Inference and analysis of cell-cell communication using CellChat

1. load libraries

2. Load Seurat Object

#Load Seurat Object Integrated Object
load("ARSIM0only_corrected.Robj")

ARSIM0only

An object of class Seurat 
39604 features across 32273 samples within 3 assays 
Active assay: integrated (2996 features, 2996 variable features)
 2 layers present: data, scale.data
 2 other assays present: RNA, ADT
 3 dimensional reductions calculated: pca, umap, tsne

# Set the default assay to 'RNA' or 'SCT' if available
if ("RNA" %in% names(ARSIM0only@assays)) {
    DefaultAssay(ARSIM0only) <- "RNA"
} else if ("SCT" %in% names(ARSIM0only@assays)) {
    DefaultAssay(ARSIM0only) <- "SCT"
} else {
    stop("Neither RNA nor SCT assay found in the Seurat object. Please ensure your data has been normalized and contains one of these assays.")
}

3. Data input & processing and initialization of CellChat object

#------------------------------------------------------------------------------
# **Part I: Data input & processing and initialization of CellChat object**
#------------------------------------------------------------------------------

# Loading and Preparing Data
ARSIM0only <- AddMetaData(object = ARSIM0only, 
                          metadata = paste0("Cluster_", ARSIM0only$seurat_clusters), 
                          col.name = 'Clusters')

ARSIM0only[["RNA"]] <- JoinLayers(ARSIM0only[["RNA"]])


# Extract the normalized data
data_input <- GetAssayData(ARSIM0only, assay = "RNA", slot = "data")

#1.  Create CellChat object
cellchat <- createCellChat(object = data_input, meta = ARSIM0only@meta.data, group.by = "Clusters")

[1] "Create a CellChat object from a data matrix"

Warning: The 'meta' data does not have a column named `samples`. We now add this column and all cells are assumed to belong to `sample1`! 

Set cell identities for the new CellChat object 
The cell groups used for CellChat analysis are  Cluster_0, Cluster_1, Cluster_10, Cluster_11, Cluster_12, Cluster_13, Cluster_14, Cluster_15, Cluster_16, Cluster_17, Cluster_18, Cluster_19, Cluster_2, Cluster_20, Cluster_21, Cluster_22, Cluster_23, Cluster_3, Cluster_4, Cluster_5, Cluster_6, Cluster_7, Cluster_8, Cluster_9 

#2.  Set the ligand-receptor interaction database

cellchatDB <- CellChatDB.human # or CellChatDB.mouse
showDatabaseCategory(cellchatDB)

# Show the structure of the database
dplyr::glimpse(cellchatDB$interaction)

Rows: 3,233
Columns: 28
$ interaction_name         <chr> "TGFB1_TGFBR1_TGFBR2", "TGFB2_TGFBR1_TGFBR2", "TGFB3_TGFBR1_TGFBR2", "TGFB1_ACVR1B_TGFBR2", "TGFB…
$ pathway_name             <chr> "TGFb", "TGFb", "TGFb", "TGFb", "TGFb", "TGFb", "TGFb", "TGFb", "TGFb", "TGFb", "TGFb", "TGFb", "…
$ ligand                   <chr> "TGFB1", "TGFB2", "TGFB3", "TGFB1", "TGFB1", "TGFB2", "TGFB2", "TGFB3", "TGFB3", "TGFB1", "TGFB2"…
$ receptor                 <chr> "TGFbR1_R2", "TGFbR1_R2", "TGFbR1_R2", "ACVR1B_TGFbR2", "ACVR1C_TGFbR2", "ACVR1B_TGFbR2", "ACVR1C…
$ agonist                  <chr> "TGFb agonist", "TGFb agonist", "TGFb agonist", "TGFb agonist", "TGFb agonist", "TGFb agonist", "…
$ antagonist               <chr> "TGFb antagonist", "TGFb antagonist", "TGFb antagonist", "TGFb antagonist", "TGFb antagonist", "T…
$ co_A_receptor            <chr> "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "…
$ co_I_receptor            <chr> "TGFb inhibition receptor", "TGFb inhibition receptor", "TGFb inhibition receptor", "TGFb inhibit…
$ evidence                 <chr> "KEGG: hsa04350", "KEGG: hsa04350", "KEGG: hsa04350", "PMID: 27449815", "PMID: 27449815", "PMID: …
$ annotation               <chr> "Secreted Signaling", "Secreted Signaling", "Secreted Signaling", "Secreted Signaling", "Secreted…
$ interaction_name_2       <chr> "TGFB1 - (TGFBR1+TGFBR2)", "TGFB2 - (TGFBR1+TGFBR2)", "TGFB3 - (TGFBR1+TGFBR2)", "TGFB1 - (ACVR1B…
$ is_neurotransmitter      <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ ligand.symbol            <chr> "TGFB1", "TGFB2", "TGFB3", "TGFB1", "TGFB1", "TGFB2", "TGFB2", "TGFB3", "TGFB3", "TGFB1", "TGFB2"…
$ ligand.family            <chr> "TGF-beta", "TGF-beta", "TGF-beta", "TGF-beta", "TGF-beta", "TGF-beta", "TGF-beta", "TGF-beta", "…
$ ligand.location          <chr> "Extracellular matrix, Secreted, Extracellular space", "Extracellular matrix, Secreted, Extracell…
$ ligand.keyword           <chr> "Disease variant, Signal, Reference proteome, Extracellular matrix, Disulfide bond, Secreted, Mit…
$ ligand.secreted_type     <chr> "growth factor", "growth factor", "cytokine;growth factor", "growth factor", "growth factor", "gr…
$ ligand.transmembrane     <lgl> FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, FAL…
$ receptor.symbol          <chr> "TGFBR2, TGFBR1", "TGFBR2, TGFBR1", "TGFBR2, TGFBR1", "TGFBR2, ACVR1B", "TGFBR2, ACVR1C", "TGFBR2…
$ receptor.family          <chr> "Protein kinase superfamily, TKL Ser/Thr protein kinase", "Protein kinase superfamily, TKL Ser/Th…
$ receptor.location        <chr> "Cell membrane, Secreted, Membrane raft, Cell surface, Cell junction, Tight junction", "Cell memb…
$ receptor.keyword         <chr> "Membrane, Secreted, Disulfide bond, Kinase, Transmembrane, Differentiation, ATP-binding, Recepto…
$ receptor.surfaceome_main <chr> "Receptors", "Receptors", "Receptors", "Receptors", "Receptors", "Receptors", "Receptors", "Recep…
$ receptor.surfaceome_sub  <chr> "Act.TGFB;Kinase", "Act.TGFB;Kinase", "Act.TGFB;Kinase", "Act.TGFB;Kinase", "Act.TGFB;Kinase", "A…
$ receptor.adhesome        <chr> "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "…
$ receptor.secreted_type   <chr> "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "…
$ receptor.transmembrane   <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, T…
$ version                  <chr> "CellChatDB v1", "CellChatDB v1", "CellChatDB v1", "CellChatDB v1", "CellChatDB v1", "CellChatDB …

# use a subset of CellChatDB for cell-cell communication analysis
cellchatDB.use <- subsetDB(cellchatDB, search = "Secreted Signaling", key = "annotation") # use Secreted Signaling

# set the used database in the object
cellchat@DB <- cellchatDB.use

#------------------------------------------------------------------------------
# **Preprocessing the expression data for cell-cell communication analysis**
#------------------------------------------------------------------------------
#3.  Preprocess the expression data
cellchat <- subsetData(cellchat)
cellchat <- identifyOverExpressedGenes(cellchat)
cellchat <- identifyOverExpressedInteractions(cellchat)

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The number of highly variable ligand-receptor pairs used for signaling inference is 1018 

4. Inference of cell-cell communication network

#7.  Visualize the communication network
groupSize <- as.numeric(table(cellchat@idents))

netVisual_circle(cellchat@net$count, vertex.weight = groupSize, weight.scale = T, label.edge= F, title.name = "Number of interactions")

netVisual_circle(cellchat@net$weight, vertex.weight = groupSize, weight.scale = T, label.edge= F, title.name = "Interaction weights/strength")

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, vertex.weight = groupSize, weight.scale = T, edge.weight.max = max(mat), title.name = rownames(mat)[i])
}

5. Visualization of cell-cell communication network

#------------------------------------------------------------------------------
# **Part III: Visualization of cell-cell communication network**
#------------------------------------------------------------------------------
 
#----------------------------------------------------------------------------------------------------
# **Visualize each signaling pathway using Hierarchy plot, Circle plot or Chord diagram**
#----------------------------------------------------------------------------------------------------
  
pathways.show <- c("CD70") 
# Hierarchy plot
# Here we define `vertex.receive` so that the left portion of the hierarchy plot shows signaling to fibroblast and the right portion shows signaling to immune cells 
vertex.receiver = seq(1,4) # a numeric vector. 
netVisual_aggregate(cellchat, signaling = pathways.show,  vertex.receiver = vertex.receiver)

# Circle plot
par(mfrow=c(1,1))

netVisual_aggregate(cellchat, signaling = pathways.show, layout = "circle")

# Chord diagram
par(mfrow=c(1,1))

netVisual_aggregate(cellchat, signaling = pathways.show, layout = "chord")

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

netVisual_heatmap(cellchat, signaling = pathways.show, color.heatmap = "Reds")

Do heatmap based on a single object 

#> Do heatmap based on a single object



# Get the levels of cell types
cell_types <- levels(cellchat@idents)

# Chord diagram
group.cellType <- c(rep("CD80", 4), rep("IL2", 4), rep("TNF", 4)) 

group.cellType <- rep(NA, length(cell_types))

# Assign each cell type to a group
# You'll need to adjust this based on your actual data and biological knowledge
group.cellType[1:8] <- "CD80"
group.cellType[9:16] <- "IL2"
group.cellType[17:24] <- "TNF"

# Name the vector with the cell types
names(group.cellType) <- cell_types

# Check the result
print(group.cellType)

 Cluster_0  Cluster_1 Cluster_10 Cluster_11 Cluster_12 Cluster_13 Cluster_14 Cluster_15 Cluster_16 Cluster_17 Cluster_18 Cluster_19 
    "CD80"     "CD80"     "CD80"     "CD80"     "CD80"     "CD80"     "CD80"     "CD80"      "IL2"      "IL2"      "IL2"      "IL2" 
 Cluster_2 Cluster_20 Cluster_21 Cluster_22 Cluster_23  Cluster_3  Cluster_4  Cluster_5  Cluster_6  Cluster_7  Cluster_8  Cluster_9 
     "IL2"      "IL2"      "IL2"      "IL2"      "TNF"      "TNF"      "TNF"      "TNF"      "TNF"      "TNF"      "TNF"      "TNF" 

netVisual_chord_cell(cellchat, signaling = pathways.show, group = group.cellType, title.name = paste0(pathways.show, " signaling network"))

Plot the aggregated cell-cell communication network at the signaling pathway level

#> Plot the aggregated cell-cell communication network at the signaling pathway level


  
# Compute the contribution of each ligand-receptor pair to the overall signaling pathway and visualize cell-cell 
#communication mediated by a single ligand-receptor pair

netAnalysis_contribution(cellchat, signaling = pathways.show)

pairLR.CXCL <- extractEnrichedLR(cellchat, signaling = pathways.show, geneLR.return = FALSE)
LR.show <- pairLR.CXCL[1,] # show one ligand-receptor pair
# Hierarchy plot
vertex.receiver = seq(1,4) # a numeric vector
netVisual_individual(cellchat, signaling = pathways.show,  pairLR.use = LR.show, vertex.receiver = vertex.receiver)

[[1]]

#> [[1]]
# Circle plot
netVisual_individual(cellchat, signaling = pathways.show, pairLR.use = LR.show, layout = "circle")

[[1]]

#> [[1]]
# Chord diagram
netVisual_individual(cellchat, signaling = pathways.show, pairLR.use = LR.show, layout = "chord")

[[1]]

# Access all the signaling pathways showing significant communications
pathways.show.all <- cellchat@netP$pathways
# check the order of cell identity to set suitable vertex.receiver
levels(cellchat@idents)

 [1] "Cluster_0"  "Cluster_1"  "Cluster_10" "Cluster_11" "Cluster_12" "Cluster_13" "Cluster_14" "Cluster_15" "Cluster_16"
[10] "Cluster_17" "Cluster_18" "Cluster_19" "Cluster_2"  "Cluster_20" "Cluster_21" "Cluster_22" "Cluster_23" "Cluster_3" 
[19] "Cluster_4"  "Cluster_5"  "Cluster_6"  "Cluster_7"  "Cluster_8"  "Cluster_9" 

vertex.receiver = seq(1,4)
for (i in 1:length(pathways.show.all)) {
  # Visualize communication network associated with both signaling pathway and individual L-R pairs
  netVisual(cellchat, signaling = pathways.show.all[i], vertex.receiver = vertex.receiver, layout = "hierarchy")
  # Compute and visualize the contribution of each ligand-receptor pair to the overall signaling pathway
  gg <- netAnalysis_contribution(cellchat, signaling = pathways.show.all[i])
  ggsave(filename=paste0(pathways.show.all[i], "_L-R_contribution.pdf"), plot=gg, width = 3, height = 2, units = 'in', dpi = 300)
}

#----------------------------------------------------------------------------------------------------
# **Visualize cell-cell communication mediated by multiple ligand-receptors or signaling pathways**
#----------------------------------------------------------------------------------------------------
#(A) Bubble plot

# (1) show all the significant interactions (L-R pairs) from some cell groups (defined by 'sources.use') to other cell groups (defined by 'targets.use')
netVisual_bubble(cellchat, sources.use = 4, targets.use = c(5:11), remove.isolate = FALSE)

Comparing communications on a single object 

#> Comparing communications on a single object



# (2) show all the significant interactions (L-R pairs) associated with certain signaling pathways
netVisual_bubble(cellchat, sources.use = 4, targets.use = c(5:11), signaling = c("CCL","CXCL"), remove.isolate = FALSE)

Comparing communications on a single object 

#> Comparing communications on a single object


# (3) show all the significant interactions (L-R pairs) based on user's input (defined by `pairLR.use`)
pairLR.use <- extractEnrichedLR(cellchat, signaling = c("CCL","CXCL","FGF"))

There is no significant communication of CXCL
There is no significant communication of FGF

netVisual_bubble(cellchat, sources.use = c(3,4), targets.use = c(5:8), pairLR.use = pairLR.use, remove.isolate = TRUE)

Comparing communications on a single object 

#> Comparing communications on a single object


# (B) Chord diagram

# show all the significant interactions (L-R pairs) from some cell groups (defined by 'sources.use') to other cell groups (defined by 'targets.use')
# show all the interactions sending from Inflam.FIB
netVisual_chord_gene(cellchat, sources.use = 4, targets.use = c(5:11), lab.cex = 0.5,legend.pos.y = 30)

# show all the interactions received by Inflam.DC
netVisual_chord_gene(cellchat, sources.use = c(1,2,3,4), targets.use = 8, legend.pos.x = 15)

# show all the significant interactions (L-R pairs) associated with certain signaling pathways
netVisual_chord_gene(cellchat, sources.use = c(1,2,3,4), targets.use = c(5:11), signaling = c("CCL","CXCL"),legend.pos.x = 8)

# show all the significant signaling pathways from some cell groups (defined by 'sources.use') to other cell groups (defined by 'targets.use')
netVisual_chord_gene(cellchat, sources.use = c(1,2,3,4), targets.use = c(5:11), slot.name = "netP", legend.pos.x = 10)

#----------------------------------------------------------------------------------------------------
# **Plot the signaling gene expression distribution using violin/dot plot**
#----------------------------------------------------------------------------------------------------

plotGeneExpression(cellchat, signaling = "CXCL", enriched.only = TRUE, type = "violin")

There is no significant communication of CXCL
Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.

plotGeneExpression(cellchat, signaling = "CXCL", enriched.only = FALSE)

Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.Warning: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.Scale for y is already present.
Adding another scale for y, which will replace the existing scale.

6. Systems analysis of cell-cell communication network

#----------------------------------------------------------------------------------------------------
# **Part IV: Systems analysis of cell-cell communication network**
#----------------------------------------------------------------------------------------------------
# (A) Compute and visualize the network centrality scores

ptm = Sys.time()
# Compute the network centrality scores
cellchat <- netAnalysis_computeCentrality(cellchat, slot.name = "netP") # the slot 'netP' means the inferred intercellular communication network of signaling pathways

  |                                                  | 0 % ~calculating  
  |++                                                | 4 % ~03s          
  |++++                                              | 8 % ~03s          
  |++++++                                            | 12% ~03s          
  |++++++++                                          | 16% ~02s          
  |++++++++++                                        | 20% ~02s          
  |++++++++++++                                      | 24% ~02s          
  |++++++++++++++                                    | 28% ~01s          
  |++++++++++++++++                                  | 32% ~01s          
  |++++++++++++++++++                                | 36% ~01s          
  |++++++++++++++++++++                              | 40% ~01s          
  |++++++++++++++++++++++                            | 44% ~01s          
  |++++++++++++++++++++++++                          | 48% ~01s          
  |++++++++++++++++++++++++++                        | 52% ~01s          
  |++++++++++++++++++++++++++++                      | 56% ~01s          
  |++++++++++++++++++++++++++++++                    | 60% ~01s          
  |++++++++++++++++++++++++++++++++                  | 64% ~01s          
  |++++++++++++++++++++++++++++++++++                | 68% ~00s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~00s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~00s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s  

# Visualize the computed centrality scores using heatmap, allowing ready identification of major signaling roles of cell groups
netAnalysis_signalingRole_network(cellchat, signaling = pathways.show, width = 8, height = 2.5, font.size = 10)

#(B) Visualize dominant senders (sources) and receivers (targets) in a 2D space

# Signaling role analysis on the aggregated cell-cell communication network from all signaling pathways
gg1 <- netAnalysis_signalingRole_scatter(cellchat)

Signaling role analysis on the aggregated cell-cell communication network from all signaling pathways

#> Signaling role analysis on the aggregated cell-cell communication network from all signaling pathways
# Signaling role analysis on the cell-cell communication networks of interest
gg2 <- netAnalysis_signalingRole_scatter(cellchat, signaling = c("CXCL", "CCL"))

Signaling role analysis on the cell-cell communication network from user's input

#> Signaling role analysis on the cell-cell communication network from user's input
gg1 + gg2

# (C) Identify signals contributing the most to outgoing or incoming signaling of certain cell groups
# Signaling role analysis on the aggregated cell-cell communication network from all signaling pathways
ht1 <- netAnalysis_signalingRole_heatmap(cellchat, pattern = "outgoing")
ht2 <- netAnalysis_signalingRole_heatmap(cellchat, pattern = "incoming")
ht1 + ht2

Warning: Heatmap/annotation names are duplicated: Relative strength

# Signaling role analysis on the cell-cell communication networks of interest
ht <- netAnalysis_signalingRole_heatmap(cellchat, signaling = c("CXCL", "CCL"))


#---------------------------------------------------------------------------------------------------------------------------
# **Identify global communication patterns to explore how multiple cell types and signaling pathways coordinate together**
#----------------------------------------------------------------------------------------------------------------------------

# (A) Identify and visualize outgoing communication pattern of secreting cells

library(NMF)

Loading required package: registry
Loading required package: rngtools
Loading required package: cluster
NMF - BioConductor layer [OK] | Shared memory capabilities [NO: bigmemory] | Cores 2/2
  To enable shared memory capabilities, try: install.extras('
NMF
')

Attaching package: ‘NMF’

The following objects are masked from ‘package:igraph’:

    algorithm, compare

#> Loading required package: registry
#> Loading required package: rngtools
#> Loading required package: cluster
#> NMF - BioConductor layer [OK] | Shared memory capabilities [NO: bigmemory] | Cores 2/2
#>   To enable shared memory capabilities, try: install.extras('
#> NMF
#> ')
#> 
#> Attaching package: 'NMF'
#> The following objects are masked from 'package:igraph':
#> 
#>     algorithm, compare
library(ggalluvial)

selectK(cellchat, pattern = "outgoing")

Warning: Continuous limits supplied to discrete scale.
ℹ Did you mean `limits = factor(...)` or `scale_*_continuous()`?

nPatterns = 6

cellchat <- identifyCommunicationPatterns(cellchat, pattern = "outgoing", k = nPatterns)

Warning: Recycling array of length 1 in array-vector arithmetic is deprecated.
  Use c() or as.vector() instead.
Warning: Recycling array of length 1 in array-vector arithmetic is deprecated.
  Use c() or as.vector() instead.
Warning: Recycling array of length 1 in array-vector arithmetic is deprecated.
  Use c() or as.vector() instead.
Warning: Recycling array of length 1 in array-vector arithmetic is deprecated.
  Use c() or as.vector() instead.
Warning: Recycling array of length 1 in array-vector arithmetic is deprecated.
  Use c() or as.vector() instead.
Warning: Recycling array of length 1 in array-vector arithmetic is deprecated.
  Use c() or as.vector() instead.
Warning: Recycling array of length 1 in array-vector arithmetic is deprecated.
  Use c() or as.vector() instead.
Warning: Recycling array of length 1 in array-vector arithmetic is deprecated.
  Use c() or as.vector() instead.
Warning: Recycling array of length 1 in array-vector arithmetic is deprecated.
  Use c() or as.vector() instead.
Warning: Recycling array of length 1 in array-vector arithmetic is deprecated.
  Use c() or as.vector() instead.

# river plot
netAnalysis_river(cellchat, pattern = "outgoing")

Please make sure you have load `library(ggalluvial)` when running this function

#> Please make sure you have load `library(ggalluvial)` when running this function


# dot plot
netAnalysis_dot(cellchat, pattern = "outgoing")

# (B) Identify and visualize incoming communication pattern of target cells

selectK(cellchat, pattern = "incoming")

Warning: Continuous limits supplied to discrete scale.
ℹ Did you mean `limits = factor(...)` or `scale_*_continuous()`?

 nPatterns = 3
cellchat <- identifyCommunicationPatterns(cellchat, pattern = "incoming", k = nPatterns)

Warning: Recycling array of length 1 in array-vector arithmetic is deprecated.
  Use c() or as.vector() instead.
Warning: Recycling array of length 1 in array-vector arithmetic is deprecated.
  Use c() or as.vector() instead.
Warning: Recycling array of length 1 in array-vector arithmetic is deprecated.
  Use c() or as.vector() instead.
Warning: Recycling array of length 1 in array-vector arithmetic is deprecated.
  Use c() or as.vector() instead.

# river plot
netAnalysis_river(cellchat, pattern = "incoming")

Please make sure you have load `library(ggalluvial)` when running this function

#> Please make sure you have load `library(ggalluvial)` when running this function


# dot plot
netAnalysis_dot(cellchat, pattern = "incoming")

#---------------------------------------------------------------------------------------------------------------------------
# **Manifold and classification learning analysis of signaling networks**
#----------------------------------------------------------------------------------------------------------------------------

# Identify signaling groups based on their functional similarity
#8.  Identify signaling roles and patterns
cellchat <- computeNetSimilarity(cellchat, type = "functional")
cellchat <- netEmbedding(cellchat, type = "functional")

Manifold learning of the signaling networks for a single dataset 

cellchat <- netClustering(cellchat, type = "functional")

Classification learning of the signaling networks for a single dataset 

Warning: UNRELIABLE VALUE: One of the ‘future.apply’ iterations (‘future_sapply-1’) unexpectedly generated random numbers without declaring so. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'future.seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'future.seed = NULL', or set option 'future.rng.onMisuse' to "ignore".Warning: UNRELIABLE VALUE: One of the ‘future.apply’ iterations (‘future_sapply-2’) unexpectedly generated random numbers without declaring so. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'future.seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'future.seed = NULL', or set option 'future.rng.onMisuse' to "ignore".Warning: UNRELIABLE VALUE: One of the ‘future.apply’ iterations (‘future_sapply-3’) unexpectedly generated random numbers without declaring so. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'future.seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'future.seed = NULL', or set option 'future.rng.onMisuse' to "ignore".Warning: UNRELIABLE VALUE: One of the ‘future.apply’ iterations (‘future_sapply-4’) unexpectedly generated random numbers without declaring so. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'future.seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'future.seed = NULL', or set option 'future.rng.onMisuse' to "ignore".Warning: all eigenvalues are requested, eigen() is used insteadWarning: all eigenvalues are requested, eigen() is used instead

netVisual_embedding(cellchat, type = "functional", label.size = 3.5)

Warning: Using alpha for a discrete variable is not advised.

# Identify signaling groups based on structure similarity
#install umap-learn before running these commands
 #pip install umap-learn
cellchat <- computeNetSimilarity(cellchat, type = "structural")
cellchat <- netEmbedding(cellchat, type = "structural")

Manifold learning of the signaling networks for a single dataset 

#> Manifold learning of the signaling networks for a single dataset
cellchat <- netClustering(cellchat, type = "structural")

Classification learning of the signaling networks for a single dataset 

Warning: UNRELIABLE VALUE: One of the ‘future.apply’ iterations (‘future_sapply-1’) unexpectedly generated random numbers without declaring so. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'future.seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'future.seed = NULL', or set option 'future.rng.onMisuse' to "ignore".Warning: UNRELIABLE VALUE: One of the ‘future.apply’ iterations (‘future_sapply-2’) unexpectedly generated random numbers without declaring so. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'future.seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'future.seed = NULL', or set option 'future.rng.onMisuse' to "ignore".Warning: UNRELIABLE VALUE: One of the ‘future.apply’ iterations (‘future_sapply-3’) unexpectedly generated random numbers without declaring so. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'future.seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'future.seed = NULL', or set option 'future.rng.onMisuse' to "ignore".Warning: UNRELIABLE VALUE: One of the ‘future.apply’ iterations (‘future_sapply-4’) unexpectedly generated random numbers without declaring so. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'future.seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'future.seed = NULL', or set option 'future.rng.onMisuse' to "ignore".Warning: all eigenvalues are requested, eigen() is used insteadWarning: all eigenvalues are requested, eigen() is used instead

#> Classification learning of the signaling networks for a single dataset
# Visualization in 2D-space
netVisual_embedding(cellchat, type = "structural", label.size = 3.5)

Warning: Using alpha for a discrete variable is not advised.

netVisual_embeddingZoomIn(cellchat, type = "structural", nCol = 2)

7. Save the CellChat object

save(cellchat, file = "cellCHAT_Analysis.Robj")