hello，各位好，之前呢，我们分享了Cellchat的文章内容，方法，以及单样本分析案例，文章在10X单细胞（10X空间转录组）通讯分析之CellChat，这次我们来分享一下Cellchat分析多样本通讯差异的分析过程。
Load the required libraries
library(CellChat)
library(patchwork)
Load CellChat object of each dataset and then merge together
cellchat.NL <- readRDS(url("https://ndownloader.figshare.com/files/25954199"))
cellchat.LS <- readRDS(url("https://ndownloader.figshare.com/files/25956518"))
object.list <- list(NL = cellchat.NL, LS = cellchat.LS)
cellchat <- mergeCellChat(object.list, add.names = names(object.list))
这个地方我看了一下，每个样本都是单独分析出来的结果，其实对于细胞通讯一直有一个争议，那就是先整合后再分析呢，还是单样本分析完了进行比较，不知道大家怎么看，Cellchat这里是建议分开做
Part I: Predict general principles of cell-cell communication
CellChat从全局出发，以预测细胞间通信的一般原理。 比较多种生物学条件之间的细胞间通讯时，它可以回答以下生物学问题：
- 细胞通讯是否增强
- 细胞通讯的相互联系是否显著改变
- How the major sources and targets change from one condition to another
这个地方很值得深入探讨。
Compare the total number of interactions and interaction strength
首先是上述第一个问题，CellChat compares the the total number of interactions and interaction strength of the inferred cell-cell communication networks from different biological conditions.
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.(配受体数量和通讯强度的差异)
看数量和强度变化
The differential number of interactions or interaction strength in the cell-cell communication network between two datasets can be visualized using circle plot, where red (or blue) colored edges represent increased (or decreased) signaling in the second dataset compared to the first one.（红色加强，绿色降低）
par(mfrow = c(1,2), xpd=TRUE)
netVisual_diffInteraction(cellchat, weight.scale = T)
netVisual_diffInteraction(cellchat, weight.scale = T, measure = "weight")
示例的数量差异变化很明显。
热图展示
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
如果我们不止两个样本呢？？
The differential network analysis only works for pairwise datasets. If there are more datasets for comparison, we can directly show the number of interactions or interaction strength between any two cell populations in each dataset.
为了更好地控制不同数据集上的推断网络的节点大小和边缘权重，我们计算了每个单元格组的最大单元数以及所有数据集之间的最大交互数（或交互权重）。
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$count, weight.scale = T, label.edge= F, edge.weight.max = weight.max[2], edge.width.max = 12, title.name = paste0("Number of interactions - ", names(object.list)[i]))
}
这种情况相对很少，因为即使多个样本，我们也会两两比较，总结出结果
Differential number of interactions or interaction strength among different cell types
To simplify the complicated network and gain insights into the cell-cell communication at the cell type level, we can aggregate the cell-cell communication based on the defined cell groups. Here we categorize the cell populations into three cell types, and then re-merge the list of CellChat object.（细胞类型水平上分析）
group.cellType <- c(rep("FIB", 4), rep("DC", 4), rep("TC", 4))
group.cellType <- factor(group.cellType, levels = c("FIB", "DC", "TC"))
object.list <- lapply(object.list, function(x) {mergeInteractions(x, group.cellType)})
cellchat <- mergeCellChat(object.list, add.names = names(object.list))
#> Merge the following slots: 'data.signaling','net', 'netP','meta', 'idents', 'var.features' , 'DB', and 'LR'.
weight.max <- getMaxWeight(object.list, slot.name = c("idents", "net", "net"), attribute = c("idents","count", "count.merged"))
par(mfrow = c(1,2), xpd=TRUE)
for (i in 1:length(object.list)) {
netVisual_circle(object.list[[i]]@net$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]))
}
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 2D space
比较二维空间中的传出和传入交互强度，可以轻松识别出具有不同数据集之间发送或接收信号的显着变化的细胞群体。
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)
Part II: Identify the conserved and context-specific signaling pathways
然后，CellChat可以基于它们在多种生物学条件下的细胞间通信网络，识别具有更大（或更少）差异的信号网络，信号组以及保守的和context-specific 的信号通路。
根据功能/结构相似性，识别差异较大（或较小）的通讯网络以及通讯group
CellChat performs joint manifold learning and classification of the inferred communication networks based on their functional and topological similarity. NB: Such analysis is applicable to more than two datasets.
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.(这个地方还是要注意一下，我们来看一下功能相似性的定义)
我们重点要记住这个：d based on the overlap of communications
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.
这里要注意的是A structural similarity was used to compare their signaling network structure, without considering the similarity of senders and receivers, using a previously developed measure for structural topological differences（算法在单样本时候已经分享过）
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.（这一部分单样本已经介绍过了）。
Identify signaling groups based on their functional similarity
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
Identify signaling groups based on structure similarity
cellchat <- computeNetSimilarityPairwise(cellchat, type = "structural")
#> Compute signaling network similarity for datasets 1 2
cellchat <- netEmbedding(cellchat, type = "structural")
#> Manifold learning of the signaling networks for datasets 1 2
cellchat <- netClustering(cellchat, type = "structural")
#> Classification learning of the signaling networks for datasets 1 2
# Visualization in 2D-space
netVisual_embeddingPairwise(cellchat, type = "structural", label.size = 3.5)
#> 2D visualization of signaling networks from datasets 1 2
Compute and visualize the pathway distance in the learned joint manifold
We can identify the signaling networks with larger (or less) difference based on their Euclidean distance in the shared two-dimensions space（我们可以在共享二维空间中基于它们的欧几里得距离来识别具有较大（或较小）差异的信令网络）. Larger distance implies larger difference of the communication networks between two datasets in terms of either functional or structure similarity. NB: 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 defining comparison in the function rankSimilarity.
rankSimilarity(cellchat, type = "functional")
#> Compute the distance of signaling networks between datasets 1 2
Identify and visualize the conserved and context-specific signaling pathways
By comparing the information flow/interaction strengh of each signaling pathway, we can identify signaling pathways, (i) turn off, (ii) decrease, (iii) turn on or (iv) increase, by change their information flow at one condition as compared to another condition.（这才是我们细胞通讯分析的重点，高低开合四个方向）
Compare the overall information flow of each signaling pathway
We can identify the conserved and context-specific signaling pathways by simply comparing the information flow for each signaling pathway（没怎么变的通讯）, 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 graph 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. The top signaling pathways colored red are enriched in NL skin, and these colored green were enriched in the LS skin.
gg1 <- rankNet(cellchat, mode = "comparison", stacked = T, do.stat = TRUE)
gg2 <- rankNet(cellchat, mode = "comparison", stacked = F, do.stat = TRUE)
gg1 + gg2
Compare outgoing (or incoming) signaling associated with each cell population
The above analysis summarize the information from the outgoing and incoming signaling together. We 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. NB: rankNet also shows the comparison of overall signaling, but it does not show the signaling strength in specific cell populations.
library(ComplexHeatmap)
#> Loading required package: grid
#> ========================================
#> ComplexHeatmap version 2.7.1.1010
#> 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:
#> Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional
#> genomic data. Bioinformatics 2016.
#>
#> 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"))
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"))
（功能差异的热图展示还是很直观的）
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 upgulated and down-regulated signaling ligand-receptor pairs（上调和下调的信号通路，一般我们需要关注上调的，但是下调的也非常重要）
We can compare the communication probabilities mediated by ligand-receptor pairs from some cell groups to other cell groups. This can be done by setting comparison in the function netVisual_bubble.
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, we can identify the upgulated (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 one dataset compared to the other dataset.
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.
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 gene expression analysis. 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. Such analysis can be done as follows.
# 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 = pos.dataset
# perform differential expression analysis
cellchat <- identifyOverExpressedGenes(cellchat, group.dataset = "datasets", pos.dataset = pos.dataset, features.name = features.name, only.pos = FALSE, thresh.pc = 0.1, thresh.fc = 0.1, thresh.p = 1)
#> 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)
# extract the ligand-receptor pairs with upregulated ligands in LS
net.up <- subsetCommunication(cellchat, net = net, datasets = "LS",ligand.logFC = 0.2, receptor.logFC = NULL)
# extract the ligand-receptor pairs with upregulated ligands and upregulated recetptors in NL, i.e.,downregulated in LS
net.down <- subsetCommunication(cellchat, net = net, datasets = "NL",ligand.logFC = -0.1, receptor.logFC = -0.1)
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)
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
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]))
#> Note: The first link end is drawn out of sector 'MIF'.
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]))
Part IV: Visually compare cell-cell communication using Hierarchy plot, Circle plot or Chord diagram（一些个性化的展示了）
Similar to the CellChat analysis of individual dataset, we can visualize the cell-cell communication network using Hierarchy plot, Circle plot or Chord diagram.
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]))
}
#> Note: The first link end is drawn out of sector 'Inflam. FIB'.
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
#> Note: The first link end is drawn out of sector 'Inflam. FIB'.
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)
}
#> Note: The second link end is drawn out of sector ' '.
#> Note: The first link end is drawn out of sector 'MIF'.
#> Note: The second link end is drawn out of sector ' '.
#> Note: The first link end is drawn out of sector 'CXCL '.
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（简单的展示）
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)
#> 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.
相当不错，大家多多尝试分析
生活很好，有你更好