前言
我們知道，細胞間信息傳遞方式一個是細胞表面配受體的相互作用，另一個通過細胞產生的可溶性小分子，即細胞因子。在單細胞數據分析中下游，有時候我們想看某幾種細胞類型之間的相互作用，就有人推薦我們做一個配受體分析。那什么是配受體？我們在文章Cell-Cell Interaction Database|| 單細胞配受體庫你還在文章的附錄里找嗎？中提到配受體其實是細胞的特定蛋白，蛋白追溯到基因表達上就是基因對。
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，我們就用CellChat來分析一下我們的PBMC數據，看看配受體分析的一般流程。
除了從任何給定的scRNA-seq數據推斷細胞間通信外，CellChat還提供了進一步的數據探索、分析和可視化功能。
- 它能夠分析細胞與細胞間的通訊，以獲得細胞發展軌跡上的連續狀態。
- 該方法結合社會網絡分析、模式識別和多種學習方法，可以定量地描述和比較推斷出的細胞間通信網絡。
- 它提供了一個易于使用的工具來提取和可視化推斷網絡信息。例如，它可以隨時預測所有細胞群的主要信號輸入和輸出，以及這些細胞群和信號如何協調在一起實現功能。
- 它提供了幾個可視化輸出，以方便用戶引導的直觀數據解釋。
devtools::install_github("sqjin/CellChat")
CellChat需要兩個輸入:
- 一個是細胞的基因表達數據，
- 另一個是細胞標簽(即細胞標簽)。
對于基因表達數據矩陣，基因應該在帶有行名的行中，cell應該在帶有名稱的列中。CellChat分析的輸入是均一化的數據(Seurat@assay$RNA@data)。如果用戶提供counts數據，可以用normalizeData函數來均一化。對于細胞的信息，需要一個帶有rownames的數據格式作為CellChat的輸入。
這兩個文件在我們熟悉的Seurat對象中是很容易找到的，一個是均一化之后的數據，一個是細胞類型在metadata中。那么就讓我們開始chat之旅吧。
數據配置
首先，我們加載包和引入實例數據。
library(CellChat)
library(ggplot2)
library(ggalluvial)
library(svglite)
library(Seurat)
library(SeuratData)
options(stringsAsFactors = FALSE)
我們用Seurat給出的pbmc3k.final數據集，大部分的計算已經存在其對象中了：
pbmc3k.final
An object of class Seurat
13714 features across 2638 samples within 1 assay
Active assay: RNA (13714 features, 2000 variable features)
2 dimensional reductions calculated: pca, umap
pbmc3k.final@commands$FindClusters # 你也看一看作者的其他命令，Seurat是記錄其分析過程的。
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
按照我們剛才說的，我們在Seurat對象中提出CellChat需要的數據：
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
創建一個Cell Chat對象。
cellchat <- createCellChat(data = data.input)
cellchat
An object of class CellChat
13714 genes.
2638 cells.
summary(cellchat)
Length Class Mode
1 CellChat S4
S4 類學會了嗎？
在學習單細胞數據分析工具的時候，在知道了要干嘛之后，第二步就是看數據格式，俗稱：單細胞數據格式。我們在聽說你的單細胞對象需要一個思維導圖？，曾給出一個簡單的可視化數據結構的方法：導圖。
library(mindr)
(out <- capture.output(str(cellchat)))
out2 <- paste(out, collapse="\n")
mm(gsub("\\.\\.@","# ",gsub("\\.\\. ","#",out2)),type ="text")
當然，我們可以用str來看，就是有點冗長：
> 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()
我們把metadata信息加到CellChat對象中，這個寫法跟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
導入配受體數據庫
CellChat提供了人和小鼠的配受體數據庫，分別可以用CellChatDB.human,CellChatDB.mouse來導入。來看一下這個數據庫的結構吧。
CellChatDB <- CellChatDB.human
(out3 <- capture.output(str(CellChatDB)))
out4 <- paste(out3, collapse="\n")
mm(gsub("\\$","# ",gsub("\\.\\. ","#",out4)),type ="text")
這個數據庫的信息是很全面的：
> 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$geneInfo)
Symbol Name EntrezGene.ID Ensembl.Gene.ID MGI.ID Gene.group.name
HGNC:5 A1BG alpha-1-B glycoprotein 1 ENSG00000121410 MGI:2152878 Immunoglobulin like domain containing
HGNC:37133 A1BG-AS1 A1BG antisense RNA 1 503538 ENSG00000268895 Antisense RNAs
HGNC:24086 A1CF APOBEC1 complementation factor 29974 ENSG00000148584 MGI:1917115 RNA binding motif containing
HGNC:7 A2M alpha-2-macroglobulin 2 ENSG00000175899 MGI:2449119 C3 and PZP like, alpha-2-macroglobulin domain containing
HGNC:27057 A2M-AS1 A2M antisense RNA 1 144571 ENSG00000245105 Antisense RNAs
HGNC:23336 A2ML1 alpha-2-macroglobulin like 1 144568 ENSG00000166535 C3 and PZP like, alpha-2-macroglobulin domain containing
其實是記錄了許多許多受配體相關的通路信息，不像有的配受體庫只有一個基因對。這樣，我們就可以更加扎實地把腳落到pathway上面了。在CellChat中，我們還可以先擇特定的信息描述細胞間的相互作者，這個可以理解為從特定的側面來刻畫細胞間相互作用,比用一個大的配體庫又精細了許多呢。
CellChatDB.use <- subsetDB(CellChatDB, search = "Secreted Signaling") # use Secreted Signaling for cell-cell communication analysis
cellchat@DB <- CellChatDB.use # set the used database in the object
有哪些可以選擇的側面呢？
> unique(CellChatDB$interaction$annotation)
[1] "Secreted Signaling" "ECM-Receptor" "Cell-Cell Contact"
預處理
對表達數據進行預處理，用于細胞間的通信分析。首先在一個細胞組中識別過表達的配體或受體，然后將基因表達數據投射到蛋白-蛋白相互作用(PPI)網絡上。如果配體或受體過表達，則識別過表達配體和受體之間的相互作用。
cellchat <- subsetData(cellchat) # subset the expression data of signaling genes for saving computation cost
future::plan("multiprocess", workers = 4) # do parallel 這里似乎有一些bug，在Linux上居然不行。de了它。
cellchat <- identifyOverExpressedGenes(cellchat)
cellchat <- identifyOverExpressedInteractions(cellchat)
cellchat <- projectData(cellchat, PPI.human)
相互作用推斷
然后，我們通過為每個相互作用分配一個概率值并進行置換檢驗來推斷生物意義上的細胞-細胞通信。
# cellchat <- computeCommunProb(cellchat) 注意這個函數如果你可以用就用，這個是作者的。
mycomputeCommunProb <-edit(computeCommunProb) # computeCommunProb內部似乎有一些bug，同一套數據在window10上沒事，到了Linux上有報錯。發現是computeExpr_antagonist這個函數有問題，(matrix(1, nrow = 1, ncol = length((group))))，中應為(matrix(1, nrow = 1, ncol = length(unique(group))))？ 不然矩陣返回的不對。de了它。
environment(mycomputeCommunProb) <- environment(computeCommunProb)
cellchat <- mycomputeCommunProb(cellchat) # 這兒是我de過的。
關于這個bug。我在GitHub上向作者提出了，并在20200727得到答復：已經修訂。大家遇到問題也可以直接在GitHub上提問和回復。下面是例子（與本文無關）：
進入GitHub倉庫：https://github.com/sqjin/CellChat，點擊Issues
就可以經行提交問題了，對話框是支持markerdown語法的。如我們的例子。
這個對話有兩點值得我們學習：
- 提問者說的很清楚，代碼具體到哪一行，而且給出了示例。
- 回答者很快檢查代碼，并做了回應。
好了，我們可以接著往下走了。
推測細胞間在信號通路水平上的通訊。我們還通過計算與每個信號通路相關的所有配體-受體相互作用的通信概率來推斷信號通路水平上的通信概率。
注:推測的每個配體-受體對的細胞間通信網絡和每個信號通路分別存儲在“net”和“netP”槽中。
我們可以通過計算鏈路的數量或匯總通信概率來計算細胞間的聚合通信網絡。
cellchat <- computeCommunProbPathway(cellchat)
cellchat <- aggregateNet(cellchat)
讓我們看看這結果。
> cellchat@netP$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$LRsig)
interaction_name pathway_name ligand receptor agonist antagonist co_A_receptor co_I_receptor
TGFB1_TGFBR1_TGFBR2 TGFB1_TGFBR1_TGFBR2 TGFb TGFB1 TGFbR1_R2 TGFb agonist TGFb antagonist TGFb inhibition receptor
TGFB1_ACVR1B_TGFBR2 TGFB1_ACVR1B_TGFBR2 TGFb TGFB1 ACVR1B_TGFbR2 TGFb agonist TGFb antagonist TGFb inhibition receptor
TGFB1_ACVR1C_TGFBR2 TGFB1_ACVR1C_TGFBR2 TGFb TGFB1 ACVR1C_TGFbR2 TGFb agonist TGFb antagonist TGFb inhibition receptor
TGFB1_ACVR1_TGFBR1 TGFB1_ACVR1_TGFBR1 TGFb TGFB1 ACVR1_TGFbR
WNT10A_FZD1_LRP5 WNT10A_FZD1_LRP5 WNT WNT10A FZD1_LRP5 WNT agonist WNT antagonist WNT activation receptor WNT inhibition receptor
WNT10A_FZD2_LRP5 WNT10A_FZD2_LRP5 WNT WNT10A FZD2_LRP5 WNT agonist WNT antagonist WNT activation receptor WNT inhibition receptor
evidence annotation interaction_name_2
TGFB1_TGFBR1_TGFBR2 KEGG: hsa04350 Secreted Signaling TGFB1 - (TGFBR1+TGFBR2)
TGFB1_ACVR1B_TGFBR2 PMID: 27449815 Secreted Signaling TGFB1 - (ACVR1B+TGFBR2)
TGFB1_ACVR1C_TGFBR2 PMID: 27449815 Secreted Signaling TGFB1 - (ACVR1C+TGFBR2)
TGFB1_ACVR1_TGFBR1 PMID: 29376829 Secreted Signaling TGFB1 - (ACVR1+TGFBR1)
WNT10A_FZD1_LRP5 KEGG: hsa04310; PMID: 23209157 Secreted Signaling WNT10A - (FZD1+LRP5)
WNT10A_FZD2_LRP5 KEGG: hsa04310; PMID: 23209159 Secreted Signaling WNT10A - (FZD2+LRP5)
可視化
在推斷細胞-細胞通信網絡的基礎上，CellChat為進一步的探索、分析和可視化提供了各種功能。
通過結合社會網絡分析、模式識別和多種學習方法的綜合方法，t可以定量地描述和比較推斷出的細胞-細胞通信網絡。
它提供了一個易于使用的工具來提取和可視化推斷網絡的高階信息。例如，它可以隨時預測所有細胞群的主要信號輸入和輸出，以及這些細胞群和信號如何協調在一起實現功能。
你可以使用層次圖或圈圖可視化每個信號通路。 如果使用層次圖可視化通信網絡，請定義vertex.receiver，它是一個數字向量，給出作為第一個層次結構圖中的目標的細胞組的索引。我們可以使用netVisual_aggregate來可視化信號路徑的推斷通信網絡，并使用netVisual_individual來可視化與該信號路徑相關的單個L-R對的通信網絡。
在層次圖中，實體圓和空心圓分別表示源和目標。圓的大小與每個細胞組的細胞數成比例。邊緣顏色與信源一致。線越粗，信號越強。這里我們展示了一個MIF信號網絡的例子。所有顯示重要通信的信令路徑都可以通過cellchat@netP$pathways訪問。
>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$antagonist
pathways.show <- "MIF"
# netVisual_aggregate(cellchat, signaling = pathways.show, vertex.receiver = vertex.receiver, vertex.size = groupSize) # 原函數
mynetVisual_aggregate(cellchat, signaling = pathways.show, vertex.receiver = vertex.receiver, vertex.size = groupSize) 原函數這里似乎有一個和igraph相關的小問題在不同igraph可能會表現bug，不巧我遇到了，de了它。
經典的配受體圈圖：
mynetVisual_aggregate(cellchat, signaling = c("MIF"), layout = "circle", vertex.size = groupSize,pt.title=20,vertex.label.cex = 1.7)
計算和可視化每個配體-受體對整個信號通路的貢獻度。
netAnalysis_contribution(cellchat, signaling = pathways.show)
識別細胞群的信號轉導作用，通過計算每個細胞群的網絡中心性指標，CellChat允許隨時識別細胞間通信網絡中的主要發送者、接收者、調解者和影響者。
cellchat <- netAnalysis_signalingRole(cellchat, slot.name = "netP") # the slot 'netP' means the inferred intercellular communication network of signaling pathways
···
netVisual_signalingRole(cellchat, signaling = pathways.show, width = 12, height = 2.5, font.size = 10)
···
識別特定細胞群的全局通信模式和主要信號。除了探索單個通路的詳細通訊外，一個重要的問題是多個細胞群和信號通路如何協調運作。CellChat采用模式識別方法來識別全局通信模式以及每個小群的關鍵信號。
識別分泌細胞外向交流模式。隨著模式數量的增加，可能會出現冗余的模式，使得解釋通信模式變得困難。我們選擇了5種模式作為默認模式。一般來說，當模式的數量大于2時就可以認為具有生物學意義。
nPatterns = 5
# 同樣在這里遇到了bug，難道說是我沒有安裝好嗎，de了它。
# cellchat <- myidentifyCommunicationPatterns(cellchat, pattern = "outgoing", k = nPatterns)
myidentifyCommunicationPatterns <- edit(identifyCommunicationPatterns)
environment(myidentifyCommunicationPatterns) <- environment(identifyCommunicationPatterns)
cellchat <- myidentifyCommunicationPatterns(cellchat, pattern = "outgoing", k = nPatterns)
# Visualize the communication pattern using river plot
netAnalysis_river(cellchat, pattern = "outgoing")
# Visualize the communication pattern using dot plot
netAnalysis_dot(cellchat, pattern = "outgoing")
識別目標細胞的傳入(incoming)通信模式。
netAnalysis_river(cellchat, pattern = "incoming")
netAnalysis_dot(cellchat, pattern = "incoming")
作為結尾有大量的空間，我們得以先看看cellchat配受體推斷的結構是如何的。
> head(cellchat@LR$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
每個pattern有outgoing和ingoing兩種。
> 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 倉庫在：
https://github.com/sqjin/CellChat
https://www.youtube.com/watch?v=kc45au1RhNs
https://www.youtube.com/watch?v=lag9UstpYhk
老師在youtube的講解
