④-1 Single Cell Learning-cellchat Single Data Code Supplement

catalogs

1, Data input and processing

①Load packets and data

②CellChat Input Data Preparation

③ Construct CellChat object

④Data preprocessing

2, Cellular communication prediction

① Calculate the cellular communication probability

②Table of extracted ligand receptor-to-cell communication results

③ Extraction of cellular communication tables at the level of signaling pathways

④ Visualization of cellular interactions

1) Network diagram of the number of ligand receptors between cell subpopulations

2) Ligand-receptor probability/intensity network diagram between cellular subpopulations

3) The probability of ligand-receptor communication for each cell subpopulation is shown separately

(Edit 4) Quantity and intensity chord plots merged

3, Analysis of cellular communication at the level of signaling pathways

4, Analysis of cellular communication at the level of signaling pathway-associated ligand-receptor pairs

5, Visualization of cellular communication mediated at the level of multiple ligand-receptor pairs/signaling pathways

① Designated signaling pathways

② Demonstration of the distribution of expression of genes involved in the target signaling pathway in various cell subpopulations

③Bubble chart

1, Data input and processing

①Load packets and data

Official Learning:focuslyj/CellChat - Code Cloud - Open Source China ()

The data provided here should be counts data

rm(list=ls())




library(CellChat)




library(patchwork)




library(ggplot2)




library(Seurat)




library(ggalluvial)# Plotting Sankey diagrams




library(expm)




library(sna)




library(NMF)




options(stringsAsFactors = FALSE)## input data is not automatically converted to factors (to prevent data formatting errors)




load("data_humanSkin.Rdata") #data loaded: here is the count data data

②CellChat Input Data Preparation

#Normalized gene expression matrix and cell grouping information files required



# Different input formats are handled differently




data.input = data_humanSkin$data#Normalized gene expression matrix and cell grouping information files required



meta = data_humanSkin$meta




data.input[1:6,1:3] #Expressioncount




head(meta);table(meta$condition) #with normal(NL) and disequations(LS)



cell.use = rownames(meta)[meta$condition == 'LS'] #Name of the cell from which the LS was extracted




data.input = data.input[, cell.use]# Extract the LS expression matrix



meta = meta[cell.use, ]#Extract LS cell information



identical(rownames(meta),colnames(data.input)) #Check that matrix column names and grouped file line names are identical



unique(meta$labels) #check cell subpopulation label type

③Building CellChat Objects

# Next build the CellChat object



cellchat <- createCellChat(object = data.input, # Support for normalized expression matrices, Seurat objects, and SingleCellExperiment objects



                           meta = meta, #meta file



                           group.by = 'labels') #cell classification columns in meta



#cellchat <- addMeta(cellchat, meta = meta)#Create CellChat object without cellmeta information when adding the information



cellchat <- setIdent(cellchat, ident.use = 'labels') #Set the labels to the default order in which they are displayed



levels(cellchat@idents) #look at celltype and factor order




table(cellchat@idents) # of cells in each celltype



 



# Setting up the ligand receptor database (CellChatDB).



CellChatDB <-  #() ()



showDatabaseCategory(CellChatDB) #View a pie chart describing the composition of this database



dplyr::glimpse(CellChatDB$interaction) #view database structure



 



#Directly use the full CellChatDB library for cell communication analysis:



##CellChatDB.use <- CellChatDB # simply use the default CellChatDB



#Select a specific subset of the database for cellular communication analysis.



CellChatDB.use <- subsetDB(CellChatDB, 



                           search = 'Secreted Signaling') #selectableSecreted Signaling、ECM-ReceptormaybeCell-Cell Contact



cellchat@DB <- CellChatDB.use# Add database to CellChat object (DB)

④Data preprocessing

#Data preprocessing; expression matrix subsetting of signaling genes saves computational costs



cellchat <- subsetData(cellchat) # mandatory step, take a subset of the expression matrix of the signaling genes in the previous step, assign to cellchat@data.Signaling




#future::plan('multiprocess', workers = 4) # do parallel (parallel computation can be unchecked)



#Identification of overexpressed signaling genes associated with each cell subpopulation: determined based on the proportion of cells expressing the gene, differential ploidy and p-value.



cellchat <- identifyOverExpressedGenes(cellchat,



                                        = TRUE, # Only positive markers are returned




                                        = 0, #Cell Proportion Threshold




                                        = 0, # Multiples of variance




                                        = 0.05) #P-Value




#Calculated results assigned to cellchat@var.features：



head(cellchat@var.features$features) # Overexpressed signaling gene name




head(cellchat@var.features$features.info) # Table of results of variance calculations




# Recognize overexpressed gene ligand-receptor interactions:



cellchat <- identifyOverExpressedInteractions(cellchat)



head(cellchat@LR$LRsig) # Calculated result assignment position




# Map gene expression data to PPI networks (skippable):



cellchat <- projectData(cellchat, PPI.human) #Return results:cellchat@data.project

2, Cellular communication prediction

① Calculate the cellular communication probability

#CellComm predictions ##############################################



cellchat <- computeCommunProb(cellchat, raw.use = TRUE) #Return result:cellchat@options$parameter



## Use raw expression data by default (cellchat@data.Signaling), if you want to use the PPI correction data from the previous step, set raw.use = TALSE



cellchat <- filterCommunication(cellchat,  = 10) #Cell Communication Filtering (set the minimum number of cells in each subpopulation required for intercellular communication)

②Extracted Ligand Receptor to Cell Communication Results Table

# Extracted ligand receptor to cellular communication results table:



 <- subsetCommunication(cellchat,  = 'net')



head() #Get a table of ligand-receptor-to-cell communication results



 



# or visit other interested/Specific cellular communication results:



1 <- subsetCommunication(cellchat,



                               sources.use = c('LC'),



                               targets.use = c('FBN1+ FIB')) # Access to a subset of specific cell pairs



head(1)



 



2 <- subsetCommunication(cellchat, signaling = c('CD40')) # Access to specific subsets of signaling pathways



head(2)

③Extracting cellular communication tables at the level of signaling pathways

Extraction of ligand receptor-to-cell communication results Table:subsetCommunication function

Extraction of cellular communication tables at the level of signaling pathways:computeCommunProbPathway function

# Extract cellular communication tables at the level of signaling pathways:



cellchat <- computeCommunProbPathway(cellchat) #Calculate the probability of communication at the level of the signaling pathway



df.netp <- subsetCommunication(cellchat, = 'netP') #Get signaling pathway level cellchat table




head()

④Visualization of cellular interactions

1) Network diagram of the number of ligand receptors between cell subpopulations

cellchat <- aggregateNet(cellchat)# Calculate the number and probability strength of communication between cell pairs



# Number and probability of interactions between different cell subpopulations/Intensity visualization:



groupSize <- as.numeric(table(cellchat@idents))## Network map of ligand receptor numbers between cell subpopulations:



par(mfrow = c(1,1), xpd = TRUE)



netVisual_circle(cellchat@net$count,



                  = groupSize,



                  = T,



                  = F,



                  = 'Number of interactions')

Cell Communication| Basic Analysis Tutorial_Beep_bilibili

2) Ligand-receptor probability/intensity network diagram between cellular subpopulations

## Probability/intensity network plot of ligand receptors between cell subpopulations:




par(mfrow = c(1,1), xpd = TRUE)




netVisual_circle(cellchat@net$weight,



                  = groupSize,



                  = T,



                 = F,



                  = 'Interaction weights/strength')

3) The probability of ligand-receptor communication for each cell subpopulation is shown separately

Here you need to pay attention to the scope of the R drawing board, you can save the previous drawing and devoff before making a drawing

## Examine the strength of interactions signals in individual cell subpopulations; each cell is shown individually ###



mat <- cellchat@net$weight



par(mfrow = c(3,4), xpd = TRUE)




for (i in 1:nrow(mat)) {



  mat2 <- matrix(0, nrow = nrow(mat), ncol = ncol(mat), dimnames = dimnames(mat))



  mat2[i, ] <- mat[i, ]



  netVisual_circle(mat2,  = groupSize,  = T,  = max(mat),  = rownames(mat)[i])



}

4) Quantity and intensity chord plots merged

# Quantity and intensity chord plots merged




par(mfrow = c(1,2), xpd = TRUE)




netVisual_circle(cellchat@net$count,



                  = groupSize,



                  = T,



                  = F,



                  = 'Number of interactions')




netVisual_circle(cellchat@net$weight,



                  = groupSize,



                  = T,



                 = F,



                  = 'Interaction weights/strength')

# Save the cellchat object:



save(cellchat, groupSize, file = c('humanSkin_CellChat.Rdata'))

3, Analysis of cellular communication at the level of signaling pathways

cellchat@netP$pathways##Signaling pathways to view



 <- c('GALECTIN')## to'GALECTIN'Signaling pathway demonstration as an example



# Hierarchy plotting



levels(cellchat@idents)# View cell subpopulations and factor order:



#Select the subpopulation of cells of interest therein:



 = c(3,8,9,10)# for the first column of the drawingsourcecolumns



par(mfrow = c(1,1))



netVisual_aggregate(cellchat,



                    layout = c('hierarchy'), #"circle", "hierarchy", "chord"




                    signaling = ,



                     = ) # Select the pathway to display

Other charts

par(mfrow = c(1,1))# Show network diagrams



netVisual_aggregate(cellchat,



                    layout = c('circle'),



                    signaling = )



par(mfrow=c(1,1))#Display string diagrams



netVisual_aggregate(cellchat,



                    layout = c('chord'),



                    signaling = )



par(mfrow=c(1,1))#Display Heatmap



netVisual_heatmap(cellchat,



                  signaling = ,



                   = c("white", "#b2182b"))

4，Analysis of cellular communication at the level of signaling pathway-associated ligand-receptor pairs

netAnalysis_contribution(cellchat, 



                         signaling = ) # Bar graph of ligand receptor pair contributions



 <- extractEnrichedLR(cellchat, # extract cell pair



                                 signaling = ,



                                 geneLR.return = FALSE)



 <- [1,] # with the contributiontop1As an example, the ligand-receptor pairs of



; 

Other charts

netVisual_individual(cellchat,#Hierarchy plot：



                     layout = c('hierarchy'),



                     signaling = , #Target signaling pathways



                     pairLR.use = , # target ligand receptor pairs



                      = ) #Subpopulation of cells of interest



#Circle plot：



netVisual_individual(cellchat,



                     layout = c('circle'),



                     signaling = ,



                     pairLR.use = )



#Chord diagram：



netVisual_individual(cellchat,



                     layout = c('chord'),



                     signaling = ,



                     pairLR.use = )

5，Visualization of cellular communication mediated at the level of multiple ligand-receptor pairs/signaling pathways

①Specify signaling pathway

Multiple ligand-receptor pairs/Visualization of cellular communication mediated at the level of signaling pathways ######



levels(cellchat@idents)#specify signaling pathways



netVisual_bubble(cellchat,



                 sources.use = 4,



                 targets.use = c(5:11),



                 signaling = c("CCL","CXCL"), # Specify both CCL and CXCL signaling pathways



                  = FALSE)



# Specify matching receptor pairs:



pairLR.use <- extractEnrichedLR(cellchat, 



                                signaling = c("CCL","CXCL","FGF")) #Identify ligand-receptor pairs that are important in target signaling pathways



pairLR.use




netVisual_bubble(cellchat,



                 sources.use = 4,



                 targets.use = c(5:11),



                 pairLR.use = pairLR.use,# Specified receptor pairs



                  = TRUE)

② Demonstration of the distribution of expression of genes involved in the target signaling pathway in various cell subpopulations

#Demonstration of the expression distribution of genes involved in target signaling pathways across cellular subpopulations:



plotGeneExpression(cellchat, signaling = 'GALECTIN', 



                   type = 'violin') # Violin charts

③Bubble chart

plotGeneExpression(cellchat, 



                   signaling = 'GALECTIN', type = 'dot', color.use = c("white", "#b2182b")) #bubble chart