前语
咱们知道，细胞间信息传递办法一个是细胞表面配受体的相互作用，另一个经过细胞发生的可溶性小分子，即细胞因子。在单细胞数据剖析中下游，有时分咱们想看某几种细胞类型之间的相互作用，就有人推荐咱们做一个配受体剖析。那什么是配受体？咱们在文章细胞互作：单细胞配受体库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便是基因对。
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
今日，咱们就用CellChat来剖析一下咱们的PBMC数据，看看配受体剖析的一般流程。
除了从任何给定的scRNA-seq数据揣度细胞间通讯外，CellChat还供给了进一步的数据探索、剖析和可视化功用。
- 它能够剖析细胞与细胞间的通讯，以取得细胞发展轨道上的接连状况。
- 该办法结合社会网络剖析、形式辨认和多种学习办法，能够定量地描述和比较揣度出的细胞间通讯网络。
- 它供给了一个易于运用的东西来提取和可视化揣度网络信息。例如，它能够随时猜测一切细胞群的首要信号输入和输出，以及这些细胞群和信号怎么和谐在一起实现功用。
- 它供给了几个可视化输出，以方便用户引导的直观数据解释。
devtools::install_github(\"sqjin/CellChat\")
CellChat需求两个输入:
- 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应该在带有称号的列中。CellChat剖析的输入是均一化的数据(Seurat@assay$RNA@data)。假如用户供给counts数据，能够用normalizeData函数来均一化。对于细胞的信息，需求一个带有rownames的数据格式作为CellChat的输入。
这两个文件在咱们熟悉的Seurat方针中是很容易找到的，一个是均一化之后的数据，一个是细胞类型在metadata中。那么就让咱们开端chat之旅吧。
数据配置
首先，咱们加载包和引进实例数据。
library(CellChat) library(ggplot2) library(ggalluvial) library(svglite) library(Seurat) library(SeuratData) options(stringsAsFactors = FALSE)
咱们用Seurat给出的pbmc3k.final数据集，大部分的核算现已存在其方针中了：
pbmc3k.final An object of class Seurat 13714 features across 2638 samples within 1 assay Active assay: RNA (13714 features, 2000 variable features) 2 dimensional reductions calculated: pca, umap pbmc3k.final@commands$FindClusters # 你也看一看作者的其他指令，Seurat是记录其剖析进程的。 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
按照咱们方才说的，咱们在Seurat方针中提出CellChat需求的数据：
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
创立一个Cell Chat方针。
cellchat <- createCellChat(data = data.input)
cellchat
An object of class CellChat
13714 genes.
2638 cells.
summary(cellchat)
Length Class Mode
1 CellChat S4S4 类学会了吗？
在学习单细胞数据剖析东西的时分，在知道了要干嘛之后，第二步便是看数据格式，俗称：单细胞数据格式。这儿给出一个简略的可视化数据结构的办法：导图。
library(mindr) (out <- capture.output(str(cellchat))) out2 <- paste(out, collapse=\"n\") mm(gsub(\"..@\",\"# \",gsub(\".. \",\"#\",out2)),type =\"text\")
当然，咱们能够用str来看，便是有点冗长：
> 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()
咱们把metadata信息加到CellChat方针中，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供给了人和小鼠的配受体数据库，别离能够用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要一个基因对。这样，咱们就能够愈加扎实地把脚落到pathway上面了。在CellChat中，咱们还能够先择特定的信息描述细胞间的相互作者，这个能够理解为从特定的旁边面来描写细胞间相互作用,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
有哪些能够挑选的旁边面呢？
> 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，用于细胞间的通讯剖析。首先在一个细胞组中辨认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。假如配体或受体过表达，则辨认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 这儿好像有一些bug，在Linux上竟然不行。de了它。 cellchat <- identifyOverExpressedGenes(cellchat) cellchat <- identifyOverExpressedInteractions(cellchat) cellchat <- projectData(cellchat, PPI.human)
相互作用揣度
然后，咱们经过为每个相互作用分配一个概率值并进行置换查验来揣度生物含义上的细胞-细胞通讯。
# cellchat <- computeCommunProb(cellchat) 注意这个函数假如你能够用就用，这个是作者的。 mycomputeCommunProb <-edit(computeCommunProb) # computeCommunProb内部好像有一些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))))？ 否则矩阵返回的不对。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：现已修订。大家遇到问题也能够直接在GitHub上发问和回复。下面是比如（与本文无关）：
进入GitHub库房：https://github.com/sqjin/CellChat，点击Issues
就能够经行提交问题了，对话框是支持markerdown语法的。如咱们的比如。
这个对话有两点值得咱们学习：
- 发问者说的很清楚，代码具体到哪一行，并且给出了示例。
- 回答者很快检查代码，并做了回应。
好了，咱们能够接着往下走了。
估测细胞间在信号通路水平上的通讯。咱们还经过核算与每个信号通路相关的一切配体-受体相互作用的通讯概率来揣度信号通路水平上的通讯概率。
注:估测的每个配体-受体对的细胞间通讯网络和每个信号通路别离存储在“net”和“netP”槽中。
咱们能够经过核算链路的数量或汇总通讯概率来核算细胞间的聚合通讯网络。
cellchat <- computeCommunProbPathway(cellchat) cellchat <- aggregateNet(cellchat)
让咱们看看这成果。
> 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
在揣度细胞-细胞通讯网络的基础上，CellChat为进一步的探索、剖析和可视化供给了各种功用。
- 经过结合社会网络剖析、形式辨认和多种学习办法的归纳办法，t能够定量地描述和比较揣度出的细胞-细胞通讯网络。
- 它供给了一个易于运用的东西来提取和可视化揣度网络的高阶信息。例如，它能够随时猜测一切细胞群的首要信号输入和输出，以及这些细胞群和信号怎么和谐在一起实现功用。
你能够运用层次图或圈图可视化每个信号通路。 假如运用层次图可视化通讯网络，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方针的细胞组的索引。咱们能够运用netVisual_aggregate来可视化信号途径的揣度通讯网络，并运用netVisual_individual来可视化与该信号途径相关的单个L-R对的通讯网络。
在层次图中，实体圆和空心圆别离表明源和方针。圆的大小与每个细胞组的细胞数成比例。边缘颜色与信源共同。线越粗，信号越强。这儿咱们展示了一个MIF信号网络的比如。一切显示重要通讯的信令途径都能够经过cellchat@netP$pathways拜访。
>cellchat@netP$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) 原函数这儿好像有一个和igraph相关的小问题在不同igraph可能会体现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)
核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)
辨认细胞群的信号转导作用，经过核算每个细胞群的网络中心性方针，CellChat答应随时辨认细胞间通讯网络中的首要发送者、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)
···
辨认特定细胞群的大局通讯形式和首要信号。除了探索单个通路的具体通讯外，一个重要的问题是多个细胞群和信号通路怎么和谐运作。CellChat采用形式辨认办法来辨认大局通讯形式以及每个小群的关键信号。
辨认分泌细胞外向交流形式。随着形式数量的增加，可能会呈现冗余的形式，使得解释通讯形式变得困难。咱们挑选了5种形式作为默认形式。一般来说，当形式的数量大于2时就能够以为具有生物学含义。
nPatterns = 5 # 同样在这儿遇到了bug，难道说是我没有装置好吗，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\")
辨认方针细胞的传入(incoming)通讯形式。
netAnalysis_river(cellchat, pattern = \"incoming\")
netAnalysis_dot(cellchat, pattern = \"incoming\")
作为结尾有很多的空间，咱们得以先看看cellchat配受体揣度的结构是怎么的。
> 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 库房在：
https://github.com/sqjin/CellChat
https://www.youtube.com/watch?v=kc45au1RhNs
https://www.youtube.com/watch?v=lag9UstpYhk
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