RunCellChat performs CellChat analysis on a Seurat object to investigate cell-to-cell communication. The results are stored in the Seurat object and can be visualized using CellChatPlot.
Usage
RunCellChat(
srt,
group.by,
species = c("human", "mouse", "zebrafish"),
split.by = NULL,
annotation_selected = NULL,
group_column = NULL,
group_cmp = NULL,
thresh = 0.05,
min.cells = 10,
verbose = TRUE
)Arguments
- srt
A Seurat object.
- group.by
Name of the metadata column in the Seurat object that contains cell annotations.
- species
The species of the data, either 'human', 'mouse' or 'zebrafish'.
- split.by
Name of the metadata column in the Seurat object that contains sample information.
- 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.
- min.cells
the minmum number of expressed cells required for the genes that are considered for cell-cell communication analysis.
- verbose
Whether to print the message. Default is
TRUE.
Examples
data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2025-11-13 12:03:24] Start standard scop workflow...
#> ℹ [2025-11-13 12:03:25] Checking a list of <Seurat> object...
#> ! [2025-11-13 12:03:25] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2025-11-13 12:03:25] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 1/1 of the `srt_list`...
#> ℹ [2025-11-13 12:03:27] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2025-11-13 12:03:27] Use the separate HVF from srt_list
#> ℹ [2025-11-13 12:03:27] Number of available HVF: 2000
#> ℹ [2025-11-13 12:03:27] Finished check
#> ℹ [2025-11-13 12:03:28] Perform `Seurat::ScaleData()`
#> ℹ [2025-11-13 12:03:28] Perform pca linear dimension reduction
#> StandardPC_ 1
#> Positive: Aplp1, Cpe, Gnas, Fam183b, Map1b, Hmgn3, Pcsk1n, Chga, Tuba1a, Bex2
#> Syt13, Isl1, 1700086L19Rik, Pax6, Chgb, Scgn, Rbp4, Scg3, Gch1, Camk2n1
#> Cryba2, Pcsk2, Pyy, Tspan7, Mafb, Hist3h2ba, Dbpht2, Abcc8, Rap1b, Slc38a5
#> Negative: Spp1, Anxa2, Sparc, Dbi, 1700011H14Rik, Wfdc2, Gsta3, Adamts1, Clu, Mgst1
#> Bicc1, Ldha, Vim, Cldn3, Cyr61, Rps2, Mt1, Ptn, Phgdh, Nudt19
#> Smtnl2, Smco4, Habp2, Mt2, Col18a1, Rpl12, Galk1, Cldn10, Acot1, Ccnd1
#> StandardPC_ 2
#> Positive: Rbp4, Tagln2, Tuba1b, Fkbp2, Pyy, Pcsk2, Iapp, Tmem27, Meis2, Tubb4b
#> Pcsk1n, Dbpht2, Rap1b, Dynll1, Tubb2a, Sdf2l1, Scgn, 1700086L19Rik, Scg2, Abcc8
#> Atp1b1, Hspa5, Fam183b, Papss2, Slc38a5, Scg3, Mageh1, Tspan7, Ppp1r1a, Ociad2
#> Negative: Neurog3, Btbd17, Gadd45a, Ppp1r14a, Neurod2, Sox4, Smarcd2, Mdk, Pax4, Btg2
#> Sult2b1, Hes6, Grasp, Igfbpl1, Gpx2, Cbfa2t3, Foxa3, Shf, Mfng, Tmsb4x
#> Amotl2, Gdpd1, Cdc14b, Epb42, Rcor2, Cotl1, Upk3bl, Rbfox3, Cldn6, Cer1
#> StandardPC_ 3
#> Positive: Nusap1, Top2a, Birc5, Aurkb, Cdca8, Pbk, Mki67, Tpx2, Plk1, Ccnb1
#> 2810417H13Rik, Incenp, Cenpf, Ccna2, Prc1, Racgap1, Cdk1, Aurka, Cdca3, Hmmr
#> Spc24, Kif23, Sgol1, Cenpe, Cdc20, Hist1h1b, Cdca2, Mxd3, Kif22, Ska1
#> Negative: Anxa5, Pdzk1ip1, Acot1, Tpm1, Anxa2, Dcdc2a, Capg, Sparc, Ttr, Pamr1
#> Clu, Cxcl12, Ndrg2, Hnf1aos1, Gas6, Gsta3, Krt18, Ces1d, Atp1b1, Muc1
#> Hhex, Acadm, Spp1, Enpp2, Bcl2l14, Sat1, Smtnl2, 1700011H14Rik, Tgm2, Fam159a
#> StandardPC_ 4
#> Positive: Glud1, Tm4sf4, Akr1c19, Cldn4, Runx1t1, Fev, Pou3f4, Gm43861, Pgrmc1, Arx
#> Cd200, Lrpprc, Hmgn3, Ppp1r14c, Pam, Etv1, Tsc22d1, Slc25a5, Akap17b, Pgf
#> Fam43a, Emb, Jun, Krt8, Dnajc12, Mid1ip1, Ids, Rgs17, Uchl1, Alcam
#> Negative: Ins2, Ins1, Ppp1r1a, Nnat, Calr, Sytl4, Sdf2l1, Iapp, Pdia6, Mapt
#> G6pc2, C2cd4b, Npy, Gng12, P2ry1, Ero1lb, Adra2a, Papss2, Arhgap36, Fam151a
#> Dlk1, Creld2, Gip, Tmem215, Gm27033, Cntfr, Prss53, C2cd4a, Lyve1, Ociad2
#> StandardPC_ 5
#> Positive: Pdx1, Nkx6-1, Npepl1, Cldn4, Cryba2, Fev, Jun, Chgb, Gng12, Adra2a
#> Mnx1, Sytl4, Pdk3, Gm27033, Nnat, Chga, Ins2, 1110012L19Rik, Enho, Krt7
#> Mlxipl, Tmsb10, Flrt1, Pax4, Tubb3, Prrg2, Gars, Frzb, BC023829, Gm2694
#> Negative: Irx2, Irx1, Gcg, Ctxn2, Tmem27, Ctsz, Tmsb15l, Nap1l5, Pou6f2, Gria2
#> Ghrl, Peg10, Smarca1, Arx, Lrpap1, Rgs4, Ttr, Gast, Tmsb15b2, Serpina1b
#> Slc16a10, Wnk3, Ly6e, Auts2, Sct, Arg1, Dusp10, Sphkap, Dock11, Edn3
#> ℹ [2025-11-13 12:03:29] Perform `Seurat::FindClusters()` with louvain and `cluster_resolution` = 0.6
#> ℹ [2025-11-13 12:03:29] Reorder clusters...
#> ℹ [2025-11-13 12:03:29] Perform umap nonlinear dimension reduction
#> ℹ [2025-11-13 12:03:29] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-11-13 12:03:29] UMAP will return its model
#> ℹ [2025-11-13 12:03:33] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-11-13 12:03:33] UMAP will return its model
#> ✔ [2025-11-13 12:03:37] Run scop standard workflow done
pancreas_sub <- RunCellChat(
pancreas_sub,
group.by = "CellType",
species = "mouse"
)
#> ℹ [2025-11-13 12:03:37] Start CellChat analysis
#> ✔ [2025-11-13 12:03:37] jinworks/CellChat installed successfully
#> ✔ [2025-11-13 12:03:37] immunogenomics/presto installed successfully
#> [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 <<< [2025-11-13 12:03:38.47218]"
#> [1] ">>> CellChat inference is done. Parameter values are stored in `object@options$parameter` <<< [2025-11-13 12:04:47.994733]"
#> ✔ [2025-11-13 12:04:48] CellChat analysis completed
CellChatPlot(pancreas_sub)
#> ℹ [2025-11-13 12:04:48] Creating "aggregate" plot for condition "ALL"
#> Signaling role analysis on the aggregated cell-cell communication network from all signaling pathways
#> ✔ [2025-11-13 12:04:48] Plot creation completed
