CellChat:细胞间相互作用分析利器

CellChat:细胞间相互作用分析利器-图片1

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就是基因对。

CellChat:细胞间相互作用分析利器-图片2

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。
  1. 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。

  1. library(CellChat)
  2. library(ggplot2)
  3. library(ggalluvial)
  4. library(svglite)
  5. library(Seurat)
  6. library(SeuratData)
  7. 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:

  1. pbmc3k.final
  2.  
  3. An object of class Seurat
  4. 13714 features across 2638 samples within 1 assay
  5. Active assay: RNA (13714 features, 2000 variable features)
  6. 2 dimensional reductions calculated: pca, umap
  7.  
  8.  
  9. 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。
  10. Command: FindClusters(pbmc3k.final, resolution = 0.5)
  11. Time: 2020-04-30 12:54:53
  12. graph.name : RNA_snn
  13. modularity.fxn : 1
  14. resolution : 0.5
  15. method : matrix
  16. algorithm : 1
  17. n.start : 10
  18. n.iter : 10
  19. random.seed : 0
  20. group.singletons : TRUE
  21. 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:

  1. data.input <- pbmc3k.final@assays$RNA@data
  2. identity = data.frame(group =pbmc3k.final$seurat_annotations , row.names = names(pbmc3k.final$seurat_annotations)) # create a dataframe consisting of the cell labels
  3. unique(identity$group) # check the cell labels
  4.  
  5. [1] Memory CD4 T B CD14 Mono NK CD8 T Naive CD4 T FCGR3A Mono DC Platelet
  6. 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。

  1. cellchat <- createCellChat(data = data.input)
  2. cellchat
  3.  
  4. An object of class CellChat
  5. 13714 genes.
  6. 2638 cells.
  7.  
  8. summary(cellchat)
  9.  
  10. Length Class Mode
  11. 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。这里给出一个简单的可视化数据结构的方法:导图。

  1. library(mindr)
  2. (out <- capture.output(str(cellchat)))
  3. out2 <- paste(out, collapse="n")
  4. mm(gsub("..@","# ",gsub(".. ","#",out2)),type ="text")
CellChat:细胞间相互作用分析利器-图片3

当然,我们可以用str来看,就是有点冗长:

  1. > str(cellchat)
  2. Formal class 'CellChat' [package "CellChat"] with 14 slots
  3. ..@ data.raw : num[0 , 0 ]
  4. ..@ data :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
  5. .. .. ..@ i : int [1:2238732] 29 73 80 148 163 184 186 227 229 230 ...
  6. .. .. ..@ p : int [1:2639] 0 779 2131 3260 4220 4741 5522 6304 7094 7626 ...
  7. .. .. ..@ Dim : int [1:2] 13714 2638
  8. .. .. ..@ Dimnames:List of 2
  9. .. .. .. ..$ : chr [1:13714] "AL627309.1" "AP006222.2" "RP11-206L10.2" "RP11-206L10.9" ...
  10. .. .. .. ..$ : chr [1:2638] "AAACATACAACCAC" "AAACATTGAGCTAC" "AAACATTGATCAGC" "AAACCGTGCTTCCG" ...
  11. .. .. ..@ x : num [1:2238732] 1.64 1.64 2.23 1.64 1.64 ...
  12. .. .. ..@ factors : list()
  13. ..@ data.signaling: num[0 , 0 ]
  14. ..@ data.scale : num[0 , 0 ]
  15. ..@ data.project : num[0 , 0 ]
  16. ..@ net : list()
  17. ..@ netP : list()
  18. ..@ meta :'data.frame': 0 obs. of 0 variables
  19. Formal class 'data.frame' [package "methods"] with 4 slots
  20. .. .. ..@ .Data : list()
  21. .. .. ..@ names : chr(0)
  22. .. .. ..@ row.names: int(0)
  23. .. .. ..@ .S3Class : chr "data.frame"
  24. ..@ idents :Formal class 'factor' [package "methods"] with 3 slots
  25. .. .. ..@ .Data : int(0)
  26. .. .. ..@ levels : chr(0)
  27. .. .. ..@ .S3Class: chr "factor"
  28. ..@ DB : list()
  29. ..@ LR : list()
  30. ..@ var.features : logi(0)
  31. ..@ dr : list()
  32. ..@ options : list()

我们把metadata信息加到CellChat对象中,这个写法跟Seurat很像啊。

  1. cellchat <- addMeta(cellchat, meta = identity, meta.name = "labels")
  2. cellchat <- setIdent(cellchat, ident.use = "labels") # set "labels" as default cell identity
  3. levels(cellchat@idents) # show factor levels of the cell labels
  4.  
  5. [1] "Naive CD4 T" "Memory CD4 T" "CD14 Mono" "B" "CD8 T" "FCGR3A Mono" "NK"
  6.  
  7. groupSize <- as.numeric(table(cellchat@idents)) # number of cells in each cell group
  8.  
  9. [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。

  1. CellChatDB <- CellChatDB.human
  2. (out3 <- capture.output(str(CellChatDB)))
  3. out4 <- paste(out3, collapse="n")
  4. mm(gsub("$","# ",gsub(".. ","#",out4)),type ="text")
CellChat:细胞间相互作用分析利器-图片4

这个数据库的信息是很全面的:

  1. > colnames(CellChatDB$interaction)
  2. [1] "interaction_name" "pathway_name" "ligand" "receptor" "agonist" "antagonist" "co_A_receptor"
  3. [8] "co_I_receptor" "evidence" "annotation" "interaction_name_2"
  4. > CellChatDB$interaction[1:4,1:4]
  5. interaction_name pathway_name ligand receptor
  6. TGFB1_TGFBR1_TGFBR2 TGFB1_TGFBR1_TGFBR2 TGFb TGFB1 TGFbR1_R2
  7. TGFB2_TGFBR1_TGFBR2 TGFB2_TGFBR1_TGFBR2 TGFb TGFB2 TGFbR1_R2
  8. TGFB3_TGFBR1_TGFBR2 TGFB3_TGFBR1_TGFBR2 TGFb TGFB3 TGFbR1_R2
  9. TGFB1_ACVR1B_TGFBR2 TGFB1_ACVR1B_TGFBR2 TGFb TGFB1 ACVR1B_TGFbR2
  10. > head(CellChatDB$cofactor)
  11. cofactor1 cofactor2 cofactor3 cofactor4 cofactor5 cofactor6 cofactor7 cofactor8 cofactor9 cofactor10 cofactor11 cofactor12
  12. ACTIVIN antagonist FST
  13. ACTIVIN inhibition receptor BAMBI
  14. ANGPT inhibition receptor 1 TIE1
  15. ANGPT inhibition receptor 2 PTPRB
  16. BMP antagonist NBL1 GREM1 GREM2 CHRD NOG BMP3 LEFTY1 LEFTY2
  17. BMP inhibition receptor BAMBI
  18. cofactor13 cofactor14 cofactor15 cofactor16
  19. ACTIVIN antagonist
  20. ACTIVIN inhibition receptor
  21. ANGPT inhibition receptor 1
  22. ANGPT inhibition receptor 2
  23. BMP antagonist
  24. BMP inhibition receptor
  25. > head(CellChatDB$complex)
  26. subunit_1 subunit_2 subunit_3 subunit_4
  27. Activin AB INHBA INHBB
  28. Inhibin A INHA INHBA
  29. Inhibin B INHA INHBB
  30. IL12AB IL12A IL12B
  31. IL23 complex IL12B IL23A
  32. IL27 complex IL27 EBI3
  33. > head(CellChatDB$geneInfo)
  34. Symbol Name EntrezGene.ID Ensembl.Gene.ID MGI.ID Gene.group.name
  35. HGNC:5 A1BG alpha-1-B glycoprotein 1 ENSG00000121410 MGI:2152878 Immunoglobulin like domain containing
  36. HGNC:37133 A1BG-AS1 A1BG antisense RNA 1 503538 ENSG00000268895 Antisense RNAs
  37. HGNC:24086 A1CF APOBEC1 complementation factor 29974 ENSG00000148584 MGI:1917115 RNA binding motif containing
  38. HGNC:7 A2M alpha-2-macroglobulin 2 ENSG00000175899 MGI:2449119 C3 and PZP like, alpha-2-macroglobulin domain containing
  39. HGNC:27057 A2M-AS1 A2M antisense RNA 1 144571 ENSG00000245105 Antisense RNAs
  40. 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。

  1. CellChatDB.use <- subsetDB(CellChatDB, search = "Secreted Signaling") # use Secreted Signaling for cell-cell communication analysis
  2. cellchat@DB <- CellChatDB.use # set the used database in the object

有哪些可以选择的侧面呢?

  1. > unique(CellChatDB$interaction$annotation)
  2. [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。

  1. cellchat <- subsetData(cellchat) # subset the expression data of signaling genes for saving computation cost
  2. 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。
  3. cellchat <- identifyOverExpressedGenes(cellchat)
  4. cellchat <- identifyOverExpressedInteractions(cellchat)
  5. 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。

  1. # cellchat <- computeCommunProb(cellchat) 注意这个函数如果你可以用就用,这个是作者的。
  2. 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。
  3. environment(mycomputeCommunProb) <- environment(computeCommunProb)
  4. 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

CellChat:细胞间相互作用分析利器-图片5

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。

  1. cellchat <- computeCommunProbPathway(cellchat)
  2. cellchat <- aggregateNet(cellchat)

让我们看看这结果。

  1. > cellchat@netP$pathways
  2. [1] "TGFb" "NRG" "PDGF" "CCL" "CXCL" "MIF" "IL2" "IL6" "IL10" "IL1" "CSF"
  3. [12] "IL16" "IFN-II" "LT" "LIGHT" "FASLG" "TRAIL" "BAFF" "CD40" "VISFATIN" "COMPLEMENT" "PARs"
  4. [23] "FLT3" "ANNEXIN" "GAS" "GRN" "GALECTIN" "BTLA" "BAG"
  5. > head(cellchat@LR$LRsig)
  6. interaction_name pathway_name ligand receptor agonist antagonist co_A_receptor co_I_receptor
  7. TGFB1_TGFBR1_TGFBR2 TGFB1_TGFBR1_TGFBR2 TGFb TGFB1 TGFbR1_R2 TGFb agonist TGFb antagonist TGFb inhibition receptor
  8. TGFB1_ACVR1B_TGFBR2 TGFB1_ACVR1B_TGFBR2 TGFb TGFB1 ACVR1B_TGFbR2 TGFb agonist TGFb antagonist TGFb inhibition receptor
  9. TGFB1_ACVR1C_TGFBR2 TGFB1_ACVR1C_TGFBR2 TGFb TGFB1 ACVR1C_TGFbR2 TGFb agonist TGFb antagonist TGFb inhibition receptor
  10. TGFB1_ACVR1_TGFBR1 TGFB1_ACVR1_TGFBR1 TGFb TGFB1 ACVR1_TGFbR
  11. WNT10A_FZD1_LRP5 WNT10A_FZD1_LRP5 WNT WNT10A FZD1_LRP5 WNT agonist WNT antagonist WNT activation receptor WNT inhibition receptor
  12. WNT10A_FZD2_LRP5 WNT10A_FZD2_LRP5 WNT WNT10A FZD2_LRP5 WNT agonist WNT antagonist WNT activation receptor WNT inhibition receptor
  13. evidence annotation interaction_name_2
  14. TGFB1_TGFBR1_TGFBR2 KEGG: hsa04350 Secreted Signaling TGFB1 - (TGFBR1 TGFBR2)
  15. TGFB1_ACVR1B_TGFBR2 PMID: 27449815 Secreted Signaling TGFB1 - (ACVR1B TGFBR2)
  16. TGFB1_ACVR1C_TGFBR2 PMID: 27449815 Secreted Signaling TGFB1 - (ACVR1C TGFBR2)
  17. TGFB1_ACVR1_TGFBR1 PMID: 29376829 Secreted Signaling TGFB1 - (ACVR1 TGFBR1)
  18. WNT10A_FZD1_LRP5 KEGG: hsa04310; PMID: 23209157 Secreted Signaling WNT10A - (FZD1 LRP5)
  19. 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。

  1. >cellchat@netP$pathways
  2. [1] "TGFb" "NRG" "PDGF" "CCL" "CXCL" "MIF" "IL2" "IL6" "IL10" "IL1"
  3. [11] "CSF" "IL16" "IFN-II" "LT" "LIGHT" "FASLG" "TRAIL" "BAFF" "CD40" "VISFATIN"
  4. [21] "COMPLEMENT" "PARs" "FLT3" "ANNEXIN" "GAS" "GRN" "GALECTIN" "BTLA" "BAG"
  1. levels(cellchat@idents)
  2. vertex.receiver = seq(1,4) # a numeric vector
  3. # check the order of cell identity to set suitable vertex.receiver
  4. #cellchat@LR$LRsig$pathway_name
  5. #cellchat@LR$LRsig$antagonist
  6. pathways.show <- "MIF"
  7. # netVisual_aggregate(cellchat, signaling = pathways.show, vertex.receiver = vertex.receiver, vertex.size = groupSize) # 原函数
  8. 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。
CellChat:细胞间相互作用分析利器-图片6

经典的配受体圈图:

  1. 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。

  1. netAnalysis_contribution(cellchat, signaling = pathways.show)
CellChat:细胞间相互作用分析利器-图片7

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。

  1. 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)
···

CellChat:细胞间相互作用分析利器-图片8

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。

  1. nPatterns = 5
  2. # 同样在这里遇到了bug,难道说是我没有安装好吗,de了它。
  3. # cellchat <- myidentifyCommunicationPatterns(cellchat, pattern = "outgoing", k = nPatterns)
  4. myidentifyCommunicationPatterns <- edit(identifyCommunicationPatterns)
  5. environment(myidentifyCommunicationPatterns) <- environment(identifyCommunicationPatterns)
  6. cellchat <- myidentifyCommunicationPatterns(cellchat, pattern = "outgoing", k = nPatterns)
CellChat:细胞间相互作用分析利器-图片9
  1. # Visualize the communication pattern using river plot
  2. netAnalysis_river(cellchat, pattern = "outgoing")
CellChat:细胞间相互作用分析利器-图片9
  1. # Visualize the communication pattern using dot plot
  2. netAnalysis_dot(cellchat, pattern = "outgoing")
CellChat:细胞间相互作用分析利器-图片9

识别目标细胞的传入(incoming)通信模式。

  1. netAnalysis_river(cellchat, pattern = "incoming")
  1. 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。

  1. > head(cellchat@LR$LRsig)
  2. interaction_name pathway_name ligand receptor agonist antagonist co_A_receptor co_I_receptor
  3. TGFB1_TGFBR1_TGFBR2 TGFB1_TGFBR1_TGFBR2 TGFb TGFB1 TGFbR1_R2 TGFb agonist TGFb antagonist TGFb inhibition receptor
  4. TGFB1_ACVR1B_TGFBR2 TGFB1_ACVR1B_TGFBR2 TGFb TGFB1 ACVR1B_TGFbR2 TGFb agonist TGFb antagonist TGFb inhibition receptor
  5. TGFB1_ACVR1C_TGFBR2 TGFB1_ACVR1C_TGFBR2 TGFb TGFB1 ACVR1C_TGFbR2 TGFb agonist TGFb antagonist TGFb inhibition receptor
  6. TGFB1_ACVR1_TGFBR1 TGFB1_ACVR1_TGFBR1 TGFb TGFB1 ACVR1_TGFbR
  7. WNT10A_FZD1_LRP5 WNT10A_FZD1_LRP5 WNT WNT10A FZD1_LRP5 WNT agonist WNT antagonist WNT activation receptor WNT inhibition receptor
  8. WNT10A_FZD2_LRP5 WNT10A_FZD2_LRP5 WNT WNT10A FZD2_LRP5 WNT agonist WNT antagonist WNT activation receptor WNT inhibition receptor
  9. evidence annotation interaction_name_2
  10. TGFB1_TGFBR1_TGFBR2 KEGG: hsa04350 Secreted Signaling TGFB1 - (TGFBR1 TGFBR2)
  11. TGFB1_ACVR1B_TGFBR2 PMID: 27449815 Secreted Signaling TGFB1 - (ACVR1B TGFBR2)
  12. TGFB1_ACVR1C_TGFBR2 PMID: 27449815 Secreted Signaling TGFB1 - (ACVR1C TGFBR2)
  13. TGFB1_ACVR1_TGFBR1 PMID: 29376829 Secreted Signaling TGFB1 - (ACVR1 TGFBR1)
  14. WNT10A_FZD1_LRP5 KEGG: hsa04310; PMID: 23209157 Secreted Signaling WNT10A - (FZD1 LRP5)
  15. WNT10A_FZD2_LRP5 KEGG: hsa04310; PMID: 23209159 Secreted Signaling WNT10A - (FZD2 LRP5)
  16. > head(cellchat@dr)
  17. list()
  18. > head(cellchat@data)
  19. 6 x 2638 sparse Matrix of class "dgCMatrix"
  20. [[ suppressing 70 column names 'AAACATACAACCAC', 'AAACATTGAGCTAC', 'AAACATTGATCAGC' ... ]]
  21. AL627309.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
  22. AP006222.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
  23. RP11-206L10.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
  24. RP11-206L10.9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
  25. LINC00115 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
  26. NOC2L . . . . . . . . . . . 1.646272 . . . . . . . . 1.398186 . . . . . . . . . . . . 1.89939 . . . . . . . 1.36907 1.721224 . . . . . . . . .
  27. AL627309.1 . . . . . . . . . . . . . . . . . . ......
  28. AP006222.2 . . . . . . . . . . . . . . . . . . ......
  29. RP11-206L10.2 . . . . . . . . . . . . . . . . . . ......
  30. RP11-206L10.9 . . . . . . . . . . . . . . . . . . ......
  31. LINC00115 . . . . . . . . . . . . . . . . . . ......
  32. NOC2L . . . 1.568489 1.678814 . 1.253835 . . 3.791113 . . . . . . . . ......
  33.  
  34. .....suppressing 2568 columns in show(); maybe adjust 'options(max.print= *, width = *)'
  35. ..............................
  36. > head(cellchat@idents)
  37. [1] Memory CD4 T B Memory CD4 T CD14 Mono NK Memory CD4 T
  38. Levels: Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  39. > head(cellchat@meta)
  40. labels
  41. AAACATACAACCAC Memory CD4 T
  42. AAACATTGAGCTAC B
  43. AAACATTGATCAGC Memory CD4 T
  44. AAACCGTGCTTCCG CD14 Mono
  45. AAACCGTGTATGCG NK
  46. AAACGCACTGGTAC Memory CD4 T
  47. > head(cellchat@netP$pathways)
  48. [1] "TGFb" "NRG" "PDGF" "CCL" "CXCL" "MIF"
  49. > head(cellchat@netP$prob)
  50. [1] 0 0 0 0 0 0
  51. > head(cellchat@netP$centr)
  52. $TGFb
  53. $TGFb$outdeg
  54. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  55. 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
  56.  
  57. $TGFb$indeg
  58. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  59. 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
  60.  
  61. $TGFb$hub
  62. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  63. 0.00000000 0.02278982 1.00000000 0.00000000 0.04484954 0.37878876 0.37787064 0.01116456 0.13193619
  64.  
  65. $TGFb$authority
  66. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  67. 0.00000000 0.74712407 1.00000000 0.00000000 0.56314554 0.86435263 0.37969073 0.04280264 0.11659336
  68.  
  69. $TGFb$eigen
  70. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  71. 0.01217244 0.31304003 1.00000000 0.01217244 0.25802457 0.58202001 0.37843282 0.02320534 0.12622971
  72.  
  73. $TGFb$page_rank
  74. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  75. 0.02054795 0.13742492 0.21555291 0.02054795 0.11208641 0.31212523 0.09458943 0.02724384 0.05988138
  76.  
  77. $TGFb$betweenness
  78. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  79. 0 0 24 0 0 10 0 0 0
  80.  
  81. $TGFb$flowbet
  82. [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
  83.  
  84. $TGFb$info
  85. [1] 0.00000000 0.16628670 0.19401551 0.00000000 0.12870372 0.18191312 0.16895822 0.03556505 0.12455769
  86.  
  87.  
  88. $NRG
  89. $NRG$outdeg
  90. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  91. 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
  92.  
  93. $NRG$indeg
  94. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  95. 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
  96.  
  97. $NRG$hub
  98. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  99. 0.508835533 0.466696996 1.000000000 0.247709130 0.263909627 0.531501345 0.211169583 0.068881216 0.007422998
  100.  
  101. $NRG$authority
  102. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  103. 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
  104.  
  105. $NRG$eigen
  106. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  107. 0.36342198 0.33332567 0.71422288 0.17691953 0.18849029 0.37961042 0.15082215 0.04919654 1.00000000
  108.  
  109. $NRG$page_rank
  110. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  111. 0.01666667 0.01666667 0.01666667 0.01666667 0.01666667 0.01666667 0.01666667 0.01666667 0.86666667
  112.  
  113. $NRG$betweenness
  114. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  115. 0 0 0 0 0 0 0 0 0
  116.  
  117. $NRG$flowbet
  118. [1] 0 0 0 0 0 0 0 0 0
  119.  
  120. $NRG$info
  121. [1] 0 0 0 0 0 0 0 0 0
  122.  
  123.  
  124. $PDGF
  125. $PDGF$outdeg
  126. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  127. 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
  128.  
  129. $PDGF$indeg
  130. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  131. 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
  132.  
  133. $PDGF$hub
  134. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  135. 0.09759699 0.32222056 1.00000000 0.00000000 0.18684898 0.65291566 0.35275497 0.08152314 0.00000000
  136.  
  137. $PDGF$authority
  138. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  139. 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
  140.  
  141. $PDGF$eigen
  142. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  143. 0.44237332 0.51181753 1.00000000 0.07396075 0.29250188 0.67517921 0.29135234 0.07823533 0.07396075
  144.  
  145. $PDGF$page_rank
  146. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  147. 0.15091590 0.12046482 0.24044555 0.02054795 0.07685927 0.27934926 0.05452706 0.03634225 0.02054795
  148.  
  149. $PDGF$betweenness
  150. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  151. 1 0 18 0 0 5 0 0 0
  152.  
  153. $PDGF$flowbet
  154. [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
  155.  
  156. $PDGF$info
  157. [1] 0.16144948 0.14611532 0.20300365 0.00000000 0.10956327 0.17885050 0.14080069 0.06021709 0.00000000
  158.  
  159.  
  160. $CCL
  161. $CCL$outdeg
  162. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  163. 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
  164.  
  165. $CCL$indeg
  166. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  167. 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
  168.  
  169. $CCL$hub
  170. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  171. 0.050735945 0.193934101 0.210819077 0.029282445 1.000000000 0.003249727 0.551908511 0.013914236 0.052892139
  172.  
  173. $CCL$authority
  174. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  175. 0.30095289 0.35990610 0.14750945 0.17431275 1.00000000 0.03323390 0.45558530 0.02222082 0.04989215
  176.  
  177. $CCL$eigen
  178. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  179. 0.17214374 0.27750548 0.17861545 0.09964869 1.00000000 0.01772801 0.50285164 0.01802822 0.05152917
  180.  
  181. $CCL$page_rank
  182. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  183. 0.08937815 0.10366984 0.05186354 0.05878616 0.41583926 0.02465234 0.19773523 0.02202754 0.03604793
  184.  
  185. $CCL$betweenness
  186. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  187. 0 0 0 0 56 0 0 0 0
  188.  
  189. $CCL$flowbet
  190. [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
  191.  
  192. $CCL$info
  193. [1] 0.13488584 0.13862093 0.12659975 0.11726949 0.15963716 0.03961851 0.15306688 0.04024833 0.09005310
  194.  
  195.  
  196. $CXCL
  197. $CXCL$outdeg
  198. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  199. 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
  200.  
  201. $CXCL$indeg
  202. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  203. 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
  204.  
  205. $CXCL$hub
  206. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  207. 0 0 0 0 0 0 0 0 1
  208.  
  209. $CXCL$authority
  210. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  211. 1.00000000 0.90554935 0.79734257 0.43753795 0.47944431 0.61609465 0.39381731 0.07716707 0.01037905
  212.  
  213. $CXCL$eigen
  214. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  215. 0.5394037 0.4884566 0.4300895 0.2360096 0.2586140 0.3323237 0.2124265 0.0416242 1.0000000
  216.  
  217. $CXCL$page_rank
  218. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  219. 0.1198308 0.1181000 0.1161172 0.1095240 0.1102919 0.1127960 0.1087229 0.1029205 0.1016966
  220.  
  221. $CXCL$betweenness
  222. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  223. 0 0 0 0 0 0 0 0 0
  224.  
  225. $CXCL$flowbet
  226. [1] 0 0 0 0 0 0 0 0 0
  227.  
  228. $CXCL$info
  229. [1] 0.12994155 0.12702636 0.12305974 0.10129559 0.10488823 0.11427509 0.09707279 0.03583427 0.16660638
  230.  
  231.  
  232. $MIF
  233. $MIF$outdeg
  234. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  235. 0.0012989751 0.0039272021 0.0006234461 0.0006401726 0.0005135156 0.0002049902 0.0003848437 0.0001321595 0.0000000000
  236.  
  237. $MIF$indeg
  238. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  239. 0.0005188736 0.0008184262 0.0007859180 0.0035144980 0.0009227472 0.0008137752 0.0001170739 0.0002339928 0.0000000000
  240.  
  241. $MIF$hub
  242. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  243. 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
  244.  
  245. $MIF$authority
  246. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  247. 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
  248.  
  249. $MIF$eigen
  250. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  251. 0.40785736 1.00000000 0.28217435 0.81714092 0.31062247 0.21639268 0.11882053 0.07323643 0.01492405
  252.  
  253. $MIF$page_rank
  254. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  255. 0.02128513 0.02564654 0.12732754 0.50874503 0.11392566 0.11715499 0.01911107 0.04839913 0.01840491
  256.  
  257. $MIF$betweenness
  258. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  259. 0 10 0 17 14 0 11 0 0
  260.  
  261. $MIF$flowbet
  262. [1] 0.0010772004 0.0004430504 0.0013253722 0.0018828736 0.0013830050 0.0007361476 0.0002572374 0.0005064761 0.0000000000
  263.  
  264. $MIF$info
  265. [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。

  1. > head(cellchat@netP$pattern$outgoing$pattern$cell)
  2. CellGroup Pattern Contribution
  3. 1 Naive CD4 T Pattern 1 9.182571e-01
  4. 2 Memory CD4 T Pattern 1 8.643879e-01
  5. 3 CD14 Mono Pattern 1 6.958107e-04
  6. 4 B Pattern 1 8.943340e-01
  7. 5 CD8 T Pattern 1 8.497941e-02
  8. 6 FCGR3A Mono Pattern 1 2.351798e-05
  9. > head(cellchat@netP$pattern$outgoing$pattern$signaling)
  10. Pattern Signaling Contribution
  11. 1 Pattern 1 TGFb 1.509635e-08
  12. 2 Pattern 2 TGFb 5.851347e-01
  13. 3 Pattern 3 TGFb 2.021400e-01
  14. 4 Pattern 4 TGFb 4.466321e-08
  15. 5 Pattern 5 TGFb 2.127253e-01
  16. 6 Pattern 1 NRG 3.333424e-01
  17. > head(cellchat@netP$pattern$outgoing$data)
  18. TGFb NRG PDGF CCL CXCL MIF IL2 IL6 IL10 IL1 CSF IL16 IFN-II
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. LT LIGHT FASLG TRAIL BAFF CD40 VISFATIN COMPLEMENT PARs FLT3 ANNEXIN GAS
  26. 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
  27. 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
  28. 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
  29. B 0.5629699 0.0000000 0.06312626 0.00000000 8.393504e-05 0.0003093270 0 0.3587101 0 0.0003490343 0.0003780528 0.01054186
  30. 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
  31. 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
  32. GRN GALECTIN BTLA BAG
  33. Naive CD4 T 0.0000000 0.0000000 0.0000000 1.0000000
  34. Memory CD4 T 0.0000000 0.0000000 1.0000000 0.9388102
  35. CD14 Mono 1.0000000 0.8983294 0.0000000 0.7920962
  36. B 0.0000000 0.0000000 0.5998942 0.4454517
  37. CD8 T 0.0000000 0.0000000 0.0000000 0.4831780
  38. FCGR3A Mono 0.1277283 1.0000000 0.2785847 0.3247730
  39. > cellchat@net
  40. $prob
  41. , , TGFB1_TGFBR1_TGFBR2
  42.  
  43. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  44. 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
  45. 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
  46. 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
  47. 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
  48. 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
  49. 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
  50. 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
  51. 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
  52. 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
  53.  
  54. , , TGFB1_ACVR1B_TGFBR2
  55.  
  56. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  57. 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
  58. 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
  59. 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
  60. 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
  61. 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
  62. 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
  63. 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
  64. 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
  65. 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
  66.  
  67. , , TGFB1_ACVR1C_TGFBR2
  68.  
  69. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  70. Naive CD4 T 0 0 0 0 0 0 0 0 0
  71. Memory CD4 T 0 0 0 0 0 0 0 0 0
  72. CD14 Mono 0 0 0 0 0 0 0 0 0
  73. B 0 0 0 0 0 0 0 0 0
  74. CD8 T 0 0 0 0 0 0 0 0 0
  75. FCGR3A Mono 0 0 0 0 0 0 0 0 0
  76. NK 0 0 0 0 0 0 0 0 0
  77. DC 0 0 0 0 0 0 0 0 0
  78. Platelet 0 0 0 0 0 0 0 0 0
  79.  
  80. , , TGFB1_ACVR1_TGFBR1
  81.  
  82. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  83. 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
  84. 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
  85. 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
  86. 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
  87. 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
  88. 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
  89. 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
  90. 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
  91. 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
  92.  
  93. , , WNT10A_FZD1_LRP5
  94.  
  95. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  96. Naive CD4 T 0 0 0 0 0 0 0 0 0
  97. Memory CD4 T 0 0 0 0 0 0 0 0 0
  98. CD14 Mono 0 0 0 0 0 0 0 0 0
  99. B 0 0 0 0 0 0 0 0 0
  100. CD8 T 0 0 0 0 0 0 0 0 0
  101. FCGR3A Mono 0 0 0 0 0 0 0 0 0
  102. NK 0 0 0 0 0 0 0 0 0
  103. DC 0 0 0 0 0 0 0 0 0
  104. Platelet 0 0 0 0 0 0 0 0 0
  105.  
  106. , , WNT10A_FZD2_LRP5
  107.  
  108. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  109. Naive CD4 T 0 0 0 0 0 0 0 0 0
  110. Memory CD4 T 0 0 0 0 0 0 0 0 0
  111. CD14 Mono 0 0 0 0 0 0 0 0 0
  112. B 0 0 0 0 0 0 0 0 0
  113. CD8 T 0 0 0 0 0 0 0 0 0
  114. FCGR3A Mono 0 0 0 0 0 0 0 0 0
  115. NK 0 0 0 0 0 0 0 0 0
  116. DC 0 0 0 0 0 0 0 0 0
  117. Platelet 0 0 0 0 0 0 0 0 0
  118.  
  119. , , WNT10A_FZD3_LRP5
  120.  
  121. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  122. Naive CD4 T 0 0 0 0 0 0 0 0 0
  123. Memory CD4 T 0 0 0 0 0 0 0 0 0
  124. CD14 Mono 0 0 0 0 0 0 0 0 0
  125. B 0 0 0 0 0 0 0 0 0
  126. CD8 T 0 0 0 0 0 0 0 0 0
  127. FCGR3A Mono 0 0 0 0 0 0 0 0 0
  128. NK 0 0 0 0 0 0 0 0 0
  129. DC 0 0 0 0 0 0 0 0 0
  130. Platelet 0 0 0 0 0 0 0 0 0
  131.  
  132. , , WNT10A_FZD6_LRP5
  133.  
  134. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  135. Naive CD4 T 0 0 0 0 0 0 0 0 0
  136. Memory CD4 T 0 0 0 0 0 0 0 0 0
  137. CD14 Mono 0 0 0 0 0 0 0 0 0
  138. B 0 0 0 0 0 0 0 0 0
  139. CD8 T 0 0 0 0 0 0 0 0 0
  140. FCGR3A Mono 0 0 0 0 0 0 0 0 0
  141. NK 0 0 0 0 0 0 0 0 0
  142. DC 0 0 0 0 0 0 0 0 0
  143. Platelet 0 0 0 0 0 0 0 0 0
  144.  
  145. , , WNT10B_FZD1_LRP5
  146.  
  147. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  148. Naive CD4 T 0 0 0 0 0 0 0 0 0
  149. Memory CD4 T 0 0 0 0 0 0 0 0 0
  150. CD14 Mono 0 0 0 0 0 0 0 0 0
  151. B 0 0 0 0 0 0 0 0 0
  152. CD8 T 0 0 0 0 0 0 0 0 0
  153. FCGR3A Mono 0 0 0 0 0 0 0 0 0
  154. NK 0 0 0 0 0 0 0 0 0
  155. DC 0 0 0 0 0 0 0 0 0
  156. Platelet 0 0 0 0 0 0 0 0 0
  157.  
  158. , , WNT10B_FZD2_LRP5
  159.  
  160. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  161. Naive CD4 T 0 0 0 0 0 0 0 0 0
  162. Memory CD4 T 0 0 0 0 0 0 0 0 0
  163. CD14 Mono 0 0 0 0 0 0 0 0 0
  164. B 0 0 0 0 0 0 0 0 0
  165. CD8 T 0 0 0 0 0 0 0 0 0
  166. FCGR3A Mono 0 0 0 0 0 0 0 0 0
  167. NK 0 0 0 0 0 0 0 0 0
  168. DC 0 0 0 0 0 0 0 0 0
  169. Platelet 0 0 0 0 0 0 0 0 0
  170.  
  171. , , WNT10B_FZD3_LRP5
  172.  
  173. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  174. Naive CD4 T 0 0 0 0 0 0 0 0 0
  175. Memory CD4 T 0 0 0 0 0 0 0 0 0
  176. CD14 Mono 0 0 0 0 0 0 0 0 0
  177. B 0 0 0 0 0 0 0 0 0
  178. CD8 T 0 0 0 0 0 0 0 0 0
  179. FCGR3A Mono 0 0 0 0 0 0 0 0 0
  180. NK 0 0 0 0 0 0 0 0 0
  181. DC 0 0 0 0 0 0 0 0 0
  182. Platelet 0 0 0 0 0 0 0 0 0
  183.  
  184. , , WNT10B_FZD6_LRP5
  185.  
  186. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  187. Naive CD4 T 0 0 0 0 0 0 0 0 0
  188. Memory CD4 T 0 0 0 0 0 0 0 0 0
  189. CD14 Mono 0 0 0 0 0 0 0 0 0
  190. B 0 0 0 0 0 0 0 0 0
  191. CD8 T 0 0 0 0 0 0 0 0 0
  192. FCGR3A Mono 0 0 0 0 0 0 0 0 0
  193. NK 0 0 0 0 0 0 0 0 0
  194. DC 0 0 0 0 0 0 0 0 0
  195. Platelet 0 0 0 0 0 0 0 0 0
  196.  
  197. , , WNT16_FZD1_LRP5
  198.  
  199. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  200. Naive CD4 T 0 0 0 0 0 0 0 0 0
  201. Memory CD4 T 0 0 0 0 0 0 0 0 0
  202. CD14 Mono 0 0 0 0 0 0 0 0 0
  203.  
  204. [ reached getOption("max.print") -- omitted 6 row(s) and 114 matrix slice(s) ]
  205.  
  206. $pval
  207. , , TGFB1_TGFBR1_TGFBR2
  208.  
  209. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  210. Naive CD4 T 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.99 0.56
  211. Memory CD4 T 1.00 0.67 0.39 1.00 0.33 0.00 0.15 0.44 0.01
  212. CD14 Mono 0.87 0.00 0.00 0.80 0.00 0.00 0.00 0.00 0.00
  213. B 1.00 1.00 0.98 1.00 0.99 0.95 0.95 0.99 0.69
  214. CD8 T 1.00 0.36 0.04 0.99 0.07 0.00 0.00 0.44 0.00
  215. FCGR3A Mono 0.73 0.00 0.00 0.67 0.00 0.00 0.00 0.00 0.00
  216. NK 0.74 0.00 0.00 0.70 0.00 0.00 0.00 0.01 0.00
  217. DC 0.70 0.20 0.21 0.68 0.22 0.00 0.10 0.26 0.01
  218. Platelet 0.52 0.00 0.00 0.48 0.00 0.00 0.00 0.00 0.00
  219.  
  220. , , TGFB1_ACVR1B_TGFBR2
  221.  
  222. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  223. Naive CD4 T 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.99 0.94
  224. Memory CD4 T 1.00 0.73 0.39 1.00 0.48 0.00 0.24 0.46 0.02
  225. CD14 Mono 0.87 0.00 0.00 0.78 0.00 0.00 0.00 0.00 0.00
  226. B 1.00 1.00 0.99 1.00 0.99 0.97 0.96 0.99 0.92
  227. CD8 T 0.92 0.39 0.04 0.93 0.16 0.00 0.00 0.45 0.00
  228. FCGR3A Mono 0.71 0.00 0.00 0.67 0.00 0.00 0.00 0.00 0.00
  229. NK 0.75 0.00 0.00 0.70 0.00 0.00 0.00 0.01 0.00
  230. DC 0.66 0.21 0.21 0.64 0.23 0.00 0.10 0.26 0.01
  231. Platelet 0.42 0.00 0.00 0.42 0.00 0.00 0.00 0.00 0.00
  232.  
  233. , , TGFB1_ACVR1C_TGFBR2
  234.  
  235. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  236. Naive CD4 T 1 1 1 1 1 1 1 1 1
  237. Memory CD4 T 1 1 1 1 1 1 1 1 1
  238. CD14 Mono 1 1 1 1 1 1 1 1 1
  239. B 1 1 1 1 1 1 1 1 1
  240. CD8 T 1 1 1 1 1 1 1 1 1
  241. FCGR3A Mono 1 1 1 1 1 1 1 1 1
  242. NK 1 1 1 1 1 1 1 1 1
  243. DC 1 1 1 1 1 1 1 1 1
  244. Platelet 1 1 1 1 1 1 1 1 1
  245.  
  246. , , TGFB1_ACVR1_TGFBR1
  247.  
  248. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  249. Naive CD4 T 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.99 0.95
  250. Memory CD4 T 1.00 0.75 0.46 1.00 0.38 0.00 0.22 0.47 0.02
  251. CD14 Mono 0.88 0.00 0.00 0.80 0.00 0.00 0.00 0.00 0.00
  252. B 1.00 1.00 1.00 1.00 0.99 0.97 0.98 0.99 0.91
  253. CD8 T 0.92 0.38 0.05 0.92 0.05 0.00 0.00 0.46 0.00
  254. FCGR3A Mono 0.71 0.00 0.00 0.67 0.00 0.00 0.00 0.00 0.00
  255. NK 0.76 0.00 0.00 0.71 0.00 0.00 0.00 0.02 0.00
  256. DC 0.66 0.21 0.23 0.63 0.23 0.00 0.12 0.25 0.01
  257. Platelet 0.40 0.00 0.00 0.40 0.00 0.00 0.00 0.00 0.00
  258.  
  259. , , WNT10A_FZD1_LRP5
  260.  
  261. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  262. Naive CD4 T 1 1 1 1 1 1 1 1 1
  263. Memory CD4 T 1 1 1 1 1 1 1 1 1
  264. CD14 Mono 1 1 1 1 1 1 1 1 1
  265. B 1 1 1 1 1 1 1 1 1
  266. CD8 T 1 1 1 1 1 1 1 1 1
  267. FCGR3A Mono 1 1 1 1 1 1 1 1 1
  268. NK 1 1 1 1 1 1 1 1 1
  269. DC 1 1 1 1 1 1 1 1 1
  270. Platelet 1 1 1 1 1 1 1 1 1
  271.  
  272. , , WNT10A_FZD2_LRP5
  273.  
  274. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  275. Naive CD4 T 1 1 1 1 1 1 1 1 1
  276. Memory CD4 T 1 1 1 1 1 1 1 1 1
  277. CD14 Mono 1 1 1 1 1 1 1 1 1
  278. B 1 1 1 1 1 1 1 1 1
  279. CD8 T 1 1 1 1 1 1 1 1 1
  280. FCGR3A Mono 1 1 1 1 1 1 1 1 1
  281. NK 1 1 1 1 1 1 1 1 1
  282. DC 1 1 1 1 1 1 1 1 1
  283. Platelet 1 1 1 1 1 1 1 1 1
  284.  
  285. , , WNT10A_FZD3_LRP5
  286.  
  287. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  288. Naive CD4 T 1 1 1 1 1 1 1 1 1
  289. Memory CD4 T 1 1 1 1 1 1 1 1 1
  290. CD14 Mono 1 1 1 1 1 1 1 1 1
  291. B 1 1 1 1 1 1 1 1 1
  292. CD8 T 1 1 1 1 1 1 1 1 1
  293. FCGR3A Mono 1 1 1 1 1 1 1 1 1
  294. NK 1 1 1 1 1 1 1 1 1
  295. DC 1 1 1 1 1 1 1 1 1
  296. Platelet 1 1 1 1 1 1 1 1 1
  297.  
  298. , , WNT10A_FZD6_LRP5
  299.  
  300. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  301. Naive CD4 T 1 1 1 1 1 1 1 1 1
  302. Memory CD4 T 1 1 1 1 1 1 1 1 1
  303. CD14 Mono 1 1 1 1 1 1 1 1 1
  304. B 1 1 1 1 1 1 1 1 1
  305. CD8 T 1 1 1 1 1 1 1 1 1
  306. FCGR3A Mono 1 1 1 1 1 1 1 1 1
  307. NK 1 1 1 1 1 1 1 1 1
  308. DC 1 1 1 1 1 1 1 1 1
  309. Platelet 1 1 1 1 1 1 1 1 1
  310.  
  311. , , WNT10B_FZD1_LRP5
  312.  
  313. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  314. Naive CD4 T 1 1 1 1 1 1 1 1 1
  315. Memory CD4 T 1 1 1 1 1 1 1 1 1
  316. CD14 Mono 1 1 1 1 1 1 1 1 1
  317. B 1 1 1 1 1 1 1 1 1
  318. CD8 T 1 1 1 1 1 1 1 1 1
  319. FCGR3A Mono 1 1 1 1 1 1 1 1 1
  320. NK 1 1 1 1 1 1 1 1 1
  321. DC 1 1 1 1 1 1 1 1 1
  322. Platelet 1 1 1 1 1 1 1 1 1
  323.  
  324. , , WNT10B_FZD2_LRP5
  325.  
  326. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  327. Naive CD4 T 1 1 1 1 1 1 1 1 1
  328. Memory CD4 T 1 1 1 1 1 1 1 1 1
  329. CD14 Mono 1 1 1 1 1 1 1 1 1
  330. B 1 1 1 1 1 1 1 1 1
  331. CD8 T 1 1 1 1 1 1 1 1 1
  332. FCGR3A Mono 1 1 1 1 1 1 1 1 1
  333. NK 1 1 1 1 1 1 1 1 1
  334. DC 1 1 1 1 1 1 1 1 1
  335. Platelet 1 1 1 1 1 1 1 1 1
  336.  
  337. , , WNT10B_FZD3_LRP5
  338.  
  339. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  340. Naive CD4 T 1 1 1 1 1 1 1 1 1
  341. Memory CD4 T 1 1 1 1 1 1 1 1 1
  342. CD14 Mono 1 1 1 1 1 1 1 1 1
  343. B 1 1 1 1 1 1 1 1 1
  344. CD8 T 1 1 1 1 1 1 1 1 1
  345. FCGR3A Mono 1 1 1 1 1 1 1 1 1
  346. NK 1 1 1 1 1 1 1 1 1
  347. DC 1 1 1 1 1 1 1 1 1
  348. Platelet 1 1 1 1 1 1 1 1 1
  349.  
  350. , , WNT10B_FZD6_LRP5
  351.  
  352. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  353. Naive CD4 T 1 1 1 1 1 1 1 1 1
  354. Memory CD4 T 1 1 1 1 1 1 1 1 1
  355. CD14 Mono 1 1 1 1 1 1 1 1 1
  356. B 1 1 1 1 1 1 1 1 1
  357. CD8 T 1 1 1 1 1 1 1 1 1
  358. FCGR3A Mono 1 1 1 1 1 1 1 1 1
  359. NK 1 1 1 1 1 1 1 1 1
  360. DC 1 1 1 1 1 1 1 1 1
  361. Platelet 1 1 1 1 1 1 1 1 1
  362.  
  363. , , WNT16_FZD1_LRP5
  364.  
  365. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  366. Naive CD4 T 1 1 1 1 1 1 1 1 1
  367. Memory CD4 T 1 1 1 1 1 1 1 1 1
  368. CD14 Mono 1 1 1 1 1 1 1 1 1
  369.  
  370. [ reached getOption("max.print") -- omitted 6 row(s) and 114 matrix slice(s) ]
  371.  
  372. $count
  373. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  374. Naive CD4 T 4 9 15 5 11 21 12 14 6
  375. Memory CD4 T 13 21 22 9 22 31 21 21 13
  376. CD14 Mono 12 20 25 12 23 28 26 28 14
  377. B 3 6 11 4 6 17 9 11 6
  378. CD8 T 7 13 22 7 15 27 20 19 12
  379. FCGR3A Mono 12 25 28 12 22 33 26 30 15
  380. NK 10 19 24 9 20 26 21 23 12
  381. DC 13 24 25 13 21 32 22 26 18
  382. Platelet 2 6 10 2 10 11 10 11 9
  383.  
  384. $sum
  385. Naive CD4 T Memory CD4 T CD14 Mono B CD8 T FCGR3A Mono NK DC Platelet
  386. Naive CD4 T 5.235731e-04 6.742952e-04 3.909235e-04 7.501420e-04 5.434838e-04 3.885489e-04 1.610436e-04 4.846562e-05 4.413932e-06
  387. Memory CD4 T 1.007867e-03 1.385925e-03 6.727733e-04 1.319087e-03 1.129907e-03 6.201049e-04 4.244407e-04 1.029006e-04 2.323768e-05
  388. CD14 Mono 2.212146e-04 3.583798e-04 1.213175e-03 5.313253e-04 5.061446e-04 5.027468e-04 2.294104e-04 8.682125e-05 2.022770e-05
  389. B 1.301160e-05 9.973032e-05 1.565374e-04 3.703069e-04 1.646528e-04 2.057724e-04 4.275688e-05 2.459992e-05 3.097154e-06
  390. CD8 T 7.640382e-04 9.283023e-04 4.849123e-04 6.086610e-04 1.986549e-03 1.788599e-04 8.787072e-04 5.912427e-05 9.023021e-05
  391. FCGR3A Mono 1.374292e-04 2.766033e-04 4.453398e-04 1.984605e-04 1.309001e-04 2.772841e-04 6.165247e-05 3.351834e-05 9.078602e-07
  392. NK 4.436511e-04 4.983154e-04 3.013077e-04 3.858570e-04 1.078647e-03 9.820542e-05 4.720637e-04 3.638077e-05 4.795777e-05
  393. DC 3.642583e-05 8.053200e-05 1.016134e-04 9.111682e-05 6.074735e-05 6.164358e-05 2.886705e-05 1.000832e-05 1.323708e-06
  394. Platelet 2.580361e-05 3.406017e-05 1.414725e-05 1.492857e-05 9.745813e-05 3.867913e-06 4.425967e-05 2.105407e-06 4.930773e-06
  395.  
  396.  
  397. head(cellchat@netP$similarity)
  398. head(cellchat@net$count)
  399. head(cellchat@net$prob)
  400. head(cellchat@net$sum)
  401. head(cellchat@DB)
  402. head(cellchat@var.features)
  403.  

github 仓库在:

https://github.com/sqjin/CellChat

https://www.youtube.com/watch?v=kc45au1RhNs

https://www.youtube.com/watch?v=lag9UstpYhk

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