| 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 |
