cellchat-cell-communication

name	cellchat-cell-communication
description	Infer and visualize intercellular communication from scRNA-seq with CellChat (R). Build CellChat from Seurat/counts → subset CellChatDB ligand-receptor pairs → over-expressed genes per group → communication probabilities → pathway signaling → network centrality (senders/receivers/influencers) → chord/heatmap/bubble plots → cross-condition compare. Human, mouse. Use liana for pure-Python.
license	MIT

CellChat — Cell-Cell Communication Analysis

Overview

CellChat is an R package that infers and visualizes intercellular signaling networks from single-cell RNA-seq data. Starting from a normalized expression matrix and cluster labels, CellChat identifies ligand-receptor interactions supported by CellChatDB — a manually curated database of over 2,000 validated ligand-receptor pairs in human and mouse. Communication probability is modeled using the law of mass action, combining expression levels of ligands, receptors, and cofactors. CellChat aggregates pair-level probabilities into pathway-level signaling networks and quantifies each cell group's role as a signal sender, receiver, mediator, or influencer. The result is a rich, interpretable picture of which cell types talk to which, through which signaling pathways, and how these patterns change between conditions.

When to Use

• Characterizing which cell types are the dominant senders or receivers of paracrine and autocrine signals in a tissue atlas or disease sample
• Identifying specific ligand-receptor pairs mediating communication between a cell population of interest (e.g., tumor cells → T cells, fibroblasts → epithelial cells)
• Comparing intercellular signaling networks between two conditions (e.g., healthy vs. diseased, treatment vs. control) to find rewired or lost communication
• Discovering pathway-level signaling programs (e.g., MHC-II, COLLAGEN, VEGF) enriched in a particular cell-cell interaction
• Prioritizing targets for perturbation experiments by ranking signaling pathways by their aggregate communication strength or network centrality
• Use liana (Python/R) instead when you want a pure-Python workflow or a consensus ranking across multiple ligand-receptor databases (CellChat, CellPhoneDB, Connectome, NicheNet)
• Use NicheNet (R) instead when you need ligand-to-target gene regulatory inference — predicting which ligands from sender cells regulate which target genes in receiver cells

Prerequisites

• R packages: CellChat (>= 2.0), Seurat (>= 4.0, for Seurat-based input), NMF, ggplot2, ggalluvial, igraph, dplyr, patchwork, reticulate (optional)
• Data requirements: Normalized scRNA-seq count matrix (genes × cells) and a cell group identity vector (cluster labels or cell types). Raw counts are acceptable if normalized inside CellChat.
• Species: CellChatDB available for human and mouse; other species require custom database construction
• Memory: 8 GB RAM minimum for datasets with 10,000–50,000 cells; 32 GB+ recommended for larger datasets

# Install CellChat from GitHub (CRAN version may lag)
if (!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")
BiocManager::install(c("BiocNeighbors", "ComplexHeatmap"))

install.packages("devtools")
devtools::install_github("jinworks/CellChat")

# Core dependencies
install.packages(c("NMF", "ggplot2", "ggalluvial", "igraph",
                   "dplyr", "patchwork", "circlize", "RColorBrewer"))

Quick Start

library(CellChat)
library(Seurat)

# Assume `seurat_obj` is a processed Seurat object with cell type identities in Idents()
data.input <- GetAssayData(seurat_obj, assay = "RNA", slot = "data")  # normalized counts
meta       <- data.frame(labels = Idents(seurat_obj), row.names = names(Idents(seurat_obj)))

cellchat <- createCellChat(object = data.input, meta = meta, group.by = "labels")
cellchat@DB <- CellChatDB.human  # or CellChatDB.mouse

cellchat <- subsetData(cellchat)
cellchat <- identifyOverExpressedGenes(cellchat)
cellchat <- identifyOverExpressedInteractions(cellchat)
cellchat <- computeCommunProb(cellchat type  
cellchat  filterCommunicationcellchat min.cells  
cellchat  computeCommunProbPathwaycellchat
cellchat  aggregateNetcellchat


printcellchat

Workflow

Step 1: Create CellChat Object

Build a CellChat object from either a Seurat object or a raw count matrix with accompanying metadata.

library(CellChat)
library(Seurat)

# --- Option A: from a Seurat object ---
# seurat_obj must have cell type identities set with Idents() or in meta.data
data.input <- GetAssayData(seurat_obj, assay = "RNA", slot = "data")  # log-normalized
meta <- data.frame(
  labels = Idents(seurat_obj),
  row.names = colnames(seurat_obj)
)
cellchat <- createCellChat(object = data.input, meta = meta, group.by = "labels")

# --- Option B: from a count matrix directly ---
# data.input: genes-by-cells normalized matrix (dgCMatrix or dense matrix)
# identity: named factor of cell group labels (length = ncol(data.input))
cellchat <- createCellChat(object = data.input, meta = data.frame(labels = identity),
                           group.by = "labels"

cat levelscellchatidents 
cat ncoldata.input 

Step 2: Set CellChatDB and Subset Interactions

Load the species-appropriate ligand-receptor database and optionally subset to a signaling category of interest.

# Load database for the appropriate species
CellChatDB <- CellChatDB.human   # use CellChatDB.mouse for mouse data

# Inspect available signaling categories
unique(CellChatDB$interaction$annotation)
# [1] "Secreted Signaling"    "ECM-Receptor"          "Cell-Cell Contact"

# Option 1: Use all interactions (recommended for discovery)
cellchat@DB <- CellChatDB

# Option 2: Subset to secreted ligand-receptor pairs only (reduces noise)
CellChatDB.use <- subsetDB(CellChatDB, search = "Secreted Signaling",
                           key = "annotation")
cellchat@DB <- CellChatDB.use

# Subset the CellChat data slots to only genes in the database
cellchat <- subsetData(cellchat)
cat("Genes retained after database subset:", nrow([e-mail]), "\n")
# Genes retained after database subset: 1842

Step 3: Identify Over-Expressed Genes and Interactions

For each cell group, identify ligands and receptors that are significantly over-expressed compared to other groups.

# Identify over-expressed genes per cell group (uses Seurat-style wilcoxon test)
cellchat <- identifyOverExpressedGenes(cellchat)

# Map over-expressed genes to ligand-receptor pairs in CellChatDB
cellchat <- identifyOverExpressedInteractions(cellchat)

# Inspect how many interactions were identified per group pair
df.net <- subsetCommunication(cellchat)
cat("Total inferred interactions:", nrow(df.net), "\n")
head(df.net[, c("source", "target", "ligand", "receptor", "prob")], 5)
#      source   target  ligand receptor      prob
# 1   B_cell Macrophage  CD22     PTPRC 0.0318
# 2 Fibroblast    Tumor   FN1     CD44  0.1072
# ...

Step 4: Infer Cell-Cell Communication Probabilities

Compute communication probability for each ligand-receptor pair between every ordered pair of cell groups using the law of mass action. CellChat accounts for multi-subunit complexes and co-stimulatory/co-inhibitory cofactors.

# Compute pairwise communication probability
# type = "triMean": uses 25th percentile × mean × 25th percentile for robustness
# type = "truncatedMean": uses trimmed mean with threshold parameter trim
cellchat <- computeCommunProb(
  cellchat,
  type          = "triMean",   # recommended default
  trim          = 0.1,         # fraction to trim (only used if type="truncatedMean")
  nboot         = 100,         # bootstrap iterations for p-value estimation
  seed.use      = 42,
  population.size = TRUE       # weight by population size (recommended)
)

# Filter out interactions with too few cells in sender or receiver groups
cellchat <- filterCommunication(cellchat, min.cells = 10)

# Summary of retained interactions
df.net <- subsetCommunication(cellchat)
cat("Interactions after filtering:", nrow(df.net), "\n")
cat("Significant interactions (p<0.05):", df.netpval   

Step 5: Compute Pathway-Level Communication

Aggregate ligand-receptor pair probabilities into signaling pathway-level networks (e.g., COLLAGEN, MHC-II, VEGF).

# Aggregate to pathway level
cellchat <- computeCommunProbPathway(cellchat)

# Build aggregate interaction count and weight networks
cellchat <- aggregateNet(cellchat)

# View significant pathways
cat("Significant signaling pathways:\n")
print(cellchat@netP$pathways)
# [1] "MHC-II"    "COLLAGEN"  "FN1"       "VEGF"      "CXCL"
# [6] "CCL"       "MIF"       "APP"       "GALECTIN"  ...

# Extract pathway-level communication probabilities between groups
df.pathways <- subsetCommunication(cellchat, slot.name = "netP")
head(df.pathways[, c("source", "target", "pathway_name", "prob")], 5)
#        source    target pathway_name     prob
# 1  Fibroblast     Tumor     COLLAGEN   0.2341
# 2  Macrophage  Fibroblast     MIF    0.1876
# ...

Step 6: Analyze Network Centrality — Senders, Receivers, Influencers

Identify each cell group's network role by computing information flow measures: out-strength (sender), in-strength (receiver), betweenness (mediator), and eigenvector centrality (influencer).

# Compute centrality measures for all pathways
cellchat <- netAnalysis_computeCentrality(cellchat, slot.name = "netP")

# Visualize centrality scores as a heatmap (rows=pathways, cols=cell groups)
# Each dot size: outgoing signal strength; color: incoming signal strength
netAnalysis_signalingRole_heatmap(
  cellchat,
  pattern    = "all",     # "outgoing", "incoming", or "all"
  signaling  = NULL,      # NULL = all pathways; or specify e.g. c("COLLAGEN","VEGF")
  height     = 10,
  color.heatmap = "OrRd"
)

# Identify dominant communication patterns using NMF
# outgoing patterns reveal which cell groups co-activate similar pathways
library(NMF)
selectK(cellchat, pattern = "outgoing")    # elbow plot to choose K
cellchat <- identifyCommunicationPatterns(
  cellchat,
  pattern = "outgoing",
  k       = 3,            # number of latent patterns; choose from selectK elbow
  width   = 
  height   

Step 7: Visualize — Chord Diagrams, Heatmaps, Bubble Plots

CellChat provides several visualization functions for both aggregate and pathway-specific interactions.

library(ggplot2)
library(patchwork)

# --- 7a. Chord diagram: aggregate interaction count and weight ---
par(mfrow = c(1, 2))
netVisual_circle(
  cellchat@net$count,
  vertex.weight = as.numeric(table(cellchat@idents)),
  weight.scale  = TRUE,
  label.edge    = FALSE,
  title.name    = "Number of interactions"
)
netVisual_circle(
  cellchat@net$weight,
  vertex.weight = as.numeric(table(cellchat@idents)),
  weight.scale  = TRUE,
  label.edge    = FALSE,
  title.name    = 



p1  netVisual_heatmapcellchat measure    color.heatmap  
p2  netVisual_heatmapcellchat measure   color.heatmap  
p1  p2


netVisual_aggregate
  cellchat
  signaling       
  layout          
  vertex.receiver     



netVisual_bubble
  cellchat
  sources.use     
  targets.use     
  signaling      
  remove.isolate  

ggsave width   height  

Step 8: Compare Two CellChat Objects Across Conditions

When you have two conditions (e.g., healthy and diseased), merge the CellChat objects and compare signaling networks.

# Assume cellchat_ctrl and cellchat_disease are pre-computed CellChat objects
object.list <- list(Control = cellchat_ctrl, Disease = cellchat_disease)
cellchat_merged <- mergeCellChat(object.list, add.names = names(object.list))

# --- Compare total interaction count and strength ---
compareInteractions(cellchat_merged, show.legend = FALSE,
                    group = c(1, 2), measure = "count")
compareInteractions(cellchat_merged, show.legend = FALSE,
                    group = c(1, 2), measure = "weight")

# --- Differential interaction chord diagram (gained/lost connections) ---
netVisual_diffInteraction(cellchat_merged weight.scale  


rankNetcellchat_merged mode   stacked   do.stat  


rankNetPairwise
  cellchat_merged
  comparison   
  slot.name   
  measure     

Key Parameters

Parameter	Function	Default	Range / Options	Effect
type	computeCommunProb	"triMean"	"triMean", "truncatedMean", "thresholdedMean", "median"	Aggregation method for group-level expression; triMean is most stringent
trim	computeCommunProb	0.1	0–0.25	Fraction trimmed from each tail; only applies when type="truncatedMean"
nboot	computeCommunProb	100	50–1000	Bootstrap iterations for p-value estimation; higher = slower but more accurate
population.size	computeCommunProb	TRUE	TRUE, FALSE	Weight communication probability by cell group size; recommended for heterogeneous data
min.cells	filterCommunication	10	5–50	Minimum number of cells required per sender or receiver group to retain an interaction
k	identifyCommunicationPatterns	required	2–6 (choose via selectK)	Number of latent communication patterns; use selectK elbow to select
thresh	netAnalysis_computeCentrality	0.05	0.01–0.1	P-value cutoff for retaining interactions in centrality analysis

Key Concepts

Communication Probability Model

CellChat quantifies communication probability using the law of mass action. For a ligand L expressed in cell group A and receptor R (potentially a multi-subunit complex) expressed in cell group B:

P(A → B | L-R) = hill(expr_L_A) × hill(expr_R1_B) × hill(expr_R2_B) × ...

where hill(x) = x^n / (K^n + x^n) (Hill function, n=1 by default), and expression values are group-aggregated using the chosen type argument. Multi-subunit receptor complexes require all subunits to be expressed; the probability is the product of Hill-transformed subunit expressions.

CellChatDB Ligand-Receptor Database

CellChatDB is a curated database of experimentally validated ligand-receptor interactions organized into three categories:

# Inspect the database structure
dim(CellChatDB.human$interaction)   # [1] 2293   17
head(CellChatDB.human$interaction[, c("interaction_name", "pathway_name",
                                       "ligand", "receptor", "annotation")], 4)
#   interaction_name pathway_name ligand receptor          annotation
# 1         TGFB1_TGFBR1_TGFBR2         TGFb  TGFB1  TGFBR1_TGFBR2  Secreted Signaling
# 2          WNT5A_FZD1_LRP5          WNT   WNT5A     FZD1_LRP5   Secreted Signaling
# 3             FN1_CD44             FN1    FN1      CD44        ECM-Receptor
# 4          NOTCH1_DLL4        NOTCH  NOTCH1       DLL4   Cell-Cell Contact

Three categories cover distinct biological mechanisms:

• Secreted Signaling: classical paracrine/autocrine ligands (cytokines, growth factors, morphogens)
• ECM-Receptor: extracellular matrix components binding membrane receptors
• Cell-Cell Contact: juxtacrine signals requiring direct cell contact (Notch, Ephrin, Semaphorin)

Network Centrality Roles

Role	Centrality Measure	Interpretation
Sender	Out-degree / out-strength	Cell groups that broadcast signals to many targets
Receiver	In-degree / in-strength	Cell groups that receive signals from many sources
Mediator	Betweenness centrality	Cell groups that bridge communication between other groups
Influencer	Eigenvector centrality	Cell groups connected to other highly-connected groups

Information Flow vs. Interaction Count

aggregateNet computes two complementary matrices:

• cellchat@net$count: number of statistically significant ligand-receptor pairs per cell-group pair (raw interaction count)
• cellchat@net$weight: sum of communication probabilities across all pairs (interaction strength / information flow)

High count with low weight indicates many weak interactions; high weight with low count indicates a few dominant pathways.

Common Recipes

Recipe: Extract All Significant Interactions as a Data Frame

Use when you want to export results, apply custom filtering, or feed interactions into downstream pathway analysis.

# All ligand-receptor level interactions (p < 0.05)
df.lr <- subsetCommunication(cellchat, slot.name = "net")
df.lr_sig <- df.lr[df.lr$pval < 0.05, ]
cat("Significant LR interactions:", nrow(df.lr_sig), "\n")

# All pathway-level interactions
df.path <- subsetCommunication(cellchat, slot.name = "netP")

# Interactions involving specific cell groups
df.tumor_recv <- subsetCommunication(cellchat,
                                      targets.use = "Tumor",
                                      slot.name   = "net")
cat("Interactions targeting Tumor cells:", nrow(df.tumor_recv), "\n")

# Save to CSV for downstream analysis
write.csv(df.lr_sig,    "cellchat_lr_interactions.csv"    row.names  
write.csvdf.path       row.names  
write.csvdf.tumor_recv    row.names  

Recipe: Visualize Signaling Role of a Specific Pathway

Use when you want a detailed view of which cell types send and receive via one pathway.

# Show chord diagram + violin plots for a single pathway
pathway <- "COLLAGEN"

# Chord diagram
netVisual_aggregate(cellchat, signaling = pathway, layout = "chord")
title(main = paste0(pathway, " signaling network"))

# Contribution of each LR pair to the pathway
netAnalysis_contribution(cellchat, signaling = pathway)

# Gene expression of constituent ligands and receptors
plotGeneExpression(
  cellchat,
  signaling  = pathway,
  enriched.only = TRUE,     # show only significantly enriched genes
  type       = "violin"
)

Recipe: Save and Reload a CellChat Object

Use when checkpointing a completed run before visualization or comparison steps.

# Save the completed CellChat object
saveRDS(cellchat, file = "cellchat_analysis.rds")
cat("Saved to cellchat_analysis.rds\n")

# Reload and resume analysis
cellchat_loaded <- readRDS("cellchat_analysis.rds")
cat("Cell groups:", levels(cellchat_loaded@idents), "\n")
cat("Pathways:", length(cellchat_loaded@netP$pathways), "\n")

# Verify the object is complete
slotNames(cellchat_loaded)
# [1] "data"          "data.signaling" "images"        "net"
# [5] "netP"          "meta"           "idents"        "var.features"
# [9] "DB"            "LR"             "options"

Recipe: Python-Equivalent Workflow with liana

Use when your pipeline is Python-based or you want a consensus ranking across multiple LR databases.

# Install: pip install liana
import liana
import scanpy as sc
import pandas as pd

# Load preprocessed AnnData (cells x genes, log-normalized)
adata = sc.read_h5ad("my_scrna.h5ad")
# adata.obs["celltype"] must contain cluster/cell-type labels

# Run liana with CellChat resource (consensus across CellChatDB, CellPhoneDB, NATMI, etc.)
liana.mt.rank_aggregate(
    adata,
    groupby   = "celltype",
    resource_name = "consensus",   # or "cellchat" for CellChat-only LR pairs
    expr_prop = 0.1,               # min fraction cells expressing ligand/receptor
    verbose   = True
)

# Results stored in adata.uns["liana_res"]
df = adata.uns["liana_res"]
df_sig = df[df["magnitude_rank"] < 0.05].sort_values("magnitude_rank")
print(df_sig[["source", "target", "ligand_complex", "receptor_complex",
              "magnitude_rank"]].head(10))
df_sig.to_csv("liana_interactions.csv", index=False)

Expected Outputs

Output	Type	Description
cellchat@net$count	R matrix (n_groups × n_groups)	Number of significant LR interactions between each cell-group pair
cellchat@net$weight	R matrix (n_groups × n_groups)	Aggregate communication probability (information flow) between cell-group pairs
cellchat@netP$pathways	Character vector	Names of all inferred signaling pathways
subsetCommunication(cellchat)	data.frame	Table of all LR-level interactions with source, target, ligand, receptor, probability, p-value
subsetCommunication(cellchat, slot.name="netP")	data.frame	Pathway-level interaction table
Chord diagram (PDF/PNG)	Figure	Circular diagram showing interaction strength between cell groups
Heatmap (PDF/PNG)	Figure	Cell-group × cell-group interaction count or weight heatmap
Bubble plot (PDF/PNG)	Figure	Dot plot showing interaction probabilities per LR pair per group pair
Signaling role heatmap (PDF/PNG)	Figure	Pathway × cell-group centrality scores (sender/receiver roles)

Troubleshooting

Problem	Likely Cause	Solution
Error in computeCommunProb: all probabilities are zero	Genes in CellChatDB not detected or filtered out	Confirm subsetData() retains genes: nrow([e-mail]) > 0; check that expression matrix is log-normalized (not raw counts) and that gene names match CellChatDB (human: HGNC symbols; mouse: MGI symbols)
Warning: groups with fewer than min.cells cells are removed	Small clusters dropped at filtering step	Lower min.cells in filterCommunication() (e.g., min.cells = 5) or merge rare clusters before creating the CellChat object
identifyCommunicationPatterns NMF error or no convergence	Number of patterns k too high or data too sparse	Use selectK() to choose k from the elbow in cophenetic/dispersion curves; try k=2 or k=3 first
Memory error / session crash during computeCommunProb	Dataset too large for available RAM	Subsample to ≤30,000 cells per condition; or run computeCommunProb with nboot = 50 to reduce bootstrap memory footprint
Chord diagram is unreadable (too many cell groups)	Many fine-grained clusters	Aggregate clusters into broader categories before creating CellChat object; or use netVisual_heatmap which scales better with many groups
mergeCellChat error: cell group labels do not match	Cell type names differ between objects	Harmonize levels(cellchat_ctrl@idents) and levels(cellchat_disease@idents) before merging; use setIdent() to rename groups
Gene symbols not recognized (all probabilities 0)	Mixed human/mouse gene naming convention	Confirm species: human genes are ALL CAPS (e.g., TGFB1); mouse genes are title case (e.g., Tgfb1). Set CellChatDB.mouse for mouse data

References

• CellChat GitHub — jinworks/CellChat — source code, tutorials, and vignettes
• CellChat vignette (HTML) — official step-by-step tutorial
• Jin et al. (2021) Nature Communications — CellChat original paper — algorithmic description, benchmarking, and case studies
• CellChat comparison vignette — comparing CellChatDB with CellPhoneDB and other databases
• liana Python package documentation — pure-Python alternative with consensus LR ranking across CellChat, CellPhoneDB, NATMI, and Connectome