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Package: CellChat 2.2.0.9001

Suoqin Jin

CellChat: Inference and analysis of cell-cell communication from single-cell and spatially resolved transcriptomics data

an open source R tool that infers, visualizes and analyzes the cell-cell communication networks from scRNA-seq and spatially resolved transcriptomics data.

Authors:Suoqin Jin

# Install 'CellChat' in R:

install.packages('CellChat', repos = c('https://blaserlab.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jinworks/cellchat/issues

Uses libs:

c++– GNU Standard C++ Library v3

Datasets:

CellChatDB.human - Ligand-receptor interactions in CellChat database for human
CellChatDB.mouse - Ligand-receptor interactions in CellChat database for mouse
CellChatDB.zebrafish - Ligand-receptor interactions in CellChat database for Zebrafish
PPI.human - Human Protein-Protein interactions
PPI.mouse - Mouse Protein-Protein interactions

On CRAN:

cell-cell-communication cell-cell-interaction microenvironment single-cell-analysis spatial-transcriptomics cpp

8.36 score 676 stars 3.4k scripts 109 exports 174 dependencies

Last updated from:75253cd0c9. Checks:13 NOTE, 2 OK. Indexed: no.

Target	Result	Time
linux-devel-arm64	NOTE	269
linux-devel-x86_64	NOTE	306
source / vignettes	OK	364
linux-release-arm64	NOTE	281
linux-release-x86_64	NOTE	227
macos-release-arm64	NOTE	300
macos-release-x86_64	NOTE	376
macos-oldrel-arm64	NOTE	171
macos-oldrel-x86_64	NOTE	281
windows-devel-arm64	NOTE	460
windows-devel-x86_64	NOTE	308
windows-release-arm64	NOTE	359
windows-release-x86_64	NOTE	260
windows-oldrel-x86_64	NOTE	293
wasm-release	OK	228

Exports:addMeta addReduction aggregateNet barPlot barplot_internal buildSNN CellChat_theme_opts checkGeneSymbol colorRamp3 compareInteractions computeAveExpr computeCellDistance computeCommunProb computeCommunProbPathway computeEigengap computeEnrichmentScore computeExpr_agonist computeExpr_antagonist computeExpr_complex computeExpr_coreceptor computeExpr_LR computeExprGroup_agonist computeExprGroup_antagonist computeLaplacian computeNetD_structure computeNetSimilarity computeNetSimilarityPairwise computeRegionDistance createCellChat dotPlot extractEnrichedLR extractGene extractGeneSubset extractGeneSubsetFromPair extractLRfromGenes filterCommunication findEnrichedSignaling geometricMean getMaxWeight ggPalette identifyCommunicationPatterns identifyEnrichedInteractions identifyOverExpressedGenes identifyOverExpressedInteractions identifyOverExpressedLigandReceptor liftCellChat mergeCellChat mergeInteractions netAnalysis_computeCentrality netAnalysis_contribution netAnalysis_diff_signalingRole_scatter netAnalysis_dot netAnalysis_river netAnalysis_signalingChanges_scatter netAnalysis_signalingRole_heatmap netAnalysis_signalingRole_network netAnalysis_signalingRole_scatter netClustering netEmbedding netMappingDEG netVisual netVisual_aggregate netVisual_barplot netVisual_bubble netVisual_chord_cell netVisual_chord_cell_internal netVisual_chord_gene netVisual_circle netVisual_diffInteraction netVisual_embedding netVisual_embeddingPairwise netVisual_embeddingPairwiseZoomIn netVisual_embeddingZoomIn netVisual_heatmap netVisual_hierarchy1 netVisual_hierarchy2 netVisual_individual netVisual_spatial normalizeData pieChart plotGeneExpression preProcMultiomics rankNet rankNetPairwise rankSimilarity runCellChatApp runPCA runUMAP scaleData scaleMat scPalette searchPair selectK setIdent showDatabaseCategory sketchData smoothData spatialDimPlot spatialFeaturePlot StackedVlnPlot subsetCellChat subsetCommunication subsetData subsetDB triMean updateCCC_score updateCellChat updateCellChatDB updateClusterLabels

Dependencies:abind askpass assorthead backports base64enc beachmat Biobase BiocGenerics BiocManager BiocNeighbors boot broom bslib cachem car carData circlize cli clue cluster coda codetools collapse colorspace commonmark ComplexHeatmap corrplot cowplot cpp11 crayon crosstalk curl data.table DelayedArray Deriv digest doBy doParallel dplyr evaluate farver fastmap FNN fontawesome foreach forecast Formula fracdiff fs future future.apply generics GetoptLong ggalluvial ggnetwork ggplot2 ggpubr ggrepel ggsci ggsignif GlobalOptions globals glue gridBase gridExtra gtable here highr htmltools htmlwidgets httpuv httr igraph IRanges irlba isoband iterators jquerylib jsonlite knitr labeling later lattice lazyeval lifecycle listenv lme4 lmtest magrittr MASS Matrix MatrixGenerics MatrixModels matrixStats memoise mgcv mime minqa modelr network nlme nloptr NMF nnet numDeriv openssl otel parallelly patchwork pbapply pbkrtest pillar pkgconfig plotly plyr png polynom promises purrr quantreg R6 rappdirs rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen RcppTOML Rdpack reformulas registry reshape2 reticulate rjson rlang rmarkdown rngtools rprojroot RSpectra rstatix S4Arrays S4Vectors S7 sass scales shape shiny sna sourcetools SparseArray SparseM statnet.common stringi stringr survival svglite sys systemfonts textshaping tibble tidyr tidyselect timeDate tinytex urca utf8 vctrs viridisLite withr xfun xtable XVector yaml zoo

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Add the cell information into meta slot	addMeta
Add a reduced space of the data into CellChat object	addReduction
Calculate the aggregated network by counting the number of links or summarizing the communication probability	aggregateNet
compute alpha centrality	alpha_centrality
The CellChat Class	AnyMatrix-class
Bar plot for average gene expression	barPlot
Bar plot for dataframe	barplot_internal
Build SNN matrix	buildSNN
ggplot theme in CellChat	CellChat_theme_opts
The key slots used in the CellChat object are described below.	CellChat CellChat-class
Ligand-receptor interactions in CellChat database for human	CellChatDB.human
Ligand-receptor interactions in CellChat database for mouse	CellChatDB.mouse
Ligand-receptor interactions in CellChat database for Zebrafish	CellChatDB.zebrafish
check the official Gene Symbol	checkGeneSymbol
Color interpolation	colorRamp3
Comparing the number of inferred communication links between different datasets	compareInteractions
Compute averaged expression values for each cell group	computeAveExpr
Compute cell-cell distance based on the spatial coordinates	computeCellDistance
Compute Centrality measures for a signaling network	computeCentralityLocal
Compute the communication probability/strength between any interacting cell groups	computeCommunProb
Compute the communication probability on signaling pathway level by summarizing all related ligands/receptors	computeCommunProbPathway
Compute the eigengap of a given matrix for inferring the number of clusters	computeEigengap
Compute and visualize the enrichment score of ligand-receptor pairs in one condition compared to another condition	computeEnrichmentScore
Modeling the effect of agonist on the ligand-receptor interaction	computeExpr_agonist
Modeling the effect of antagonist on the ligand-receptor interaction	computeExpr_antagonist
Compute the expression of complex in individual cells using geometric mean	computeExpr_complex
Modeling the effect of coreceptor on the ligand-receptor interaction	computeExpr_coreceptor
Compute the expression of ligands or receptors using geometric mean	computeExpr_LR
Modeling the effect of agonist on the ligand-receptor interaction	computeExprGroup_agonist
Modeling the effect of antagonist on the ligand-receptor interaction	computeExprGroup_antagonist
Compute eigenvalues of associated Laplacian matrix of a given matrix	computeLaplacian
Compute the structural distance between two signaling networks	computeNetD_structure
Compute signaling network similarity for any pair of signaling networks	computeNetSimilarity
Compute signaling network similarity for any pair of datasets	computeNetSimilarityPairwise
Compute the region distance based on the spatial locations of each splot/cell of the spatial transcriptomics	computeRegionDistance
Create a new CellChat object from a data matrix, Seurat or SingleCellExperiment object	createCellChat
Dot plot	dotPlot
compute the Shannon entropy	entropia
extract the max value of the y axis	extract_max
Identify all the significant interactions (L-R pairs) and related signaling genes for a given signaling pathway	extractEnrichedLR
Identify all the significant interactions (L-R pairs) and related signaling genes for a given signaling pathway	extractEnrichedLR_internal
Extract the genes involved in CellChatDB	extractGene
Extract the gene name	extractGeneSubset
Extract the signaling gene names from ligand-receptor pairs	extractGeneSubsetFromPair
Extract L-R pairs associated with a given gene set	extractLRfromGenes
Filter cell-cell communication if there are only few number of cells in certain cell groups or inconsistent cell-cell communication across samples	filterCommunication
Find the enriched signaling according to the genes (e.g.DEGs) and cell groups of interest	findEnrichedSignaling
Compute the geometric mean	geometricMean
Compute the maximum value of certain measures in the inferred cell-cell communication networks	getMaxWeight
Generate ggplot2 colors	ggPalette
Identification of major signals for specific cell groups and general communication patterns	identifyCommunicationPatterns
Identify all the significant interactions (L-R pairs) from some cell groups to other cell groups	identifyEnrichedInteractions
Identify over-expressed signaling genes associated with each cell group	identifyOverExpressedGenes
Identify over-expressed ligand-receptor interactions (pairs) within the used CellChatDB	identifyOverExpressedInteractions
Identify over-expressed ligands and (complex) receptors associated with each cell group	identifyOverExpressedLigandReceptor
Update a CellChat object by lifting up the cell groups to the same cell labels across all datasets	liftCellChat
Merge CellChat objects	mergeCellChat
Compute the number of interactions/interaction strength between cell types based on their associated cell subpopulations	mergeInteractions
modified vlnplot	modify_vlnplot
generate circle symbol	mycircle
Compute the network centrality scores allowing identification of dominant senders, receivers, mediators and influencers in all inferred communication networks	netAnalysis_computeCentrality
Compute and visualize the contribution of each ligand-receptor pair in the overall signaling pathways	netAnalysis_contribution
2D visualization of differential signaling roles (dominant senders (sources) or receivers (targets) ) of each cell group when comparing mutiple datasets	netAnalysis_diff_signalingRole_scatter
Dot plots showing the associations of latent patterns with cell groups and ligand-receptor pairs or signaling pathways	netAnalysis_dot
River plot showing the associations of latent patterns with cell groups and ligand-receptor pairs or signaling pathways	netAnalysis_river
2D visualization of differential outgoing and incoming signaling associated with one cell group	netAnalysis_signalingChanges_scatter
Heatmap showing the contribution of signals (signaling pathways or ligand-receptor pairs) to cell groups in terms of outgoing or incoming signaling	netAnalysis_signalingRole_heatmap
Heatmap showing the centrality scores/importance of cell groups as senders, receivers, mediators and influencers in a single intercellular communication network	netAnalysis_signalingRole_network
2D visualization of dominant senders (sources) and receivers (targets)	netAnalysis_signalingRole_scatter
Classification learning of the signaling networks	netClustering
Manifold learning of the signaling networks based on their similarity	netEmbedding
Mapping the differential expressed genes (DEG) information onto the inferred cell-cell communications	netMappingDEG
Visualize the inferred cell-cell communication network	netVisual
Visualize the inferred signaling network of signaling pathways by aggregating all L-R pairs	netVisual_aggregate
Visualization of (differential) number of interactions	netVisual_barplot
Show all the significant interactions (L-R pairs) from some cell groups to other cell groups	netVisual_bubble
Chord diagram for visualizing cell-cell communication for a signaling pathway	netVisual_chord_cell
Chord diagram for visualizing cell-cell communication from a weighted adjacency matrix or a data frame	netVisual_chord_cell_internal
Chord diagram for visualizing cell-cell communication for a set of ligands/receptors or signaling pathways	netVisual_chord_gene
Circle plot of cell-cell communication network	netVisual_circle
Circle plot showing differential cell-cell communication network between two datasets	netVisual_diffInteraction
2D visualization of the learned manifold of signaling networks	netVisual_embedding
2D visualization of the joint manifold learning of signaling networks from two datasets	netVisual_embeddingPairwise
Zoom into the 2D visualization of the joint manifold learning of signaling networks from two datasets	netVisual_embeddingPairwiseZoomIn
Zoom into the 2D visualization of the learned manifold learning of the signaling networks	netVisual_embeddingZoomIn
Visualization of network using heatmap	netVisual_heatmap
Hierarchy plot of cell-cell communications sending to cell groups in vertex.receiver	netVisual_hierarchy1
Hierarchy plot of cell-cell communication sending to cell groups not in vertex.receiver	netVisual_hierarchy2
Visualize the inferred signaling network of individual L-R pairs	netVisual_individual
Spatial plot of cell-cell communication network	netVisual_spatial
compute nnd	nnd
compute the node distance matrix	node_distance
Normalize data using a scaling factor	normalizeData
Plot pie chart	pieChart
A Seurat wrapper function for plotting gene expression using violin plot, dot plot or bar plot	plotGeneExpression
Human Protein-Protein interactions	PPI.human
Mouse Protein-Protein interactions	PPI.mouse
Preprocessing multi-omics data and preparing the L-R database	preProcMultiomics
Rank signaling networks based on the information flow or the number of interactions	rankNet
Rank ligand-receptor interactions for any pair of two cell groups	rankNetPairwise
Rank the similarity of the shared signaling pathways based on their joint manifold learning	rankSimilarity
Generate a Shiny App for interactive exploration of CellChat's outputs	runCellChatApp
Dimension reduction using PCA	runPCA
Run UMAP	runUMAP
Scale the data	scaleData
Scale a data matrix	scaleMat
Generate colors from a customed color palette	scPalette
Subset the ligand-receptor interactions for given specific signals in CellChatDB	searchPair
Select the number of the patterns for running `identifyCommunicationPatterns`	selectK
Set the default identity of cells	setIdent
show method for CellChat	show,CellChat-method
Show the description of CellChatDB databse	showDatabaseCategory
Downsampling single cell data using geometric sketching algorithm	sketchData
Smooth the gene expression data	smoothData
Visualize spatial cell groups	spatialDimPlot
A spatial feature plots	spatialFeaturePlot
Stacked Violin plot	StackedVlnPlot
Subset CellChat object using a portion of cells	subsetCellChat
Subset the inferred cell-cell communications of interest	subsetCommunication
Subset the inferred cell-cell communications of interest	subsetCommunication_internal
Subset the expression data of signaling genes for saving computation cost	subsetData
Subset CellChatDB databse by only including interactions of interest	subsetDB
Compute the average expression per cell group when the percent of expressing cells per cell group larger than a threshold	thresholdedMean
Compute the Tukey's trimean	triMean
Update the cell-cell communication array from a customized cell-cell-communication scores between different cell groups	updateCCC_score
Update a single CellChat object	updateCellChat
Update CellChatDB by integrating new L-R pairs from other resources or adding more information	updateCellChatDB
Update and re-order the cell group names after running `computeCommunProb`	updateClusterLabels