 1 1 1 1
DC 1 1 1 1 1 1 1 1 1
Platelet 1 1 1 1 1 1 1 1 1
, , WNT10B_FZD1_LRP5
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
Naive CD4 T 1 1 1 1 1 1 1 1 1
Memory CD4 T 1 1 1 1 1 1 1 1 1
CD14+ Mono 1 1 1 1 1 1 1 1 1
B 1 1 1 1 1 1 1 1 1
CD8 T 1 1 1 1 1 1 1 1 1
FCGR3A+ Mono 1 1 1 1 1 1 1 1 1
NK 1 1 1 1 1 1 1 1 1
DC 1 1 1 1 1 1 1 1 1
Platelet 1 1 1 1 1 1 1 1 1
, , WNT10B_FZD2_LRP5
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
Naive CD4 T 1 1 1 1 1 1 1 1 1
Memory CD4 T 1 1 1 1 1 1 1 1 1
CD14+ Mono 1 1 1 1 1 1 1 1 1
B 1 1 1 1 1 1 1 1 1
CD8 T 1 1 1 1 1 1 1 1 1
FCGR3A+ Mono 1 1 1 1 1 1 1 1 1
NK 1 1 1 1 1 1 1 1 1
DC 1 1 1 1 1 1 1 1 1
Platelet 1 1 1 1 1 1 1 1 1
, , WNT10B_FZD3_LRP5
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
Naive CD4 T 1 1 1 1 1 1 1 1 1
Memory CD4 T 1 1 1 1 1 1 1 1 1
CD14+ Mono 1 1 1 1 1 1 1 1 1
B 1 1 1 1 1 1 1 1 1
CD8 T 1 1 1 1 1 1 1 1 1
FCGR3A+ Mono 1 1 1 1 1 1 1 1 1
NK 1 1 1 1 1 1 1 1 1
DC 1 1 1 1 1 1 1 1 1
Platelet 1 1 1 1 1 1 1 1 1
, , WNT10B_FZD6_LRP5
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
Naive CD4 T 1 1 1 1 1 1 1 1 1
Memory CD4 T 1 1 1 1 1 1 1 1 1
CD14+ Mono 1 1 1 1 1 1 1 1 1
B 1 1 1 1 1 1 1 1 1
CD8 T 1 1 1 1 1 1 1 1 1
FCGR3A+ Mono 1 1 1 1 1 1 1 1 1
NK 1 1 1 1 1 1 1 1 1
DC 1 1 1 1 1 1 1 1 1
Platelet 1 1 1 1 1 1 1 1 1
, , WNT16_FZD1_LRP5
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
Naive CD4 T 1 1 1 1 1 1 1 1 1
Memory CD4 T 1 1 1 1 1 1 1 1 1
CD14+ Mono 1 1 1 1 1 1 1 1 1
[ reached getOption("max.print") -- omitted 6 row(s) and 114 matrix slice(s) ]
$count
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
Naive CD4 T 4 9 15 5 11 21 12 14 6
Memory CD4 T 13 21 22 9 22 31 21 21 13
CD14+ Mono 12 20 25 12 23 28 26 28 14
B 3 6 11 4 6 17 9 11 6
CD8 T 7 13 22 7 15 27 20 19 12
FCGR3A+ Mono 12 25 28 12 22 33 26 30 15
NK 10 19 24 9 20 26 21 23 12
DC 13 24 25 13 21 32 22 26 18
Platelet 2 6 10 2 10 11 10 11 9
$sum
Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet
Naive CD4 T 5.235731e-04 6.742952e-04 3.909235e-04 7.501420e-04 5.434838e-04 3.885489e-04 1.610436e-04 4.846562e-05 4.413932e-06
Memory CD4 T 1.007867e-03 1.385925e-03 6.727733e-04 1.319087e-03 1.129907e-03 6.201049e-04 4.244407e-04 1.029006e-04 2.323768e-05
CD14+ Mono 2.212146e-04 3.583798e-04 1.213175e-03 5.313253e-04 5.061446e-04 5.027468e-04 2.294104e-04 8.682125e-05 2.022770e-05
B 1.301160e-05 9.973032e-05 1.565374e-04 3.703069e-04 1.646528e-04 2.057724e-04 4.275688e-05 2.459992e-05 3.097154e-06
CD8 T 7.640382e-04 9.283023e-04 4.849123e-04 6.086610e-04 1.986549e-03 1.788599e-04 8.787072e-04 5.912427e-05 9.023021e-05
FCGR3A+ Mono 1.374292e-04 2.766033e-04 4.453398e-04 1.984605e-04 1.309001e-04 2.772841e-04 6.165247e-05 3.351834e-05 9.078602e-07
NK 4.436511e-04 4.983154e-04 3.013077e-04 3.858570e-04 1.078647e-03 9.820542e-05 4.720637e-04 3.638077e-05 4.795777e-05
DC 3.642583e-05 8.053200e-05 1.016134e-04 9.111682e-05 6.074735e-05 6.164358e-05 2.886705e-05 1.000832e-05 1.323708e-06
Platelet 2.580361e-05 3.406017e-05 1.414725e-05 1.492857e-05 9.745813e-05 3.867913e-06 4.425967e-05 2.105407e-06 4.930773e-06
head(cellchat@netP$similarity)
head(cellchat@net$count)
head(cellchat@net$prob)
head(cellchat@net$sum)
head(cellchat@DB)
head(cellchat@var.features)
github 仓库在：
https://github.com/sqjin/CellChat
https://www.youtube.com/watch?v=kc45au1RhNs
https://www.youtube.com/watch?v=lag9UstpYhk
老师在youtube的讲解
