omicverse.pl.CellChatViz#
- class omicverse.pl.CellChatViz(adata, palette=None)[source]#
Visualization helper for CellPhoneDB cell-cell communication outputs.
- Parameters:
adata (AnnData) – AnnData containing CellPhoneDB interaction results.
palette (dict or sequence or None) – Color mapping used for sender/receiver cell types.
- Returns:
Initializes visualization state for interaction and pathway-level plotting.
- Return type:
None
Examples
>>> viz = ov.pl.CellChatViz(adata_cpdb, palette=color_dict)
Methods
__init__(adata[, palette])Initialize with CellPhoneDB AnnData object
analyze_pathway_statistics(pathway_stats[, ...])Analyze and display detailed pathway statistics
computeNetSimilarity([similarity_type, k, ...])计算信号网络之间的相似性（类似CellChat的computeNetSimilarity功能）
compute_aggregated_network([...])Compute aggregated cell communication network
compute_communication_prob([...])Calculate cell-cell communication probability matrix (similar to CellChat's prob matrix)
compute_network_similarity([method])Compute pairwise similarity between pathway-specific networks.
compute_pathway_communication([method, ...])Calculate pathway-level cell communication strength (similar to CellChat methods)
compute_pathway_network([pvalue_threshold])Compute one sender-receiver matrix per signaling pathway.
demo_curved_arrows([signaling_pathway, ...])Demo function to show curved arrow effects
extractEnrichedLR(signaling[, ...])Extract all significant L-R pairs in the specified signaling pathway (Similar to CellChat's extractEnrichedLR function)
get_ligand_receptor_pairs([...])Get all significant ligand-receptor pair lists
get_signaling_matrix([pattern, signaling, ...])Get signaling strength matrix
get_signaling_pathways([min_interactions, ...])Get all significant signaling pathway lists using statistically more reliable methods to combine p-values from multiple L-R pairs
get_significant_pathways_v2([...])Determine significant pathways based on pathway-level communication strength (more aligned with CellChat logic)
identifyCommunicationPatterns([pattern, k, ...])识别细胞通信模式使用NMF分解（类似CellChat的identifyCommunicationPatterns功能）
identifyOverExpressedGenes(signaling[, ...])识别在特定模式中过表达的基因
identify_signaling_role([pattern, ...])Score cell types by their signaling roles in the communication network.
mean([count_min])Compute mean expression matrix for cell-cell interactions (like CellChat)
netAnalysis_computeCentrality([signaling, ...])Calculate network centrality metrics (imitating CellChat's netAnalysis_computeCentrality function)
netAnalysis_contribution(signaling[, ...])Calculate the contribution of each ligand-receptor pair to the overall signaling pathway and visualize (Similar to CellChat's netAnalysis_contribution function)
netAnalysis_signalingRole_heatmap([pattern, ...])Create a heatmap to analyze the signaling roles of cell populations (outgoing or incoming contribution) Use Marsilea for modern heatmap visualization
netAnalysis_signalingRole_network([...])Visualize signaling roles of cell populations (imitating CellChat's netAnalysis_signalingRole_network function)
netAnalysis_signalingRole_network_marsilea([...])使用Marsilea创建高级信号角色热图（CellChat风格的netAnalysis_signalingRole_network）
netAnalysis_signalingRole_scatter([...])Create 2D scatter plot to visualize cell signaling roles
netEmbedding([method, n_components, figsize])Embed pathway similarity into low-dimensional space and cluster pathways.
netVisual_aggregate(signaling[, layout, ...])Draw an aggregated communication network for selected signaling pathways.
netVisual_bubble([sources, targets, ...])Plot pathway-level communication as a bubble matrix.
netVisual_bubble_lr([sources_use, ...])Create bubble plot to visualize specific ligand-receptor pairs in cell-cell communication Similar to netVisual_bubble_marsilea but focuses on specific L-R pairs instead of pathways
netVisual_bubble_marsilea([sources_use, ...])Create advanced bubble plot using Marsilea's SizedHeatmap to visualize cell-cell communication Similar to CellChat's netVisual_bubble function, but uses SizedHeatmap to make circle size more meaningful
netVisual_chord(matrix[, title, threshold, ...])Plot a polar chord-like diagram for cell-cell communication.
netVisual_chord_LR([ligand_receptor_pairs, ...])Create chord diagram visualization for specific ligand-receptor pairs (mimicking CellChat's ligand-receptor level analysis)
netVisual_chord_cell([signaling, ...])Create chord diagram visualization using mpl-chord-diagram (mimicking CellChat's netVisual_chord_cell function)
netVisual_chord_gene([sources_use, ...])Draw a chord diagram of all ligand-receptor pairs for specific cell types as senders (gene-level) Each sector represents a ligand or receptor, ligands use sender color, receptors use receiver color
netVisual_circle(matrix[, title, ...])Circular network visualization (similar to CellChat's circle plot) Uses sender cell type colors as edge gradient colors
netVisual_circle_focused(matrix[, title, ...])Draw focused circular network diagram, showing only cell types with actual interactions
netVisual_diffusion([similarity_type, ...])可视化信号网络相似性和扩散模式
netVisual_heatmap(matrix[, title, cmap, ...])Visualize a communication matrix as a sender-receiver heatmap.
netVisual_heatmap_marsilea([signaling, ...])Draw a CellChat-style communication heatmap with Marsilea.
netVisual_heatmap_marsilea_focused([...])Draw a focused Marsilea heatmap keeping only active cell types.
netVisual_hierarchy([pathway_name, sources, ...])Visualize directed communication as a two-layer hierarchy plot.
netVisual_individual(signaling[, ...])Visualize cell-cell communication mediated by individual ligand-receptor pairs (Similar to CellChat's netVisual_individual function)
netVisual_individual_circle([...])Plot one outgoing communication circle per cell type.
netVisual_individual_circle_incoming([...])Plot one incoming communication circle per cell type.
netVisual_signaling_heatmap([pattern, ...])Use Marsilea to create a signaling pathway heatmap, showing signaling strength of cell types
netVisual_single_circle(cell_type[, ...])Plot communication circle for one selected cell type.
plot_all_visualizations([pvalue_threshold, ...])Generate all major visualization plots
pvalue([count_min])Compute p-value matrix for cell-cell interactions (like CellChat)
selectK([pattern, k_range, nrun, ...])选择NMF分解的最优K值（类似CellChat的selectK功能）