{ "_id": "6a117041acfb0bcc41cf6b08", "Package": "CellChat", "Type": "Package", "Title": "Inference and analysis of cell-cell communication from\nsingle-cell and spatially resolved transcriptomics data", "Version": "2.2.0.9001", "Author": "Suoqin Jin", "Maintainer": "Suoqin Jin ", "Description": "an open source R tool that infers, visualizes and analyzes\nthe cell-cell communication networks from scRNA-seq and\nspatially resolved transcriptomics data.", "License": "GPL-3", "Encoding": "UTF-8", "LazyData": "TRUE", "RoxygenNote": "7.3.1", "VignetteBuilder": "knitr", "Config/pak/sysreqs": "cmake libfontconfig1-dev libfreetype6-dev\nlibfribidi-dev libglpk-dev make libharfbuzz-dev libicu-dev\nlibpng-dev libuv1-dev libxml2-dev libssl-dev perl python3\nzlib1g-dev", "Repository": "https://blaserlab.r-universe.dev", "Date/Publication": "2026-03-04 12:08:33 UTC", "RemoteUrl": "https://github.com/jinworks/CellChat", "RemoteRef": "HEAD", "RemoteSha": "75253cd0c9e68410e6e721a6d3a0419a1d7e358f", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-05-23 09:07:22 UTC", "User": "root" }, "MD5sum": "2e61296e2630f3b0079f1b14a7001148", "_user": "blaserlab", "_type": "src", "_file": "CellChat_2.2.0.9001.tar.gz", "_fileid": "4d2980dba2a81faa5f245a3441b7f84e7dc5cf4b8b22c5b19b2f817638b19a71", "_filesize": 29654769, "_sha256": "4d2980dba2a81faa5f245a3441b7f84e7dc5cf4b8b22c5b19b2f817638b19a71", "_created": "2026-05-23T09:07:22.000Z", "_published": "2026-05-23T09:15:45.149Z", "_distro": "noble", "_jobs": [ { "job": 77511401893, "time": 303, "config": "linux-devel-arm64", "r": "4.7.0", "check": "NOTE", "artifact": "7175874534" }, { "job": 77511401863, "time": 335, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "NOTE", "artifact": "7175877669" }, { "job": 77511401902, "time": 317, "config": "linux-release-arm64", "r": "4.6.0", "check": "NOTE", "artifact": "7175876078" }, { "job": 77511401885, "time": 341, "config": "linux-release-x86_64", "r": "4.6.0", "check": "NOTE", "artifact": "7175878215" }, { "job": 77511401899, "time": 202, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "NOTE", "artifact": "7175865512" }, { "job": 77511401894, "time": 417, "config": "macos-oldrel-x86_64", "r": "4.5.3", "check": "NOTE", "artifact": "7175884760" }, { "job": 77511401897, "time": 163, "config": "macos-release-arm64", "r": "4.6.0", "check": "NOTE", "artifact": "7175861862" }, { "job": 77511401891, "time": 331, "config": "macos-release-x86_64", "r": "4.6.0", "check": "NOTE", "artifact": "7175877388" }, { "job": 77511141773, "time": 322, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7175848405" }, { "job": 77511401856, "time": 239, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7175868883" }, { "job": 77511401870, "time": 302, "config": "windows-devel", "r": "4.7.0", "check": "NOTE", "artifact": "7175874435" }, { "job": 77511401875, "time": 320, "config": "windows-oldrel", "r": "4.5.3", "check": "NOTE", "artifact": "7175876401" }, { "job": 77511401879, "time": 270, "config": "windows-release", "r": "4.6.0", "check": "NOTE", "artifact": "7175871818" } ], "_buildurl": "https://github.com/r-universe/blaserlab/actions/runs/26328780932", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/jinworks/CellChat", "_commit": { "id": "75253cd0c9e68410e6e721a6d3a0419a1d7e358f", "author": "Xiangzheng Cheng <145243521+Ethel-0@users.noreply.github.com>", "committer": "GitHub ", "message": "Update analysis.R\n\nModified the `rankNet` function to fix the issue of incorrect text colors when using the `signaling` or `pairLR` parameters.", "time": 1772626113 }, "_maintainer": { "name": "Suoqin Jin", "email": "sqjin@whu.edu.cn" }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 3.6.0", "role": "Depends" }, { "package": "dplyr", "role": "Depends" }, { "package": "igraph", "role": "Depends" }, { "package": "ggplot2", "role": "Depends" }, { "package": "Rcpp", "role": "LinkingTo" }, { "package": "RcppEigen", "role": "LinkingTo" }, { "package": "future", "role": "Imports" }, { "package": "future.apply", "role": "Imports" }, { "package": "pbapply", "role": "Imports" }, { "package": "irlba", "role": "Imports" }, { "package": "NMF", "version": ">= 0.23.0", "role": "Imports" }, { "package": "ggalluvial", "role": "Imports" }, { "package": "stringr", "role": "Imports" }, { "package": "svglite", "role": "Imports" }, { "package": "Matrix", "role": "Imports" }, { "package": "ggrepel", "role": "Imports" }, { "package": "circlize", "version": ">= 0.4.12", "role": "Imports" }, { "package": "RColorBrewer", "role": "Imports" }, { "package": "cowplot", "role": "Imports" }, { "package": "methods", "role": "Imports" }, { "package": "ComplexHeatmap", "role": "Imports" }, { "package": "RSpectra", "role": "Imports" }, { "package": "Rcpp", "role": "Imports" }, { "package": "reticulate", "role": "Imports" }, { "package": "scales", "role": "Imports" }, { "package": "sna", "role": "Imports" }, { "package": "reshape2", "role": "Imports" }, { "package": "FNN", "role": "Imports" }, { "package": "shape", "role": "Imports" }, { "package": "BiocGenerics", "role": "Imports" }, { "package": "magrittr", "role": "Imports" }, { "package": "patchwork", "role": "Imports" }, { "package": "colorspace", "role": "Imports" }, { "package": "plyr", "role": "Imports" }, { "package": "ggpubr", "role": "Imports" }, { "package": "ggnetwork", "role": "Imports" }, { "package": "BiocNeighbors", "role": "Imports" }, { "package": "plotly", "role": "Imports" }, { "package": "shiny", "role": "Imports" }, { "package": "bslib", "role": "Imports" }, { "package": "collapse", "role": "Imports" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" }, { "package": "roxygen2", "role": "Suggests" }, { "package": "Seurat", "version": ">= 4.0.0", "role": "Suggests" }, { "package": "SingleCellExperiment", "role": "Suggests" }, { "package": "SummarizedExperiment", "role": "Suggests" }, { "package": "purrr", "role": "Suggests" }, { "package": "uwot", "role": "Suggests" }, { "package": "gg.gap", "role": "Suggests" }, { "package": "wordcloud", "role": "Suggests" }, { "package": "bsicons", "role": "Suggests" }, { "package": "presto", "role": "Suggests" }, { "package": "tidyverse", "role": "Suggests" }, { "package": "tidyr", "role": "Suggests" }, { "package": "rlang", "role": "Suggests" } ], "_owner": "jinworks", "_selfowned": false, "_usedby": 1, "_updates": [ { "week": "2025-35", "n": 5 }, { "week": "2025-36", "n": 1 }, { "week": "2025-41", "n": 4 }, { "week": "2026-08", "n": 10 }, { "week": "2026-09", "n": 3 }, { "week": "2026-10", "n": 1 } ], "_tags": [], "_topics": [ "cell-cell-communication", "cell-cell-interaction", "microenvironment", "single-cell-analysis", "spatial-transcriptomics", "cpp" ], "_stars": 616, "_contributors": [ { "user": "sqjin", "count": 162, "uuid": 32399212 }, { "user": "809825706", "count": 1, "uuid": 46601791 }, { "user": "abhilashdasari", "count": 1, "uuid": 54527924 }, { "user": "giannimonaco", "count": 1, "uuid": 11880848 }, { "user": "jonversusjon", "count": 1, "uuid": 17264200 }, { "user": "ethel-0", "count": 1, "uuid": 145243521 } ], "_userbio": { "uuid": 62366303, "type": "user", "name": "Brad Blaser", "description": "Experimental Hematologist and Fish Fancier" }, "_downloads": { "count": 0, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/CellChat" }, "_devurl": "https://github.com/jinworks/cellchat", "_searchresults": 2408, "_rbuild": "4.6.0", "_assets": [ "extra/CellChat.html", "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "manual.pdf" ], "_cranurl": false, "_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" ], "_datasets": [ { "name": "CellChatDB.human", "title": "Ligand-receptor interactions in CellChat database for human", "object": "CellChatDB.human", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "CellChatDB.mouse", "title": "Ligand-receptor interactions in CellChat database for mouse", "object": "CellChatDB.mouse", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "CellChatDB.zebrafish", "title": "Ligand-receptor interactions in CellChat database for Zebrafish", "object": "CellChatDB.zebrafish", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "PPI.human", "title": "Human Protein-Protein interactions", "object": "PPI.human", "class": [ "dgCMatrix" ], "fields": [], "table": false, "tojson": false }, { "name": "PPI.mouse", "title": "Mouse Protein-Protein interactions", "object": "PPI.mouse", "class": [ "dgCMatrix" ], "fields": [], "table": false, "tojson": false } ], "_help": [ { "page": "addMeta", "title": "Add the cell information into meta slot", "topics": [ "addMeta" ] }, { "page": "addReduction", "title": "Add a reduced space of the data into CellChat object", "topics": [ "addReduction" ] }, { "page": "aggregateNet", "title": "Calculate the aggregated network by counting the number of links or summarizing the communication probability", "topics": [ "aggregateNet" ] }, { "page": "alpha_centrality", "title": "compute alpha centrality", "topics": [ "alpha_centrality" ] }, { "page": "AnyMatrix-class", "title": "The CellChat Class", "topics": [ "AnyMatrix-class" ] }, { "page": "barPlot", "title": "Bar plot for average gene expression", "topics": [ "barPlot" ] }, { "page": "barplot_internal", "title": "Bar plot for dataframe", "topics": [ "barplot_internal" ] }, { "page": "buildSNN", "title": "Build SNN matrix", "topics": [ "buildSNN" ] }, { "page": "CellChat_theme_opts", "title": "ggplot theme in CellChat", "topics": [ "CellChat_theme_opts" ] }, { "page": "CellChat-class", "title": "The key slots used in the CellChat object are described below.", "topics": [ "CellChat", "CellChat-class" ] }, { "page": "CellChatDB.human", "title": "Ligand-receptor interactions in CellChat database for human", "topics": [ "CellChatDB.human" ] }, { "page": "CellChatDB.mouse", "title": "Ligand-receptor interactions in CellChat database for mouse", "topics": [ "CellChatDB.mouse" ] }, { "page": "CellChatDB.zebrafish", "title": "Ligand-receptor interactions in CellChat database for Zebrafish", "topics": [ "CellChatDB.zebrafish" ] }, { "page": "checkGeneSymbol", "title": "check the official Gene Symbol", "topics": [ "checkGeneSymbol" ] }, { "page": "colorRamp3", "title": "Color interpolation", "topics": [ "colorRamp3" ] }, { "page": "compareInteractions", "title": "Comparing the number of inferred communication links between different datasets", "topics": [ "compareInteractions" ] }, { "page": "computeAveExpr", "title": "Compute averaged expression values for each cell group", "topics": [ "computeAveExpr" ] }, { "page": "computeCellDistance", "title": "Compute cell-cell distance based on the spatial coordinates", "topics": [ "computeCellDistance" ] }, { "page": "computeCentralityLocal", "title": "Compute Centrality measures for a signaling network", "topics": [ "computeCentralityLocal" ] }, { "page": "computeCommunProb", "title": "Compute the communication probability/strength between any interacting cell groups", "topics": [ "computeCommunProb" ] }, { "page": "computeCommunProbPathway", "title": "Compute the communication probability on signaling pathway level by summarizing all related ligands/receptors", "topics": [ "computeCommunProbPathway" ] }, { "page": "computeEigengap", "title": "Compute the eigengap of a given matrix for inferring the number of clusters", "topics": [ "computeEigengap" ] }, { "page": "computeEnrichmentScore", "title": "Compute and visualize the enrichment score of ligand-receptor pairs in one condition compared to another condition", "topics": [ "computeEnrichmentScore" ] }, { "page": "computeExpr_agonist", "title": "Modeling the effect of agonist on the ligand-receptor interaction", "topics": [ "computeExpr_agonist" ] }, { "page": "computeExpr_antagonist", "title": "Modeling the effect of antagonist on the ligand-receptor interaction", "topics": [ "computeExpr_antagonist" ] }, { "page": "computeExpr_complex", "title": "Compute the expression of complex in individual cells using geometric mean", "topics": [ "computeExpr_complex" ] }, { "page": "computeExpr_coreceptor", "title": "Modeling the effect of coreceptor on the ligand-receptor interaction", "topics": [ "computeExpr_coreceptor" ] }, { "page": "computeExpr_LR", "title": "Compute the expression of ligands or receptors using geometric mean", "topics": [ "computeExpr_LR" ] }, { "page": "computeExprGroup_agonist", "title": "Modeling the effect of agonist on the ligand-receptor interaction", "topics": [ "computeExprGroup_agonist" ] }, { "page": "computeExprGroup_antagonist", "title": "Modeling the effect of antagonist on the ligand-receptor interaction", "topics": [ "computeExprGroup_antagonist" ] }, { "page": "computeLaplacian", "title": "Compute eigenvalues of associated Laplacian matrix of a given matrix", "topics": [ "computeLaplacian" ] }, { "page": "computeNetD_structure", "title": "Compute the structural distance between two signaling networks", "topics": [ "computeNetD_structure" ] }, { "page": "computeNetSimilarity", "title": "Compute signaling network similarity for any pair of signaling networks", "topics": [ "computeNetSimilarity" ] }, { "page": "computeNetSimilarityPairwise", "title": "Compute signaling network similarity for any pair of datasets", "topics": [ "computeNetSimilarityPairwise" ] }, { "page": "computeRegionDistance", "title": "Compute the region distance based on the spatial locations of each splot/cell of the spatial transcriptomics", "topics": [ "computeRegionDistance" ] }, { "page": "createCellChat", "title": "Create a new CellChat object from a data matrix, Seurat or SingleCellExperiment object", "topics": [ "createCellChat" ] }, { "page": "dotPlot", "title": "Dot plot", "topics": [ "dotPlot" ] }, { "page": "entropia", "title": "compute the Shannon entropy", "topics": [ "entropia" ] }, { "page": "extract_max", "title": "extract the max value of the y axis", "topics": [ "extract_max" ] }, { "page": "extractEnrichedLR", "title": "Identify all the significant interactions (L-R pairs) and related signaling genes for a given signaling pathway", "topics": [ "extractEnrichedLR" ] }, { "page": "extractEnrichedLR_internal", "title": "Identify all the significant interactions (L-R pairs) and related signaling genes for a given signaling pathway", "topics": [ "extractEnrichedLR_internal" ] }, { "page": "extractGene", "title": "Extract the genes involved in CellChatDB", "topics": [ "extractGene" ] }, { "page": "extractGeneSubset", "title": "Extract the gene name", "topics": [ "extractGeneSubset" ] }, { "page": "extractGeneSubsetFromPair", "title": "Extract the signaling gene names from ligand-receptor pairs", "topics": [ "extractGeneSubsetFromPair" ] }, { "page": "extractLRfromGenes", "title": "Extract L-R pairs associated with a given gene set", "topics": [ "extractLRfromGenes" ] }, { "page": "filterCommunication", "title": "Filter cell-cell communication if there are only few number of cells in certain cell groups or inconsistent cell-cell communication across samples", "topics": [ "filterCommunication" ] }, { "page": "findEnrichedSignaling", "title": "Find the enriched signaling according to the genes (e.g.DEGs) and cell groups of interest", "topics": [ "findEnrichedSignaling" ] }, { "page": "geometricMean", "title": "Compute the geometric mean", "topics": [ "geometricMean" ] }, { "page": "getMaxWeight", "title": "Compute the maximum value of certain measures in the inferred cell-cell communication networks", "topics": [ "getMaxWeight" ] }, { "page": "ggPalette", "title": "Generate ggplot2 colors", "topics": [ "ggPalette" ] }, { "page": "identifyCommunicationPatterns", "title": "Identification of major signals for specific cell groups and general communication patterns", "topics": [ "identifyCommunicationPatterns" ] }, { "page": "identifyEnrichedInteractions", "title": "Identify all the significant interactions (L-R pairs) from some cell groups to other cell groups", "topics": [ "identifyEnrichedInteractions" ] }, { "page": "identifyOverExpressedGenes", "title": "Identify over-expressed signaling genes associated with each cell group", "topics": [ "identifyOverExpressedGenes" ] }, { "page": "identifyOverExpressedInteractions", "title": "Identify over-expressed ligand-receptor interactions (pairs) within the used CellChatDB", "topics": [ "identifyOverExpressedInteractions" ] }, { "page": "identifyOverExpressedLigandReceptor", "title": "Identify over-expressed ligands and (complex) receptors associated with each cell group", "topics": [ "identifyOverExpressedLigandReceptor" ] }, { "page": "liftCellChat", "title": "Update a CellChat object by lifting up the cell groups to the same cell labels across all datasets", "topics": [ "liftCellChat" ] }, { "page": "mergeCellChat", "title": "Merge CellChat objects", "topics": [ "mergeCellChat" ] }, { "page": "mergeInteractions", "title": "Compute the number of interactions/interaction strength between cell types based on their associated cell subpopulations", "topics": [ "mergeInteractions" ] }, { "page": "modify_vlnplot", "title": "modified vlnplot", "topics": [ "modify_vlnplot" ] }, { "page": "mycircle", "title": "generate circle symbol", "topics": [ "mycircle" ] }, { "page": "netAnalysis_computeCentrality", "title": "Compute the network centrality scores allowing identification of dominant senders, receivers, mediators and influencers in all inferred communication networks", "topics": [ "netAnalysis_computeCentrality" ] }, { "page": "netAnalysis_contribution", "title": "Compute and visualize the contribution of each ligand-receptor pair in the overall signaling pathways", "topics": [ "netAnalysis_contribution" ] }, { "page": "netAnalysis_diff_signalingRole_scatter", "title": "2D visualization of differential signaling roles (dominant senders (sources) or receivers (targets) ) of each cell group when comparing mutiple datasets", "topics": [ "netAnalysis_diff_signalingRole_scatter" ] }, { "page": "netAnalysis_dot", "title": "Dot plots showing the associations of latent patterns with cell groups and ligand-receptor pairs or signaling pathways", "topics": [ "netAnalysis_dot" ] }, { "page": "netAnalysis_river", "title": "River plot showing the associations of latent patterns with cell groups and ligand-receptor pairs or signaling pathways", "topics": [ "netAnalysis_river" ] }, { "page": "netAnalysis_signalingChanges_scatter", "title": "2D visualization of differential outgoing and incoming signaling associated with one cell group", "topics": [ "netAnalysis_signalingChanges_scatter" ] }, { "page": "netAnalysis_signalingRole_heatmap", "title": "Heatmap showing the contribution of signals (signaling pathways or ligand-receptor pairs) to cell groups in terms of outgoing or incoming signaling", "topics": [ "netAnalysis_signalingRole_heatmap" ] }, { "page": "netAnalysis_signalingRole_network", "title": "Heatmap showing the centrality scores/importance of cell groups as senders, receivers, mediators and influencers in a single intercellular communication network", "topics": [ "netAnalysis_signalingRole_network" ] }, { "page": "netAnalysis_signalingRole_scatter", "title": "2D visualization of dominant senders (sources) and receivers (targets)", "topics": [ "netAnalysis_signalingRole_scatter" ] }, { "page": "netClustering", "title": "Classification learning of the signaling networks", "topics": [ "netClustering" ] }, { "page": "netEmbedding", "title": "Manifold learning of the signaling networks based on their similarity", "topics": [ "netEmbedding" ] }, { "page": "netMappingDEG", "title": "Mapping the differential expressed genes (DEG) information onto the inferred cell-cell communications", "topics": [ "netMappingDEG" ] }, { "page": "netVisual", "title": "Visualize the inferred cell-cell communication network", "topics": [ "netVisual" ] }, { "page": "netVisual_aggregate", "title": "Visualize the inferred signaling network of signaling pathways by aggregating all L-R pairs", "topics": [ "netVisual_aggregate" ] }, { "page": "netVisual_barplot", "title": "Visualization of (differential) number of interactions", "topics": [ "netVisual_barplot" ] }, { "page": "netVisual_bubble", "title": "Show all the significant interactions (L-R pairs) from some cell groups to other cell groups", "topics": [ "netVisual_bubble" ] }, { "page": "netVisual_chord_cell", "title": "Chord diagram for visualizing cell-cell communication for a signaling pathway", "topics": [ "netVisual_chord_cell" ] }, { "page": "netVisual_chord_cell_internal", "title": "Chord diagram for visualizing cell-cell communication from a weighted adjacency matrix or a data frame", "topics": [ "netVisual_chord_cell_internal" ] }, { "page": "netVisual_chord_gene", "title": "Chord diagram for visualizing cell-cell communication for a set of ligands/receptors or signaling pathways", "topics": [ "netVisual_chord_gene" ] }, { "page": "netVisual_circle", "title": "Circle plot of cell-cell communication network", "topics": [ "netVisual_circle" ] }, { "page": "netVisual_diffInteraction", "title": "Circle plot showing differential cell-cell communication network between two datasets", "topics": [ "netVisual_diffInteraction" ] }, { "page": "netVisual_embedding", "title": "2D visualization of the learned manifold of signaling networks", "topics": [ "netVisual_embedding" ] }, { "page": "netVisual_embeddingPairwise", "title": "2D visualization of the joint manifold learning of signaling networks from two datasets", "topics": [ "netVisual_embeddingPairwise" ] }, { "page": "netVisual_embeddingPairwiseZoomIn", "title": "Zoom into the 2D visualization of the joint manifold learning of signaling networks from two datasets", "topics": [ "netVisual_embeddingPairwiseZoomIn" ] }, { "page": "netVisual_embeddingZoomIn", "title": "Zoom into the 2D visualization of the learned manifold learning of the signaling networks", "topics": [ "netVisual_embeddingZoomIn" ] }, { "page": "netVisual_heatmap", "title": "Visualization of network using heatmap", "topics": [ "netVisual_heatmap" ] }, { "page": "netVisual_hierarchy1", "title": "Hierarchy plot of cell-cell communications sending to cell groups in vertex.receiver", "topics": [ "netVisual_hierarchy1" ] }, { "page": "netVisual_hierarchy2", "title": "Hierarchy plot of cell-cell communication sending to cell groups not in vertex.receiver", "topics": [ "netVisual_hierarchy2" ] }, { "page": "netVisual_individual", "title": "Visualize the inferred signaling network of individual L-R pairs", "topics": [ "netVisual_individual" ] }, { "page": "netVisual_spatial", "title": "Spatial plot of cell-cell communication network", "topics": [ "netVisual_spatial" ] }, { "page": "nnd", "title": "compute nnd", "topics": [ "nnd" ] }, { "page": "node_distance", "title": "compute the node distance matrix", "topics": [ "node_distance" ] }, { "page": "normalizeData", "title": "Normalize data using a scaling factor", "topics": [ "normalizeData" ] }, { "page": "pieChart", "title": "Plot pie chart", "topics": [ "pieChart" ] }, { "page": "plotGeneExpression", "title": "A Seurat wrapper function for plotting gene expression using violin plot, dot plot or bar plot", "topics": [ "plotGeneExpression" ] }, { "page": "PPI.human", "title": "Human Protein-Protein interactions", "topics": [ "PPI.human" ] }, { "page": "PPI.mouse", "title": "Mouse Protein-Protein interactions", "topics": [ "PPI.mouse" ] }, { "page": "preProcMultiomics", "title": "Preprocessing multi-omics data and preparing the L-R database", "topics": [ "preProcMultiomics" ] }, { "page": "rankNet", "title": "Rank signaling networks based on the information flow or the number of interactions", "topics": [ "rankNet" ] }, { "page": "rankNetPairwise", "title": "Rank ligand-receptor interactions for any pair of two cell groups", "topics": [ "rankNetPairwise" ] }, { "page": "rankSimilarity", "title": "Rank the similarity of the shared signaling pathways based on their joint manifold learning", "topics": [ "rankSimilarity" ] }, { "page": "runCellChatApp", "title": "Generate a Shiny App for interactive exploration of CellChat's outputs", "topics": [ "runCellChatApp" ] }, { "page": "runPCA", "title": "Dimension reduction using PCA", "topics": [ "runPCA" ] }, { "page": "runUMAP", "title": "Run UMAP", "topics": [ "runUMAP" ] }, { "page": "scaleData", "title": "Scale the data", "topics": [ "scaleData" ] }, { "page": "scaleMat", "title": "Scale a data matrix", "topics": [ "scaleMat" ] }, { "page": "scPalette", "title": "Generate colors from a customed color palette", "topics": [ "scPalette" ] }, { "page": "searchPair", "title": "Subset the ligand-receptor interactions for given specific signals in CellChatDB", "topics": [ "searchPair" ] }, { "page": "selectK", "title": "Select the number of the patterns for running `identifyCommunicationPatterns`", "topics": [ "selectK" ] }, { "page": "setIdent", "title": "Set the default identity of cells", "topics": [ "setIdent" ] }, { "page": "show-CellChat-method", "title": "show method for CellChat", "topics": [ "show,CellChat-method" ] }, { "page": "showDatabaseCategory", "title": "Show the description of CellChatDB databse", "topics": [ "showDatabaseCategory" ] }, { "page": "sketchData", "title": "Downsampling single cell data using geometric sketching algorithm", "topics": [ "sketchData" ] }, { "page": "smoothData", "title": "Smooth the gene expression data", "topics": [ "smoothData" ] }, { "page": "spatialDimPlot", "title": "Visualize spatial cell groups", "topics": [ "spatialDimPlot" ] }, { "page": "spatialFeaturePlot", "title": "A spatial feature plots", "topics": [ "spatialFeaturePlot" ] }, { "page": "StackedVlnPlot", "title": "Stacked Violin plot", "topics": [ "StackedVlnPlot" ] }, { "page": "subsetCellChat", "title": "Subset CellChat object using a portion of cells", "topics": [ "subsetCellChat" ] }, { "page": "subsetCommunication", "title": "Subset the inferred cell-cell communications of interest", "topics": [ "subsetCommunication" ] }, { "page": "subsetCommunication_internal", "title": "Subset the inferred cell-cell communications of interest", "topics": [ "subsetCommunication_internal" ] }, { "page": "subsetData", "title": "Subset the expression data of signaling genes for saving computation cost", "topics": [ "subsetData" ] }, { "page": "subsetDB", "title": "Subset CellChatDB databse by only including interactions of interest", "topics": [ "subsetDB" ] }, { "page": "thresholdedMean", "title": "Compute the average expression per cell group when the percent of expressing cells per cell group larger than a threshold", "topics": [ "thresholdedMean" ] }, { "page": "triMean", "title": "Compute the Tukey's trimean", "topics": [ "triMean" ] }, { "page": "updateCCC_score", "title": "Update the cell-cell communication array from a customized cell-cell-communication scores between different cell groups", "topics": [ "updateCCC_score" ] }, { "page": "updateCellChat", "title": "Update a single CellChat object", "topics": [ "updateCellChat" ] }, { "page": "updateCellChatDB", "title": "Update CellChatDB by integrating new L-R pairs from other resources or adding more information", "topics": [ "updateCellChatDB" ] }, { "page": "updateClusterLabels", "title": "Update and re-order the cell group names after running `computeCommunProb`", "topics": [ "updateClusterLabels" ] } ], "_readme": "https://github.com/jinworks/CellChat/raw/HEAD/README.md", "_rundeps": [ "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", "microbenchmark", "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" ], "_sysdeps": [ { "shlib": "libstdc++", "package": "libstdc++6", "source": "gcc", "version": "14.2.0-4ubuntu2~24.04.1", "name": "c++", "homepage": "http://gcc.gnu.org/", "description": "GNU Standard C++ Library v3" } ], "_score": 8.824449708525556, "_indexed": false, "_nocasepkg": "cellchat", "_universes": [ "blaserlab" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "2.2.0.9001", "date": "2026-05-23T09:11:27.000Z", "distro": "noble", "arch": "aarch64", "commit": "75253cd0c9e68410e6e721a6d3a0419a1d7e358f", "fileid": "94f4539a4ec8442378fbc3e854c794d91d22b8e196baf6739b41f628be9e60e8", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/blaserlab/actions/runs/26328780932" }, { "r": "4.7.0", "os": "linux", "version": "2.2.0.9001", "date": "2026-05-23T09:11:40.000Z", "distro": "noble", "arch": "x86_64", "commit": "75253cd0c9e68410e6e721a6d3a0419a1d7e358f", "fileid": "400c914b9f1955e77afc8a7437a57dab74aa57608c0f4c56a1bc3e65b19f2c78", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/blaserlab/actions/runs/26328780932" }, { "r": "4.6.0", "os": "linux", "version": "2.2.0.9001", "date": "2026-05-23T09:11:42.000Z", "distro": "noble", "arch": "aarch64", "commit": "75253cd0c9e68410e6e721a6d3a0419a1d7e358f", "fileid": "a76de0774357b73a7708c42b84079651f05c17735f75e0bb112b1da672d220a6", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/blaserlab/actions/runs/26328780932" }, { "r": "4.6.0", "os": "linux", "version": "2.2.0.9001", "date": "2026-05-23T09:11:53.000Z", "distro": "noble", "arch": "x86_64", "commit": "75253cd0c9e68410e6e721a6d3a0419a1d7e358f", "fileid": "6c9e5f6c33f49f98a80ad496b18afdc65962205725e7ee6dfef251d76a8584d6", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/blaserlab/actions/runs/26328780932" }, { "r": "4.5.3", "os": "mac", "version": "2.2.0.9001", "date": "2026-05-23T09:10:05.000Z", "arch": "aarch64", "commit": "75253cd0c9e68410e6e721a6d3a0419a1d7e358f", "fileid": "5d487c4221b22c670b58b4fd41fd7472a85910279bce2de074922b3bb813c06a", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/blaserlab/actions/runs/26328780932" }, { "r": "4.5.3", "os": "mac", "version": "2.2.0.9001", "date": "2026-05-23T09:12:03.000Z", "arch": "x86_64", "commit": "75253cd0c9e68410e6e721a6d3a0419a1d7e358f", "fileid": "e8ce3b0f082b983ca513a7feeff48590d8496d8597e3e9ec03af003ebee28ad7", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/blaserlab/actions/runs/26328780932" }, { "r": "4.6.0", "os": "mac", "version": "2.2.0.9001", "date": "2026-05-23T09:09:46.000Z", "arch": "aarch64", "commit": "75253cd0c9e68410e6e721a6d3a0419a1d7e358f", "fileid": "ed30e1ec234e4eadd381bcfde99fe0413de68fd07b61e4b1cc1ab6a7c9056550", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/blaserlab/actions/runs/26328780932" }, { "r": "4.6.0", "os": "mac", "version": "2.2.0.9001", "date": "2026-05-23T09:10:57.000Z", "arch": "x86_64", "commit": "75253cd0c9e68410e6e721a6d3a0419a1d7e358f", "fileid": "2792147a2fc2ab4b02bcdd097e7f51a840d3abc9f5874d9a3388170afb79ae4d", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/blaserlab/actions/runs/26328780932" }, { "r": "4.6.0", "os": "wasm", "version": "2.2.0.9001", "date": "2026-05-23T09:12:10.000Z", "arch": "emscripten", "commit": "75253cd0c9e68410e6e721a6d3a0419a1d7e358f", "fileid": "ecc00967a19a4f0534fb7b0323c0b81e62b8b8afe325d2b394f2b720a254f7df", "status": "success", "buildurl": "https://github.com/r-universe/blaserlab/actions/runs/26328780932" }, { "r": "4.7.0", "os": "win", "version": "2.2.0.9001", "date": "2026-05-23T09:10:35.000Z", "arch": "x86_64", "commit": "75253cd0c9e68410e6e721a6d3a0419a1d7e358f", "fileid": "6e4c6b3ee896a0e4f72923e8af376acd020d5a8785796a0bbb5adf51634bc9ea", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/blaserlab/actions/runs/26328780932" }, { "r": "4.5.3", "os": "win", "version": "2.2.0.9001", "date": "2026-05-23T09:10:24.000Z", "arch": "x86_64", "commit": "75253cd0c9e68410e6e721a6d3a0419a1d7e358f", "fileid": "a06992fbe9cce7f865ba661ecd28eab065c081f373b1a1a52a7ba3e96a54b8c5", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/blaserlab/actions/runs/26328780932" }, { "r": "4.6.0", "os": "win", "version": "2.2.0.9001", "date": "2026-05-23T09:10:02.000Z", "arch": "x86_64", "commit": "75253cd0c9e68410e6e721a6d3a0419a1d7e358f", "fileid": "e8e30639f5979e8b50a9cf8c02ab00ec7be17636c22f0d7530efbc5a49c20299", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/blaserlab/actions/runs/26328780932" } ] }