Inference and analysis of cell-cell communication using CellChat-SS_cellLines
Nasir Mahmood Abbasi
2024-09-20
1. load libraries
2. Load Seurat Object
#Load Seurat Object Integrated Object
load("/home/bioinfo/Cluster_to_Computer_Transfer_files_folder/All_Normal-PBMC_Abnormal-cellLines_T_cells_Merged_Annotated_UMAP_on_Clusters_to_USE.Robj")
All_samples_MergedAn object of class Seurat
62625 features across 46976 samples within 6 assays
Active assay: SCT (25901 features, 3000 variable features)
3 layers present: counts, data, scale.data
5 other assays present: RNA, ADT, prediction.score.celltype.l1, prediction.score.celltype.l2, prediction.score.celltype.l3
4 dimensional reductions calculated: pca, umap, integrated_dr, ref.umapDefaultAssay(All_samples_Merged) <- "SCT"
Idents(All_samples_Merged) <- "SCT_snn_res.0.9"
3. Data input & processing and initialization of CellChat object
#------------------------------------------------------------------------------
# **Part I: Data input & processing and initialization of CellChat object**
#------------------------------------------------------------------------------
# Loading and Preparing Data
All_samples_Merged <- AddMetaData(object = All_samples_Merged,
metadata = paste0("Cluster_", All_samples_Merged$seurat_clusters),
col.name = 'Clusters')
#All_samples_Merged[["RNA"]] <- JoinLayers(All_samples_Merged[["RNA"]])
# Extract the normalized data
#data_input <- GetAssayData(All_samples_Merged, assay = "RNA", slot = "data")
data.input <- All_samples_Merged@assays$SCT$data
#1. Create CellChat object
cellchat <- createCellChat(object = data.input, meta = All_samples_Merged@meta.data, group.by = "Clusters")[1] "Create a CellChat object from a data matrix"Warning: The 'meta' data does not have a column named `samples`. We now add this column and all cells are assumed to belong to `sample1`! Set cell identities for the new CellChat object
The cell groups used for CellChat analysis are Cluster_0, Cluster_1, Cluster_10, Cluster_11, Cluster_12, Cluster_13, Cluster_14, Cluster_15, Cluster_16, Cluster_17, Cluster_18, Cluster_19, Cluster_2, Cluster_20, Cluster_21, Cluster_22, Cluster_23, Cluster_24, Cluster_3, Cluster_4, Cluster_5, Cluster_6, Cluster_7, Cluster_8, Cluster_9 #2. Set the ligand-receptor interaction database
cellchatDB <- CellChatDB.human # or CellChatDB.mouse
showDatabaseCategory(cellchatDB)
# Show the structure of the database
dplyr::glimpse(cellchatDB$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 549 4. Inference of cell-cell communication network
#------------------------------------------------------------------------------
# **Part II: Inference of cell-cell communication network**
#------------------------------------------------------------------------------
#------------------------------------------------------------------------------------
# **Compute the communication probability and infer cellular communication network**
#------------------------------------------------------------------------------------
#4. Compute communication probability and infer cellular communication network
cellchat <- computeCommunProb(cellchat)triMean is used for calculating the average gene expression per cell group.
[1] ">>> Run CellChat on sc/snRNA-seq data <<< [2024-09-20 15:34:24.817166]"
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|==========================================================================================================================| 100%[1] ">>> CellChat inference is done. Parameter values are stored in `object@options$parameter` <<< [2024-09-20 15:39:47.003683]"cellchat <- filterCommunication(cellchat, min.cells = 10)
#------------------------------------------------------------------------------
# **Infer the cell-cell communication at a signaling pathway level**
#------------------------------------------------------------------------------
#5. Compute communication at the pathway level
cellchat <- computeCommunProbPathway(cellchat)
#------------------------------------------------------------------------------
# **Calculate the aggregated cell-cell communication network**
#------------------------------------------------------------------------------
#6. Aggregate the inferred cell-cell communication network
cellchat <- aggregateNet(cellchat)
#7. Visualize the communication network
groupSize <- as.numeric(table(cellchat@idents))
netVisual_circle(cellchat@net$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])
}
NA
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("CXCL")
# 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("CXCL", 6), rep("CCL", 6), rep("IL2", 6), rep("TNF", 7))
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[1:6] <- "CXCL"
group.cellType[7:12] <- "CCL"
group.cellType[13:18] <- "IL2"
group.cellType[19:25] <- "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
"CXCL" "CXCL" "CXCL" "CXCL" "CXCL" "CXCL" "CCL" "CCL" "CCL" "CCL" "CCL" "CCL"
Cluster_2 Cluster_20 Cluster_21 Cluster_22 Cluster_23 Cluster_24 Cluster_3 Cluster_4 Cluster_5 Cluster_6 Cluster_7 Cluster_8
"IL2" "IL2" "IL2" "IL2" "IL2" "IL2" "TNF" "TNF" "TNF" "TNF" "TNF" "TNF"
Cluster_9
"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_24"
[19] "Cluster_3" "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 FGFnetVisual_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")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.
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 % ~07s
|++++ | 7 % ~05s
|++++++ | 11% ~05s
|++++++++ | 14% ~04s
|+++++++++ | 18% ~03s
|+++++++++++ | 21% ~03s
|+++++++++++++ | 25% ~02s
|+++++++++++++++ | 29% ~02s
|+++++++++++++++++ | 32% ~02s
|++++++++++++++++++ | 36% ~02s
|++++++++++++++++++++ | 39% ~02s
|++++++++++++++++++++++ | 43% ~01s
|++++++++++++++++++++++++ | 46% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|+++++++++++++++++++++++++++ | 54% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|+++++++++++++++++++++++++++++++++ | 64% ~01s
|++++++++++++++++++++++++++++++++++ | 68% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~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 + ht2Warning: 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)
setwd("../SS_CellLines_Pipeline/")
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")
setwd(".")
# (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")
library(uwot)Loading required package: Matrixcellchat <- netEmbedding(cellchat, type = "functional", umap.method = "uwot")Manifold learning of the signaling networks for a single dataset options(future.globals.maxSize = 16000 * 1024^2) # Set to 16 GB
cellchat <- netClustering(cellchat, type = "functional", do.parallel = FALSE)Classification learning of the signaling networks for a single dataset
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Warning: all eigenvalues are requested, eigen() is used insteadWarning: all eigenvalues are requested, eigen() is used insteadnetVisual_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", umap.method = "uwot")Manifold learning of the signaling networks for a single dataset #> Manifold learning of the signaling networks for a single dataset
options(future.globals.maxSize = 16000 * 1024^2) # Set to 16 GB
cellchat <- netClustering(cellchat, type = "structural", do.parallel = FALSE)Classification learning of the signaling networks for a single dataset
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s 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_All_samples_Merged.Robj")---
title: "Inference and analysis of cell-cell communication using CellChat-SS_cellLines"
author: Nasir Mahmood Abbasi
date: "`r Sys.Date()`"
output:
  #rmdformats::readthedown
  html_notebook:
    toc: true
    toc_float: true
    toc_collapsed: true
---

# 1. load libraries
```{r setup, include=FALSE}

library(Seurat)
library(dplyr)
library(CellChat)
library(patchwork)
options(stringsAsFactors = FALSE)
# reticulate::use_python("/Users/suoqinjin/anaconda3/bin/python", required=T) 

```

# 2. Load Seurat Object
```{r load_seuratOBJ}

#Load Seurat Object Integrated Object
load("/home/bioinfo/Cluster_to_Computer_Transfer_files_folder/All_Normal-PBMC_Abnormal-cellLines_T_cells_Merged_Annotated_UMAP_on_Clusters_to_USE.Robj")
 
  All_samples_Merged

DefaultAssay(All_samples_Merged) <- "SCT"

Idents(All_samples_Merged) <- "SCT_snn_res.0.9"

```


# 3. Data input & processing and initialization of CellChat object
```{r DATAINPUT, fig.height=6, fig.width=10}

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

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

#All_samples_Merged[["RNA"]] <- JoinLayers(All_samples_Merged[["RNA"]])


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


data.input <- All_samples_Merged@assays$SCT$data


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




#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)

# 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)



```

# 4. Inference of cell-cell communication network
```{r INFERENCE, fig.height=6, fig.width=10}
#------------------------------------------------------------------------------
# **Part II: Inference of cell-cell communication network**
#------------------------------------------------------------------------------

#------------------------------------------------------------------------------------
# **Compute the communication probability and infer cellular communication network**
#------------------------------------------------------------------------------------

#4.  Compute communication probability and infer cellular communication network
cellchat <- computeCommunProb(cellchat)
cellchat <- filterCommunication(cellchat, min.cells = 10)

#------------------------------------------------------------------------------
# **Infer the cell-cell communication at a signaling pathway level**
#------------------------------------------------------------------------------
#5.  Compute communication at the pathway level
cellchat <- computeCommunProbPathway(cellchat)

#------------------------------------------------------------------------------
# **Calculate the aggregated cell-cell communication network**
#------------------------------------------------------------------------------
#6.  Aggregate the inferred cell-cell communication network
cellchat <- aggregateNet(cellchat)


#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
```{r VISUALIZATION, fig.height=6, fig.width=10}
#------------------------------------------------------------------------------
# **Part III: Visualization of cell-cell communication network**
#------------------------------------------------------------------------------
 
#----------------------------------------------------------------------------------------------------
# **Visualize each signaling pathway using Hierarchy plot, Circle plot or Chord diagram**
#----------------------------------------------------------------------------------------------------
  
pathways.show <- c("CXCL") 
# 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



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

# Chord diagram
group.cellType <- c(rep("CXCL", 6), rep("CCL", 6), rep("IL2", 6), rep("TNF", 7))

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[1:6] <- "CXCL"
group.cellType[7:12] <- "CCL"
group.cellType[13:18] <- "IL2"
group.cellType[19:25] <- "TNF"

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

# Check the result
print(group.cellType)

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


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



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

# 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)
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



# (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


# (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"))
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


# (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")


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

```

# 6. Systems analysis of cell-cell communication network
```{r SYSTEMS-ANALYSIS, fig.height=6, fig.width=10}
#----------------------------------------------------------------------------------------------------
# **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
# 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 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
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

# 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
library(ggalluvial)


setwd("../SS_CellLines_Pipeline/")
selectK(cellchat, pattern = "outgoing")


nPatterns = 6

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


# river plot
netAnalysis_river(cellchat, pattern = "outgoing")
#> Please make sure you have load `library(ggalluvial)` when running this function


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

setwd(".")

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

selectK(cellchat, pattern = "incoming")



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


# river plot
netAnalysis_river(cellchat, pattern = "incoming")
#> 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")
library(uwot)
cellchat <- netEmbedding(cellchat, type = "functional", umap.method = "uwot")
options(future.globals.maxSize = 16000 * 1024^2)  # Set to 16 GB
cellchat <- netClustering(cellchat, type = "functional", do.parallel = FALSE)
netVisual_embedding(cellchat, type = "functional", label.size = 3.5)

# 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", umap.method = "uwot")
#> Manifold learning of the signaling networks for a single dataset
options(future.globals.maxSize = 16000 * 1024^2)  # Set to 16 GB
cellchat <- netClustering(cellchat, type = "structural", do.parallel = FALSE)
#> Classification learning of the signaling networks for a single dataset
# Visualization in 2D-space
netVisual_embedding(cellchat, type = "structural", label.size = 3.5)


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

```
# 7. Save the CellChat object
```{r SAVE-CELLCHAT, eval = FALSE}


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

```
