This vignette shows how to apply CellChat to identify major signaling changes across different biological conditions by quantitative contrasts and joint manifold learning. We showcase CellChat’s diverse functionalities for identifying major signaling changes across conditions by applying it to scRNA-seq datasets from two biological conditions: nonlesional (NL, normal) and lesional (LS, diseased) human skin from patients with atopic dermatitis. These two datasets (conditions) have the same cell population compositions after joint clustering. If there are different cell population compositions between different conditions, please check out the tutorial on Comparison analysis of multiple datasets with different cell type compositions.

CellChat employs a top-down approach, i.e., starting with the big picture and then refining it in a greater detail on the signaling mechanisms, to identify signaling changes at different levels, including altered interactions, cell populations, signaling pathways and ligand-receptor pairs.

Load the required libraries

ptm = Sys.time()
library(CellChat)
library(patchwork)
# reticulate::use_python("/Users/suoqinjin/anaconda3/bin/python", required=T)

Create a directory to save figures

data.dir <- './comparison'
dir.create(data.dir)
setwd(data.dir)

Load CellChat object of each dataset and merge them together

Users need to first run CellChat on each dataset separately and then merge different CellChat objects together. Please do updateCellChat if the CellChat objects are obtained using the earlier version (< 1.6.0).

cellchat.NL <- readRDS("/Users/suoqinjin/Library/CloudStorage/OneDrive-Personal/works/CellChat/tutorial/cellchat_humanSkin_NL.rds")
cellchat.LS <- readRDS("/Users/suoqinjin/Library/CloudStorage/OneDrive-Personal/works/CellChat/tutorial/cellchat_humanSkin_LS.rds")
object.list <- list(NL = cellchat.NL, LS = cellchat.LS)
cellchat <- mergeCellChat(object.list, add.names = names(object.list))
#> Merge the following slots: 'data.signaling','images','net', 'netP','meta', 'idents', 'var.features' , 'DB', and 'LR'.
cellchat
#> An object of class CellChat created from a merged object with multiple datasets 
#>  593 signaling genes.
#>  7563 cells. 
#> CellChat analysis of single cell RNA-seq data!
execution.time = Sys.time() - ptm
print(as.numeric(execution.time, units = "secs"))
#> [1] 2.763455
# Users can now export the merged CellChat object and the list of the two separate objects for later use
save(object.list, file = "cellchat_object.list_humanSkin_NL_LS.RData")
save(cellchat, file = "cellchat_merged_humanSkin_NL_LS.RData")

Part I: Identify altered interactions and cell populations

CellChat employs a top-down approach, i.e., starting with the big picture and then refining it in a greater detail on the signaling mechanisms, to identify signaling changes at different levels, including altered interactions, cell populations, signaling pathways and ligand-receptor pairs. First, CellChat starts with a big picture to answer the following questions:

Compare the total number of interactions and interaction strength

To answer the question on whether the cell-cell communication is enhanced or not, CellChat compares the total number of interactions and interaction strength of the inferred cell-cell communication networks from different biological conditions.

ptm = Sys.time()
gg1 <- compareInteractions(cellchat, show.legend = F, group = c(1,2))
gg2 <- compareInteractions(cellchat, show.legend = F, group = c(1,2), measure = "weight")
gg1 + gg2

Compare the number of interactions and interaction strength among different cell populations

To identify the interaction between which cell populations showing significant changes, CellChat compares the number of interactions and interaction strength among different cell populations using a circle plot with differential interactions (option A), a heatmap with differential interactions (option B) and two circle plots with the number of interactions or interaction strength per dataset (option C). Alternatively, users can examine the differential number of interactions or interaction strength among coarse cell types by aggregating the cell-cell communication based on the defined cell groups (option D).

(A) Circle plot showing differential number of interactions or interaction strength among different cell populations across two datasets

The differential number of interactions or interaction strength in the cell-cell communication network between two datasets can be visualized using circle plot, where \(\color{red}{\text{red}}\) (or \(\color{blue}{\text{blue}}\)) colored edges represent \(\color{red}{\text{increased}}\) (or \(\color{blue}{\text{decreased}}\)) signaling in the second dataset compared to the first one.

par(mfrow = c(1,2), xpd=TRUE)
netVisual_diffInteraction(cellchat, weight.scale = T)
netVisual_diffInteraction(cellchat, weight.scale = T, measure = "weight")

(B) Heatmap showing differential number of interactions or interaction strength among different cell populations across two datasets

CellChat can also show differential number of interactions or interaction strength in greater details using a heatmap. The top colored bar plot represents the sum of each column of the absolute values displayed in the heatmap (incoming signaling). The right colored bar plot represents the sum of each row of the absolute values (outgoing signaling). Therefore, the bar height indicates the degree of change in terms of the number of interactions or interaction strength between the two conditions. In the colorbar, \(\color{red}{\text{red}}\) (or \(\color{blue}{\text{blue}}\)) represents \(\color{red}{\text{increased}}\) (or \(\color{blue}{\text{decreased}}\)) signaling in the second dataset compared to the first one.

gg1 <- netVisual_heatmap(cellchat)
#> Do heatmap based on a merged object
gg2 <- netVisual_heatmap(cellchat, measure = "weight")
#> Do heatmap based on a merged object
gg1 + gg2

(C) Circle plot showing the number of interactions or interaction strength among different cell populations across multiple datasets

The above differential network analysis only works for pairwise datasets. If there are more datasets for comparison, CellChat can directly show the number of interactions or interaction strength between any two cell populations in each dataset.

To better control the node size and edge weights of the inferred networks across different datasets, CellChat computes the maximum number of cells per cell group and the maximum number of interactions (or interaction weights) across all datasets.

weight.max <- getMaxWeight(object.list, attribute = c("idents","count"))
par(mfrow = c(1,2), xpd=TRUE)
for (i in 1:length(object.list)) {
  netVisual_circle(object.list[[i]]@net$count, weight.scale = T, label.edge= F, edge.weight.max = weight.max[2], edge.width.max = 12, title.name = paste0("Number of interactions - ", names(object.list)[i]))
}

(D) Circle plot showing the differential number of interactions or interaction strength among coarse cell types

To simplify the complicated network and gain insights into the cell-cell communication at the cell type level, CellChat aggregates the cell-cell communication based on the defined cell groups. Here we categorize the cell populations into three cell types, and then re-merge the list of CellChat object.

group.cellType <- c(rep("FIB", 4), rep("DC", 4), rep("TC", 4))
group.cellType <- factor(group.cellType, levels = c("FIB", "DC", "TC"))
object.list <- lapply(object.list, function(x) {mergeInteractions(x, group.cellType)})
cellchat <- mergeCellChat(object.list, add.names = names(object.list))
#> Merge the following slots: 'data.signaling','images','net', 'netP','meta', 'idents', 'var.features' , 'DB', and 'LR'.

We then can show the number of interactions or interaction strength between any two cell types in each dataset.

weight.max <- getMaxWeight(object.list, slot.name = c("idents", "net", "net"), attribute = c("idents","count", "count.merged"))
par(mfrow = c(1,2), xpd=TRUE)
for (i in 1:length(object.list)) {
  netVisual_circle(object.list[[i]]@net$count.merged, weight.scale = T, label.edge= T, edge.weight.max = weight.max[3], edge.width.max = 12, title.name = paste0("Number of interactions - ", names(object.list)[i]))
}

Similarly, CellChat can also show the differential number of interactions or interaction strength between any two cell types using circle plot. Red (or blue) colored edges represent increased (or decreased) signaling in the second dataset compared to the first one.

par(mfrow = c(1,2), xpd=TRUE)
netVisual_diffInteraction(cellchat, weight.scale = T, measure = "count.merged", label.edge = T)
netVisual_diffInteraction(cellchat, weight.scale = T, measure = "weight.merged", label.edge = T)

Compare the major sources and targets in a 2D space

Comparing the outgoing and incoming interaction strength in a 2D space allows ready identification of the cell populations with significant changes in sending or receiving signals between different datasets.

Identify cell populations with significant changes in sending or receiving signals between different datasets by following option A, and the signaling changes of specific cell populations by following option B.

(A) Identify cell populations with significant changes in sending or receiving signals

num.link <- sapply(object.list, function(x) {rowSums(x@net$count) + colSums(x@net$count)-diag(x@net$count)})
weight.MinMax <- c(min(num.link), max(num.link)) # control the dot size in the different datasets
gg <- list()
for (i in 1:length(object.list)) {
  gg[[i]] <- netAnalysis_signalingRole_scatter(object.list[[i]], title = names(object.list)[i], weight.MinMax = weight.MinMax)
}
#> 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
patchwork::wrap_plots(plots = gg)

From the scatter plot, we can see that Inflam.DC and cDC1 emerge as one of the major source and targets in LS compared to NL. Fibroblast populations also become the major sources in LS.

(B) Identify the signaling changes of specific cell populations

Furthermore, we can identify the specific signaling changes of Inflam.DC and cDC1 between NL and LS.

gg1 <- netAnalysis_signalingChanges_scatter(cellchat, idents.use = "Inflam. DC", signaling.exclude = "MIF")
#> Visualizing differential outgoing and incoming signaling changes from NL to LS
#> The following `from` values were not present in `x`: 0
#> The following `from` values were not present in `x`: 0, -1
gg2 <- netAnalysis_signalingChanges_scatter(cellchat, idents.use = "cDC1", signaling.exclude = c("MIF"))
#> Visualizing differential outgoing and incoming signaling changes from NL to LS
#> The following `from` values were not present in `x`: 0, 2
#> The following `from` values were not present in `x`: 0, -1
patchwork::wrap_plots(plots = list(gg1,gg2))


execution.time = Sys.time() - ptm
print(as.numeric(execution.time, units = "secs"))
#> [1] 1.982786

Part II: Identify altered signaling with distinct network architecture and interaction strength

CellChat then can identify signaling with larger (or smaller) network difference as well as signaling groups based on their functional or structure similarity across multiple biological conditions.

Identify signaling networks with larger (or less) difference as well as signaling groups based on their functional/structure similarity

CellChat performs joint manifold learning and classification of the inferred communication networks based on their functional and topological similarity across different conditions. NB: Such analysis is applicable to more than two datasets.

By quantifying the similarity between the cellular communication networks of signaling pathways across conditions, this analysis highlights the potentially altered signaling pathways. CellChat adopts the concept of network rewiring from network biology and hypothesized that the difference between different communication networks may affect biological processes across conditions. UMAP is used for visualizing signaling relationship and interpreting our signaling outputs in an intuitive way without involving the classification of conditions.

Functional similarity: High degree of functional similarity indicates major senders and receivers are similar, and it can be interpreted as the two signaling pathways or two ligand-receptor pairs exhibit similar and/or redundant roles. NB: Functional similarity analysis is not applicable to multiple datsets with different cell type composition.

Structural similarity: A structural similarity was used to compare their signaling network structure, without considering the similarity of senders and receivers. NB: Structural similarity analysis is applicable to multiple datsets with the same cell type composition or the vastly different cell type composition.

Here we can run the manifold and classification learning analysis based on the functional similarity because the two datasets have the the same cell type composition.

Identify signaling groups based on their functional similarity

ptm = Sys.time()

cellchat <- computeNetSimilarityPairwise(cellchat, type = "functional")
#> Compute signaling network similarity for datasets 1 2
cellchat <- netEmbedding(cellchat, type = "functional")
#> Manifold learning of the signaling networks for datasets 1 2
cellchat <- netClustering(cellchat, type = "functional")
#> Classification learning of the signaling networks for datasets 1 2
# Visualization in 2D-space
netVisual_embeddingPairwise(cellchat, type = "functional", label.size = 3.5)
#> 2D visualization of signaling networks from datasets 1 2

# netVisual_embeddingZoomIn(cellchat, type = "functional", nCol = 2)

Identify signaling groups based on structure similarity

cellchat <- computeNetSimilarityPairwise(cellchat, type = "structural")
cellchat <- netEmbedding(cellchat, type = "structural")
cellchat <- netClustering(cellchat, type = "structural")
# Visualization in 2D-space
netVisual_embeddingPairwise(cellchat, type = "structural", label.size = 3.5)
netVisual_embeddingPairwiseZoomIn(cellchat, type = "structural", nCol = 2)

Compute and visualize the pathway distance in the learned joint manifold

CellChat can identify the signaling networks with larger (or smaller) difference based on their Euclidean distance in the shared two-dimensions space. Larger distance implies larger difference of the communication networks between two datasets in terms of either functional or structure similarity. It should be noted that we only compute the distance of overlapped signaling pathways between two datasets. Those signaling pathways that are only identified in one dataset are not considered here. If there are more than three datasets, one can do pairwise comparisons by modifying the parameter comparison in the function rankSimilarity.

rankSimilarity(cellchat, type = "functional")
#> Compute the distance of signaling networks between datasets 1 2

Identify altered signaling with distinct interaction strength

By comparing the information flow/interaction strength of each signaling pathway, CellChat identifies signaling pathways that: (i) turn off, (ii) decrease, (iii) turn on, or (iv) increase, by changing their information flow at one condition as compared to another condition. Identify the altered signaling pathways or ligand-receptor pairs based on the overall information flow by following option A, and based on the outgoing (or incoming) signaling patterns by following option B.

(A) Compare the overall information flow of each signaling pathway or ligand-receptor pair

CellChat can identify the conserved and context-specific signaling pathways by simply comparing the information flow for each signaling pathway, which is defined by the sum of communication probability among all pairs of cell groups in the inferred network (i.e., the total weights in the network).

This bar chart can be plotted in a stacked mode or not. Significant signaling pathways were ranked based on differences in the overall information flow within the inferred networks between NL and LS skin. When setting do.stat = TRUE, a paired Wilcoxon test is performed to determine whether there is a significant difference of the signaling information flow between two conditions. The top signaling pathways colored red are enriched in NL skin, and these colored greens are enriched in the LS skin.

gg1 <- rankNet(cellchat, mode = "comparison", measure = "weight", sources.use = NULL, targets.use = NULL, stacked = T, do.stat = TRUE)
gg2 <- rankNet(cellchat, mode = "comparison", measure = "weight", sources.use = NULL, targets.use = NULL, stacked = F, do.stat = TRUE)

gg1 + gg2

(B) Compare outgoing (or incoming) signaling patterns associated with each cell population

The above analysis summarize the information from the outgoing and incoming signaling together. CellChat can also compare the outgoing (or incoming) signaling pattern between two datasets, allowing to identify signaling pathways/ligand-receptors that exhibit different signaling patterns. We can combine all the identified signaling pathways from different datasets and thus compare them side by side, including outgoing signaling, incoming signaling and overall signaling by aggregating outgoing and incoming signaling together.

CellChat uses heatmap plot to show the contribution of signals (signaling pathways or ligand-receptor pairs) to cell groups in terms of outgoing or incoming signaling. In this heatmap, colobar represents the relative signaling strength of a signaling pathway across cell groups (Note that values are row-scaled). The top colored bar plot shows the total signaling strength of a cell group by summarizing all signaling pathways displayed in the heatmap. The right grey bar plot shows the total signaling strength of a signaling pathway by summarizing all cell groups displayed in the heatmap.

library(ComplexHeatmap)
#> Loading required package: grid
#> ========================================
#> ComplexHeatmap version 2.15.4
#> Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
#> Github page: https://github.com/jokergoo/ComplexHeatmap
#> Documentation: http://jokergoo.github.io/ComplexHeatmap-reference
#> 
#> If you use it in published research, please cite either one:
#> - Gu, Z. Complex Heatmap Visualization. iMeta 2022.
#> - Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional 
#>     genomic data. Bioinformatics 2016.
#> 
#> 
#> The new InteractiveComplexHeatmap package can directly export static 
#> complex heatmaps into an interactive Shiny app with zero effort. Have a try!
#> 
#> This message can be suppressed by:
#>   suppressPackageStartupMessages(library(ComplexHeatmap))
#> ========================================
i = 1
# combining all the identified signaling pathways from different datasets 
pathway.union <- union(object.list[[i]]@netP$pathways, object.list[[i+1]]@netP$pathways)
ht1 = netAnalysis_signalingRole_heatmap(object.list[[i]], pattern = "outgoing", signaling = pathway.union, title = names(object.list)[i], width = 5, height = 6)
ht2 = netAnalysis_signalingRole_heatmap(object.list[[i+1]], pattern = "outgoing", signaling = pathway.union, title = names(object.list)[i+1], width = 5, height = 6)
draw(ht1 + ht2, ht_gap = unit(0.5, "cm"))


execution.time = Sys.time() - ptm
print(as.numeric(execution.time, units = "secs"))
#> [1] 17.62665
ht1 = netAnalysis_signalingRole_heatmap(object.list[[i]], pattern = "incoming", signaling = pathway.union, title = names(object.list)[i], width = 5, height = 6, color.heatmap = "GnBu")
ht2 = netAnalysis_signalingRole_heatmap(object.list[[i+1]], pattern = "incoming", signaling = pathway.union, title = names(object.list)[i+1], width = 5, height = 6, color.heatmap = "GnBu")
draw(ht1 + ht2, ht_gap = unit(0.5, "cm"))
ht1 = netAnalysis_signalingRole_heatmap(object.list[[i]], pattern = "all", signaling = pathway.union, title = names(object.list)[i], width = 5, height = 6, color.heatmap = "OrRd")
ht2 = netAnalysis_signalingRole_heatmap(object.list[[i+1]], pattern = "all", signaling = pathway.union, title = names(object.list)[i+1], width = 5, height = 6, color.heatmap = "OrRd")
draw(ht1 + ht2, ht_gap = unit(0.5, "cm"))

Part III: Identify the up-gulated and down-regulated signaling ligand-receptor pairs

Identify dysfunctional signaling by comparing the communication probabities

CellChat can compare the communication probabilities mediated by L-R pairs from certain cell groups to other cell groups. This can be done by setting comparison in the function netVisual_bubble.

ptm = Sys.time()

netVisual_bubble(cellchat, sources.use = 4, targets.use = c(5:11),  comparison = c(1, 2), angle.x = 45)
#> Comparing communications on a merged object

Moreover, CellChat can identify the up-regulated (increased) and down-regulated (decreased) signaling ligand-receptor pairs in one dataset compared to the other dataset. This can be done by specifying max.dataset and min.dataset in the function netVisual_bubble. The increased signaling means these signaling have higher communication probability (strength) in the second dataset compared to the first dataset. The ligand-receptor pairs shown in the bubble plot can be accessed via gg1$data.

gg1 <- netVisual_bubble(cellchat, sources.use = 4, targets.use = c(5:11),  comparison = c(1, 2), max.dataset = 2, title.name = "Increased signaling in LS", angle.x = 45, remove.isolate = T)
#> Comparing communications on a merged object
gg2 <- netVisual_bubble(cellchat, sources.use = 4, targets.use = c(5:11),  comparison = c(1, 2), max.dataset = 1, title.name = "Decreased signaling in LS", angle.x = 45, remove.isolate = T)
#> Comparing communications on a merged object
gg1 + gg2

NB: The ligand-receptor pairs shown in the bubble plot can be accessed via signaling.LSIncreased = gg1$data.

Identify dysfunctional signaling by using differential expression analysis

The above method for identifying the upgulated and down-regulated signaling is perfomed by comparing the communication probability between two datasets for each L-R pair and each pair of cell groups. Alternative, we can identify the upgulated and down-regulated signaling ligand-receptor pairs based on the differential expression analysis (DEA). Specifically, we perform differential expression analysis between two biological conditions (i.e., NL and LS) for each cell group, and then obtain the upgulated and down-regulated signaling based on the fold change of ligands in the sender cells and receptors in the receiver cells.

Of note, users may observe the same LR pairs appearing in both the up-regulated and down-regulated results due to the fact that DEA between conditions is performed for each cell group. To perform DEA between conditions by ignoring cell group information, users can set group.DE.combined = TRUE in identifyOverExpressedGenes for CellChat v2.1.1.

# define a positive dataset, i.e., the dataset with positive fold change against the other dataset
pos.dataset = "LS"
# define a char name used for storing the results of differential expression analysis
features.name = paste0(pos.dataset, ".merged")

# perform differential expression analysis 
# Of note, compared to CellChat version < v2, CellChat v2 now performs an ultra-fast Wilcoxon test using the presto package, which gives smaller values of logFC. Thus we here set a smaller value of thresh.fc compared to the original one (thresh.fc = 0.1). Users can also provide a vector and dataframe of customized DEGs by modifying the cellchat@var.features$LS.merged and cellchat@var.features$LS.merged.info. 

cellchat <- identifyOverExpressedGenes(cellchat, group.dataset = "datasets", pos.dataset = pos.dataset, features.name = features.name, only.pos = FALSE, thresh.pc = 0.1, thresh.fc = 0.05,thresh.p = 0.05, group.DE.combined = FALSE) 
#> Use the joint cell labels from the merged CellChat object

# map the results of differential expression analysis onto the inferred cell-cell communications to easily manage/subset the ligand-receptor pairs of interest
net <- netMappingDEG(cellchat, features.name = features.name, variable.all = TRUE)
# extract the ligand-receptor pairs with upregulated ligands in LS
net.up <- subsetCommunication(cellchat, net = net, datasets = "LS",ligand.logFC = 0.05, receptor.logFC = NULL)
# extract the ligand-receptor pairs with upregulated ligands and upregulated receptors in NL, i.e.,downregulated in LS
net.down <- subsetCommunication(cellchat, net = net, datasets = "NL",ligand.logFC = -0.05, receptor.logFC = NULL)

Since the signaling genes in the net.up and net.down might be complex with multi-subunits, we can do further deconvolution to obtain the individual signaling genes.

gene.up <- extractGeneSubsetFromPair(net.up, cellchat)
gene.down <- extractGeneSubsetFromPair(net.down, cellchat)

Users can also find all the significant outgoing/incoming/both signaling according to the customized features and cell groups of interest

df <- findEnrichedSignaling(object.list[[2]], features = c("CCL19", "CXCL12"), idents = c("Inflam. FIB", "COL11A1+ FIB"), pattern ="outgoing")

Visualize the identified up-regulated and down-regulated signaling ligand-receptor pairs

CellChat can visualize the identified up-regulated and down-regulated signaling ligand-receptor pairs using bubble plot (option A), chord diagram (option B) or wordcloud (option C).

(A) Bubble plot

We then visualize the upgulated and down-regulated signaling ligand-receptor pairs using bubble plot or chord diagram.

pairLR.use.up = net.up[, "interaction_name", drop = F]
gg1 <- netVisual_bubble(cellchat, pairLR.use = pairLR.use.up, sources.use = 4, targets.use = c(5:11), comparison = c(1, 2),  angle.x = 90, remove.isolate = T,title.name = paste0("Up-regulated signaling in ", names(object.list)[2]))
#> Comparing communications on a merged object
pairLR.use.down = net.down[, "interaction_name", drop = F]
gg2 <- netVisual_bubble(cellchat, pairLR.use = pairLR.use.down, sources.use = 4, targets.use = c(5:11), comparison = c(1, 2),  angle.x = 90, remove.isolate = T,title.name = paste0("Down-regulated signaling in ", names(object.list)[2]))
#> Comparing communications on a merged object
gg1 + gg2

(B) Chord diagram

Visualize the upgulated and down-regulated signaling ligand-receptor pairs using Chord diagram

# Chord diagram
par(mfrow = c(1,2), xpd=TRUE)
netVisual_chord_gene(object.list[[2]], sources.use = 4, targets.use = c(5:11), slot.name = 'net', net = net.up, lab.cex = 0.8, small.gap = 3.5, title.name = paste0("Up-regulated signaling in ", names(object.list)[2]))
netVisual_chord_gene(object.list[[1]], sources.use = 4, targets.use = c(5:11), slot.name = 'net', net = net.down, lab.cex = 0.8, small.gap = 3.5, title.name = paste0("Down-regulated signaling in ", names(object.list)[2]))
#> You may try the function `netVisual_chord_cell` for visualizing individual signaling pathway

(C) Wordcloud plot

Visualize the enriched ligands, signaling,or ligand-receptor pairs in one condition compared to another condition using wordcloud

# visualize the enriched ligands in the first condition
computeEnrichmentScore(net.down, species = 'human', variable.both = TRUE)


# visualize the enriched ligands in the second condition
computeEnrichmentScore(net.up, species = 'human', variable.both = TRUE)

Part IV: Visually compare cell-cell communication using Hierarchy plot, Circle plot or Chord diagram

Similar to the CellChat analysis of individual dataset, CellChat can visually compare cell-cell communication networks using hierarchy plot, circle plot, chord diagram, or heatmap. More details on the visualization can be found in the CellChat analysis of individual dataset.

Edge color/weight, node color/size/shape: In all visualization plots, edge colors are consistent with the sources as sender, and edge weights are proportional to the interaction strength. Thicker edge line indicates a stronger signal. In the Hierarchy plot and Circle plot, circle sizes are proportional to the number of cells in each cell group. In the hierarchy plot, solid and open circles represent source and target, respectively. In the Chord diagram, the inner thinner bar colors represent the targets that receive signal from the corresponding outer bar. The inner bar size is proportional to the signal strength received by the targets. Such inner bar is helpful for interpreting the complex chord diagram. Note that there exist some inner bars without any chord for some cell groups, please just igore it because this is an issue that has not been addressed by circlize package.

pathways.show <- c("CXCL") 
weight.max <- getMaxWeight(object.list, slot.name = c("netP"), attribute = pathways.show) # control the edge weights across different datasets
par(mfrow = c(1,2), xpd=TRUE)
for (i in 1:length(object.list)) {
  netVisual_aggregate(object.list[[i]], signaling = pathways.show, layout = "circle", edge.weight.max = weight.max[1], edge.width.max = 10, signaling.name = paste(pathways.show, names(object.list)[i]))
}

pathways.show <- c("CXCL") 
par(mfrow = c(1,2), xpd=TRUE)
ht <- list()
for (i in 1:length(object.list)) {
  ht[[i]] <- netVisual_heatmap(object.list[[i]], signaling = pathways.show, color.heatmap = "Reds",title.name = paste(pathways.show, "signaling ",names(object.list)[i]))
}
#> Do heatmap based on a single object 
#> 
#> Do heatmap based on a single object
ComplexHeatmap::draw(ht[[1]] + ht[[2]], ht_gap = unit(0.5, "cm"))

# Chord diagram
pathways.show <- c("CXCL") 
par(mfrow = c(1,2), xpd=TRUE)
for (i in 1:length(object.list)) {
  netVisual_aggregate(object.list[[i]], signaling = pathways.show, layout = "chord", signaling.name = paste(pathways.show, names(object.list)[i]))
}

For the chord diagram, CellChat has an independent function netVisual_chord_cell to flexibly visualize the signaling network by adjusting different parameters in the circlize package. For example, we can define a named char vector group to create multiple-group chord diagram, e.g., grouping cell clusters into different cell types.

# Chord diagram
group.cellType <- c(rep("FIB", 4), rep("DC", 4), rep("TC", 4)) # grouping cell clusters into fibroblast, DC and TC cells
names(group.cellType) <- levels(object.list[[1]]@idents)
pathways.show <- c("CXCL") 
par(mfrow = c(1,2), xpd=TRUE)
for (i in 1:length(object.list)) {
  netVisual_chord_cell(object.list[[i]], signaling = pathways.show, group = group.cellType, title.name = paste0(pathways.show, " signaling network - ", names(object.list)[i]))
}
#> Plot the aggregated cell-cell communication network at the signaling pathway level
#> Plot the aggregated cell-cell communication network at the signaling pathway level

Using chord diagram, CellChat provides two functions netVisual_chord_cell and netVisual_chord_gene for visualizing cell-cell communication with different purposes and different levels. netVisual_chord_cell is used for visualizing the cell-cell communication between different cell groups (where each sector in the chord diagram is a cell group), and netVisual_chord_gene is used for visualizing the cell-cell communication mediated by mutiple ligand-receptors or signaling pathways (where each sector in the chord diagram is a ligand, receptor or signaling pathway.)

par(mfrow = c(1, 2), xpd=TRUE)
# compare all the interactions sending from Inflam.FIB to DC cells
for (i in 1:length(object.list)) {
  netVisual_chord_gene(object.list[[i]], sources.use = 4, targets.use = c(5:8), lab.cex = 0.5, title.name = paste0("Signaling from Inflam.FIB - ", names(object.list)[i]))
}


# compare all the interactions sending from fibroblast to inflamatory immune cells
par(mfrow = c(1, 2), xpd=TRUE)
for (i in 1:length(object.list)) {
  netVisual_chord_gene(object.list[[i]], sources.use = c(1,2, 3, 4), targets.use = c(8,10),  title.name = paste0("Signaling received by Inflam.DC and .TC - ", names(object.list)[i]), legend.pos.x = 10)
}

# show all the significant signaling pathways from fibroblast to immune cells
par(mfrow = c(1, 2), xpd=TRUE)
for (i in 1:length(object.list)) {
  netVisual_chord_gene(object.list[[i]], sources.use = c(1,2,3,4), targets.use = c(5:11),slot.name = "netP", title.name = paste0("Signaling pathways sending from fibroblast - ", names(object.list)[i]), legend.pos.x = 10)
}

NB: Please ignore the note when generating the plot such as “Note: The first link end is drawn out of sector ‘MIF’.”. If the gene names are overlapped, you can adjust the argument small.gap by decreasing the value.

Part V: Compare the signaling gene expression distribution between different datasets

We can plot the gene expression distribution of signaling genes related to L-R pairs or signaling pathway using a Seurat wrapper function plotGeneExpression.

cellchat@meta$datasets = factor(cellchat@meta$datasets, levels = c("NL", "LS")) # set factor level
plotGeneExpression(cellchat, signaling = "CXCL", split.by = "datasets", colors.ggplot = T, type = "violin")
#> The legacy packages maptools, rgdal, and rgeos, underpinning the sp package,
#> which was just loaded, were retired in October 2023.
#> Please refer to R-spatial evolution reports for details, especially
#> https://r-spatial.org/r/2023/05/15/evolution4.html.
#> It may be desirable to make the sf package available;
#> package maintainers should consider adding sf to Suggests:.
#> The default behaviour of split.by has changed.
#> Separate violin plots are now plotted side-by-side.
#> To restore the old behaviour of a single split violin,
#> set split.plot = TRUE.
#>       
#> This message will be shown once per session.
#> Scale for y is already present.
#> Adding another scale for y, which will replace the existing scale.
#> Scale for y is already present.
#> Adding another scale for y, which will replace the existing scale.
#> Scale for y is already present.
#> Adding another scale for y, which will replace the existing scale.


execution.time = Sys.time() - ptm
print(as.numeric(execution.time, units = "secs"))
#> [1] 7.094303

Save the merged CellChat object

save(object.list, file = "cellchat_object.list_humanSkin_NL_LS.RData")
save(cellchat, file = "cellchat_merged_humanSkin_NL_LS.RData")

sessionInfo()
#> R version 4.3.1 (2023-06-16)
#> Platform: aarch64-apple-darwin20 (64-bit)
#> Running under: macOS Ventura 13.5
#> 
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0
#> 
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#> 
#> time zone: Asia/Shanghai
#> tzcode source: internal
#> 
#> attached base packages:
#> [1] grid      stats     graphics  grDevices utils     datasets  methods  
#> [8] base     
#> 
#> other attached packages:
#> [1] ComplexHeatmap_2.15.4 patchwork_1.1.3       CellChat_2.1.2       
#> [4] Biobase_2.60.0        BiocGenerics_0.46.0   ggplot2_3.4.3        
#> [7] igraph_1.5.1          dplyr_1.1.3          
#> 
#> loaded via a namespace (and not attached):
#>   [1] RcppAnnoy_0.0.21       splines_4.3.1          later_1.3.1           
#>   [4] tibble_3.2.1           polyclip_1.10-4        ggnetwork_0.5.12      
#>   [7] fastDummies_1.7.3      lifecycle_1.0.3        rstatix_0.7.2.999     
#>  [10] doParallel_1.0.17      rprojroot_2.0.3        globals_0.16.2        
#>  [13] lattice_0.21-8         MASS_7.3-60            backports_1.4.1       
#>  [16] magrittr_2.0.3         plotly_4.10.2          sass_0.4.7            
#>  [19] rmarkdown_2.24         jquerylib_0.1.4        yaml_2.3.7            
#>  [22] httpuv_1.6.11          Seurat_5.0.1           sctransform_0.4.1     
#>  [25] NMF_0.26               spam_2.9-1             spatstat.sparse_3.0-2 
#>  [28] sp_2.1-0               reticulate_1.31        cowplot_1.1.1         
#>  [31] pbapply_1.7-2          RColorBrewer_1.1-3     abind_1.4-5           
#>  [34] Rtsne_0.16             purrr_1.0.2            presto_1.0.0          
#>  [37] circlize_0.4.16        IRanges_2.34.1         S4Vectors_0.38.1      
#>  [40] ggrepel_0.9.3          irlba_2.3.5.1          spatstat.utils_3.0-3  
#>  [43] listenv_0.9.0          goftest_1.2-3          RSpectra_0.16-1       
#>  [46] spatstat.random_3.1-5  fitdistrplus_1.1-11    parallelly_1.36.0     
#>  [49] svglite_2.1.1          leiden_0.4.3           codetools_0.2-19      
#>  [52] tidyselect_1.2.0       shape_1.4.6            farver_2.1.1          
#>  [55] spatstat.explore_3.2-1 matrixStats_1.0.0      stats4_4.3.1          
#>  [58] jsonlite_1.8.7         GetoptLong_1.0.5       BiocNeighbors_1.18.0  
#>  [61] ellipsis_0.3.2         progressr_0.14.0       ggridges_0.5.4        
#>  [64] ggalluvial_0.12.5      survival_3.5-7         iterators_1.0.14      
#>  [67] systemfonts_1.0.4      foreach_1.5.2          tools_4.3.1           
#>  [70] ragg_1.2.5             sna_2.7-1              ica_1.0-3             
#>  [73] Rcpp_1.0.11            glue_1.6.2             gridExtra_2.3         
#>  [76] xfun_0.40              here_1.0.1             withr_2.5.0           
#>  [79] BiocManager_1.30.22    fastmap_1.1.1          fansi_1.0.4           
#>  [82] digest_0.6.33          R6_2.5.1               mime_0.12             
#>  [85] textshaping_0.3.6      colorspace_2.1-0       scattermore_1.2       
#>  [88] tensor_1.5             spatstat.data_3.0-1    utf8_1.2.3            
#>  [91] tidyr_1.3.0            generics_0.1.3         data.table_1.14.9     
#>  [94] FNN_1.1.3.2            httr_1.4.7             htmlwidgets_1.6.2     
#>  [97] uwot_0.1.16            pkgconfig_2.0.3        gtable_0.3.4          
#> [100] registry_0.5-1         lmtest_0.9-40          htmltools_0.5.6       
#> [103] carData_3.0-5          dotCall64_1.0-2        clue_0.3-64           
#> [106] SeuratObject_5.0.1     scales_1.2.1           tidyverse_2.0.0       
#> [109] png_0.1-8              wordcloud_2.6          knitr_1.43            
#> [112] rstudioapi_0.15.0      reshape2_1.4.4         rjson_0.2.21          
#> [115] nlme_3.1-163           coda_0.19-4            statnet.common_4.9.0  
#> [118] cachem_1.0.8           zoo_1.8-12             GlobalOptions_0.1.2   
#> [121] stringr_1.5.0          KernSmooth_2.23-22     parallel_4.3.1        
#> [124] miniUI_0.1.1.1         pillar_1.9.0           vctrs_0.6.3           
#> [127] RANN_2.6.1             promises_1.2.1         ggpubr_0.6.0          
#> [130] car_3.1-2              xtable_1.8-4           cluster_2.1.4         
#> [133] evaluate_0.21          magick_2.8.1           cli_3.6.1             
#> [136] compiler_4.3.1         rlang_1.1.1            crayon_1.5.2          
#> [139] rngtools_1.5.2         future.apply_1.11.0    ggsignif_0.6.4        
#> [142] labeling_0.4.3         plyr_1.8.8             stringi_1.7.12        
#> [145] deldir_1.0-9           viridisLite_0.4.2      network_1.18.1        
#> [148] gridBase_0.4-7         BiocParallel_1.34.2    munsell_0.5.0         
#> [151] lazyeval_0.2.2         spatstat.geom_3.2-4    Matrix_1.6-5          
#> [154] RcppHNSW_0.5.0         future_1.33.0          shiny_1.7.5           
#> [157] highr_0.10             ROCR_1.0-11            broom_1.0.5           
#> [160] bslib_0.5.1