cellchat.single-analysis
Ying
2023-07-13
CellChat single analysis
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## components## [1] "pre"## [1] "PD"## [1] "/research/bsi/projects/PI/tertiary/Lin_Yi_yxl07/s215605.10xsc_human_lym_extend/mm_map2ref_bmref/output_mm_all//data.rds"## [1] 113236## [1] 30864## [1] 113236Part I: Data input & processing and initialization of CellChat object
CellChat requires two user inputs: one is the gene expression data of cells, and the other is either user assigned cell labels (i.e., label-based mode) or a low-dimensional representation of the single-cell data (i.e., label-free mode). For the latter, CellChat automatically groups cells by building a shared neighbor graph based on the cell-cell distance in the low-dimensional space or the pseudotemporal trajectory space.
Load data For the gene expression data matrix, genes should be in rows with rownames and cells in columns with colnames. Normalized data (e.g., library-size normalization and then log-transformed with a pseudocount of 1) is required as input for CellChat analysis. If user provides count data, we provide a normalizeData function to account for library size and then do log-transformed. For the cell group information, a dataframe with rownames is required as input for CellChat.
## [1] "Create a CellChat object from a data matrix"
## Set cell identities for the new CellChat object
## The cell groups used for CellChat analysis are B CD4 T CD8 T DC HSPC Mono NK other other T## [1] "B" "CD4 T" "CD8 T" "DC" "HSPC" "Mono" "NK"
## [8] "other" "other T"Set the ligand-receptor interaction database
Our database CellChatDB is a manually curated database of literature-supported ligand-receptor interactions in both human and mouse. CellChatDB in mouse contains 2,021 validated molecular interactions, including 60% of secrete autocrine/paracrine signaling interactions, 21% of extracellular matrix (ECM)-receptor interactions and 19% of cell-cell contact interactions. CellChatDB in human contains 1,939 validated molecular interactions, including 61.8% of paracrine/autocrine signaling interactions, 21.7% of extracellular matrix (ECM)-receptor interactions and 16.5% of cell-cell contact interactions.
Users can update CellChatDB by adding their own curated ligand-receptor pairs.Please check our tutorial on how to do it.
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## $ interaction_name <chr> "TGFB1_TGFBR1_TGFBR2", "TGFB2_TGFBR1_TGFBR2", "TGFB…
## $ pathway_name <chr> "TGFb", "TGFb", "TGFb", "TGFb", "TGFb", "TGFb", "TG…
## $ ligand <chr> "TGFB1", "TGFB2", "TGFB3", "TGFB1", "TGFB1", "TGFB2…
## $ receptor <chr> "TGFbR1_R2", "TGFbR1_R2", "TGFbR1_R2", "ACVR1B_TGFb…
## $ agonist <chr> "TGFb agonist", "TGFb agonist", "TGFb agonist", "TG…
## $ antagonist <chr> "TGFb antagonist", "TGFb antagonist", "TGFb antagon…
## $ co_A_receptor <chr> "", "", "", "", "", "", "", "", "", "", "", "", "",…
## $ co_I_receptor <chr> "TGFb inhibition receptor", "TGFb inhibition recept…
## $ evidence <chr> "KEGG: hsa04350", "KEGG: hsa04350", "KEGG: hsa04350…
## $ annotation <chr> "Secreted Signaling", "Secreted Signaling", "Secret…
## $ interaction_name_2 <chr> "TGFB1 - (TGFBR1+TGFBR2)", "TGFB2 - (TGFBR1+TGFBR2)…Preprocessing the expression data for cell-cell communication analysis
To infer the cell state-specific communications, we identify over-expressed ligands or receptors in one cell group and then identify over-expressed ligand-receptor interactions if either ligand or receptor is over-expressed.
Project gene expression data onto protein-protein interaction (PPI) network. Specifically, a diffusion process is used to smooth genes’ expression values based on their neighbors’ defined in a high-confidence experimentally validated protein-protein network. This function is useful when analyzing single-cell data with shallow sequencing depth because the projection reduces the dropout effects of signaling genes, in particular for possible zero expression of subunits of ligands/receptors. One might be concerned about the possible artifact introduced by this diffusion process, however, it will only introduce very weak communications. USERS can also skip this step and set raw.use = TRUE in the function computeCommunProb().
subset the expression data of signaling genes for saving computation cost
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## ?parallelly::supportsMulticorePart II: Inference of cell-cell communication network
CellChat infers the biologically significant cell-cell communication by assigning each interaction with a probability value and peforming a permutation test. CellChat models the probability of cell-cell communication by integrating gene expression with prior known knowledge of the interactions between signaling ligands, receptors and their cofactors using the law of mass action.
The number of inferred ligand-receptor pairs clearly depends on the method for calculating the average gene expression per cell group. By default, CellChat uses a statistically robust mean method called ‘trimean’, which produces fewer interactions than other methods. However, we find that CellChat performs well at predicting stronger interactions, which is very helpful for narrowing down on interactions for further experimental validations. In computeCommunProb, we provide an option for using other methods, such as 5% and 10% truncated mean, to calculating the average gene expression. Of note, ‘trimean’ approximates 25% truncated mean, implying that the average gene expression is zero if the percent of expressed cells in one group is less than 25%. To use 10% truncated mean, USER can set type = “truncatedMean” and trim = 0.1. The function computeAveExpr can help to check the average expression of signaling genes of interest, e.g, computeAveExpr(cellchat, features = c(“CXCL12”,“CXCR4”), type = “truncatedMean”, trim = 0.1).
When analyzing unsorted single-cell transcriptomes, under the assumption that abundant cell populations tend to send collectively stronger signals than the rare cell populations, CellChat can also consider the effect of cell proportion in each cell group in the probability calculation. USER can set population.size = TRUE.
Compute the communication probability and infer cellular communication network If well-known signaling pathways in the studied biological process are not predicted, USER can try truncatedMean to change the method for calculating the average gene expression per cell group.
triMean is used for calculating the average gene expression per cell group.
Filter out the cell-cell communication if there are only few number of cells in certain cell groups. All the inferred cell-cell communications at the level of ligands/receptors. Set slot.name = “netP” to access the the inferred communications at the level of signaling pathways.
Infer the cell-cell communication at a signaling pathway level CellChat computes the communication probability on signaling pathway level by summarizing the communication probabilities of all ligands-receptors interactions associated with each signaling pathway.
NB: The inferred intercellular communication network of each ligand-receptor pair and each signaling pathway is stored in the slot ‘net’ and ‘netP’, respectively.
Calculate the aggregated cell-cell communication network We can calculate the aggregated cell-cell communication network by counting the number of links or summarizing the communication probability. USER can also calculate the aggregated network among a subset of cell groups by setting sources.use and targets.use.
## triMean is used for calculating the average gene expression per cell group.
## [1] ">>> Run CellChat on sc/snRNA-seq data <<< [2023-07-16 10:04:13]"
## [1] ">>> CellChat inference is done. Parameter values are stored in `object@options$parameter` <<< [2023-07-16 10:50:33]"
Due to the complicated cell-cell communication network, we can examine the signaling sent from each cell group. Here we also control the parameter edge.weight.max so that we can compare edge weights between differet networks.
Part III: Visualization of cell-cell communication network
Upon infering the cell-cell communication network, CellChat provides various functionality for further data exploration, analysis, and visualization.
It provides several ways for visualizing cell-cell communication network, including hierarchical plot, circle plot, Chord diagram, and bubble plot.
It provides an easy-to-use tool for extracting and visualizing high-order information of the inferred networks. For example, it allows ready prediction of major signaling inputs and outputs for cell populations and how these populations and signals coordinate together for functions.
It can quantitatively characterize and compare the inferred cell-cell communication networks using an integrated approach by combining social network analysis, pattern recognition, and manifold learning approaches.
Access all the signaling pathways showing significant communications
Save pathway
## [1] "B" "CD4 T" "CD8 T" "DC" "HSPC" "Mono" "NK"
## [8] "other" "other T"Visualize cell-cell communication mediated by multiple ligand-receptors or signaling pathways
Bubble plot We can also show all the significant interactions (L-R pairs) from some cell groups to other cell groups using netVisual_bubble.
## Comparing communications on a single object
show all the significant signaling pathways from some cell groups (defined by ‘sources.use’) to other cell groups (defined by ‘targets.use’)
Plot the signaling gene expression distribution using violin/dot plot We can plot the gene expression distribution of signaling genes related to L-R pairs or signaling pathway using a Seurat wrapper function plotGeneExpression.
## There is no significant communication of CXCL## Rasterizing points since number of points exceeds 100,000.
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By default, plotGeneExpression only shows the expression of signaling genes related to the inferred significant communications. USERS can show the expression of all signaling genes related to one signaling pathway by
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Part IV: Systems analysis of cell-cell communication network
To facilitate the interpretation of the complex intercellular communication networks, CellChat quantitively measures networks through methods abstracted from graph theory, pattern recognition and manifold learning.
It can determine major signaling sources and targets as well as mediators and influencers within a given signaling network using centrality measures from network analysis
It can predict key incoming and outgoing signals for specific cell types as well as coordinated responses among different cell types by leveraging pattern recognition approaches.
It can group signaling pathways by defining similarity measures and performing manifold learning from both functional and topological perspectives.
It can delineate conserved and context-specific signaling pathways by joint manifold learning of multiple networks.
Identify signaling roles (e.g., dominant senders, receivers) of cell groups as well as the major contributing signaling CellChat allows ready identification of dominant senders, receivers, mediators and influencers in the intercellular communication network by computing several network centrality measures for each cell group. Specifically, we used measures in weighted-directed networks, including out-degree, in-degree, flow betweenesss and information centrality, to respectively identify dominant senders, receivers, mediators and influencers for the intercellular communications. In a weighteddirected network with the weights as the computed communication probabilities, the outdegree, computed as the sum of communication probabilities of the outgoing signaling from a cell group, and the in-degree, computed as the sum of the communication probabilities of the incoming signaling to a cell group, can be used to identify the dominant cell senders and receivers of signaling networks, respectively. For the definition of flow betweenness and information centrality, please check our paper and related reference.
Compute and visualize the network centrality scores
# 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)Visualize the dominant senders (sources) and receivers (targets) in a 2D space We also provide another intutive way to visualize the dominant senders (sources) and receivers (targets) in a 2D space using scatter plot.
# 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
gg1
Identify signals contributing most to outgoing or incoming signaling of certain cell groups We can also answer the question on which signals contributing 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"))
#htIdentify global communication patterns to explore how multiple cell types and signaling pathways coordinate together In addition to exploring detailed communications for individual pathways, an important question is how multiple cell groups and signaling pathways coordinate to function. CellChat employs a pattern recognition method to identify the global communication patterns.
As the number of patterns increases, there might be redundant patterns, making it difficult to interpret the communication patterns. We chose five patterns as default. Generally, it is biologically meaningful with the number of patterns greater than 2. In addition, we also provide a function selectK to infer the number of patterns, which is based on two metrics that have been implemented in the NMF R package, including Cophenetic and Silhouette. Both metrics measure the stability for a particular number of patterns based on a hierarchical clustering of the consensus matrix. For a range of the number of patterns, a suitable number of patterns is the one at which Cophenetic and Silhouette values begin to drop suddenly.
Identify and visualize outgoing communication pattern of secreting cells Outgoing patterns reveal how the sender cells (i.e. cells as signal source) coordinate with each other as well as how they coordinate with certain signaling pathways to drive communication.
To intuitively show the associations of latent patterns with cell groups and ligand-receptor pairs or signaling pathways, we used river (alluvial) plots. We first normalized each row of W and each column of H to be [0,1], and then set the elements in W and H to be zero if they are less than 0.5. Such thresholding allows to uncover the most enriched cell groups and signaling pathways associated with each inferred pattern, that is, each cell group or signaling pathway is associated with only one inferred pattern. These thresholded matrices W and H are used as inputs for creating alluvial plots.
To directly relate cell groups with their enriched signaling pathways, we set the elements in W and H to be zero if they are less than 1/R where R is the number of latent patterns. By using a less strict threshold, more enriched signaling pathways associated each cell group might be obtained. Using a contribution score of each cell group to each signaling pathway computed by multiplying W by H, we constructed a dot plot in which the dot size is proportion to the contribution score to show association between cell group and their enriched signaling pathways. USERS can also decrease the parameter cutoff to show more enriched signaling pathways associated each cell group.
Load required package for the communication pattern analysis
library(NMF)## Loading required package: registry## Loading required package: rngtools## Loading required package: cluster## NMF - BioConductor layer [OK] | Shared memory capabilities [OK] | Cores 2/2##
## Attaching package: 'NMF'## The following objects are masked from 'package:igraph':
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## algorithm, comparelibrary(ggalluvial)Here we run selectK to infer the number of patterns. Both Cophenetic and Silhouette values begin to drop suddenly when the number of outgoing patterns is 3.
selectK(cellchat, pattern = "outgoing")
nPatterns = 3
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
#> Please make sure you have load `library(ggalluvial)` when running this function
# dot plot
netAnalysis_dot(cellchat, pattern = "outgoing")
Identify and visualize incoming communication pattern of target cells Incoming patterns show how the target cells (i.e. cells as signal receivers) coordinate with each other as well as how they coordinate with certain signaling pathways to respond to incoming signals.
selectK(cellchat, pattern = "incoming")
#Cophenetic values begin to drop when the number of incoming patterns is 4.
nPatterns = 4
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
#> 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 Further, CellChat is able to quantify the similarity between all significant signaling pathways and then group them based on their cellular communication network similarity. Grouping can be done either based on the functional or structural similarity.
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. The functional similarity analysis requires the same cell population composition between two datasets.
Structural similarity: A structural similarity was used to compare their signaling network structure, without considering the similarity of senders and receivers.
Identify signaling groups based on their functional similarity (skipped, umap-learn issue)
#cellchat <- computeNetSimilarity(cellchat, type = "functional")
#cellchat <- netEmbedding(cellchat, type = "functional")
#> Manifold learning of the signaling networks for a single dataset
#cellchat <- netClustering(cellchat, type = "functional")
#> Classification learning of the signaling networks for a single dataset
# Visualization in 2D-space
#netVisual_embedding(cellchat, type = "functional", label.size = 3.5)
# netVisual_embeddingZoomIn(cellchat, type = "functional", nCol = 2)
#Identify signaling groups based on structure similarity
#cellchat <- computeNetSimilarity(cellchat, type = "structural")
#cellchat <- netEmbedding(cellchat, type = "structural")
#> Manifold learning of the signaling networks for a single dataset
#cellchat <- netClustering(cellchat, type = "structural")
#> Classification learning of the signaling networks for a single dataset
# Visualization in 2D-space
#netVisual_embedding(cellchat, type = "structural", label.size = 3.5)
#netVisual_embeddingZoomIn(cellchat, type = "structural", nCol = 2)Part V: Save the CellChat object
saveRDS(cellchat, file = paste0(“cellchat_”,clinical_type,“-”,grp_name,“r4.2.rds”) sessionInfo()