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Run CellChat analysis

Source: R/RunCellChat.R
RunCellChat.Rd

Run CellChat analysis

Usage

RunCellChat(
  srt,
  group.by,
  species = c("Homo_sapiens", "Mus_musculus", "zebrafish"),
  split.by = NULL,
  annotation_selected = NULL,
  group_column = NULL,
  group_cmp = NULL,
  thresh = 0.05,
  min.cells = 10,
  assay = NULL,
  layer = "data",
  verbose = TRUE
)

Arguments

srt

A Seurat object.

group.by

Name of one or more meta.data columns to group (color) cells by.

species

The species of the data, either "Homo_sapiens", "Mus_musculus", or "zebrafish".

split.by

Name of a column in meta.data column to split plot by. Default is NULL.

annotation_selected

A vector of cell annotations of interest for running the CellChat analysis. If not provided, all cell types will be considered.

group_column

Name of the metadata column in the Seurat object that defines conditions or groups.

group_cmp

A list of pairwise condition comparisons for differential CellChat analysis.

thresh

The threshold for computing centrality scores. Default is 0.05.

min.cells

the minmum number of expressed cells required for the genes that are considered for cell-cell communication analysis. Default is 10.

assay

Which assay to use. If NULL, the default assay of the Seurat object will be used.

layer

The layer to use for the expression data. Default is "data".

verbose

Whether to print the message. Default is TRUE.

Value

A Seurat object with CellChat results stored in srt@tools[["CellChat"]].

References

CellChat

See also

CCCHeatmap, CCCStatPlot, CCCNetworkPlot

Examples

data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2026-05-14 06:55:00] Start standard processing workflow...
#> ℹ [2026-05-14 06:55:01] Checking a list of <Seurat>...
#> ! [2026-05-14 06:55:01] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-05-14 06:55:01] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-05-14 06:55:03] Perform `Seurat::FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-05-14 06:55:03] Use the separate HVF from `srt_list`
#> ℹ [2026-05-14 06:55:04] Number of available HVF: 2000
#> ℹ [2026-05-14 06:55:04] Finished check
#> ℹ [2026-05-14 06:55:04] Perform `Seurat::ScaleData()`
#> ℹ [2026-05-14 06:55:04] Perform pca linear dimension reduction
#> ℹ [2026-05-14 06:55:04] Use stored estimated dimensions 1:20 for Standardpca
#> ℹ [2026-05-14 06:55:05] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-05-14 06:55:05] Reorder clusters...
#> ℹ [2026-05-14 06:55:05] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-05-14 06:55:05] Perform umap nonlinear dimension reduction
#> ℹ [2026-05-14 06:55:05] Perform umap nonlinear dimension reduction using Standardpca (1:20)
#> ℹ [2026-05-14 06:55:10] Perform umap nonlinear dimension reduction using Standardpca (1:20)
#> ✔ [2026-05-14 06:55:15] Standard processing workflow completed
pancreas_sub <- RunCellChat(
  pancreas_sub,
  group.by = "CellType",
  species = "Mus_musculus"
)
#> ℹ [2026-05-14 06:55:15] Start CellChat analysis
#> [1] "Create a CellChat object from a data matrix"
#> Set cell identities for the new CellChat object 
#> The cell groups used for CellChat analysis are  Ductal, Ngn3-high-EP, Endocrine, Ngn3-low-EP, Pre-endocrine 
#> The number of highly variable ligand-receptor pairs used for signaling inference is 841 
#> triMean is used for calculating the average gene expression per cell group. 
#> [1] ">>> Run CellChat on sc/snRNA-seq data <<< [2026-05-14 06:55:16.649211]"
#> [1] ">>> CellChat inference is done. Parameter values are stored in `object@options$parameter` <<< [2026-05-14 06:55:38.676401]"
#> ✔ [2026-05-14 06:55:38] CellChat analysis completed

CCCNetworkPlot(
  pancreas_sub,
  method = "CellChat",
  plot_type = "bipartite"
)


CCCHeatmap(
  pancreas_sub,
  method = "CellChat",
  plot_type = "heatmap"
)


CCCStatPlot(
  pancreas_sub,
  method = "CellChat",
  plot_type = "violin",
  top_n = 50
)