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CCC network and flow plots

Source: R/CCCNetworkPlot.R
CCCNetworkPlot.Rd

CCC network and flow plots

Usage

CCCNetworkPlot(
  srt,
  method = NULL,
  condition = NULL,
  dataset = 1,
  comparison = c(1, 2),
  plot_type = c("circle", "circle_focused", "chord", "lr_chord", "gene_chord", "pathway",
    "individual_lr", "individual", "individual_outgoing", "individual_incoming", "arrow",
    "sigmoid", "bipartite", "embedding_network", "diff_network", "spatial", "diffusion"),
  display_by = c("aggregation", "interaction"),
  sender.use = NULL,
  receiver.use = NULL,
  ligand.use = NULL,
  receptor.use = NULL,
  interaction.use = NULL,
  group.by = NULL,
  reduction = NULL,
  dims = c(1, 2),
  signaling = NULL,
  pairLR.use = NULL,
  slot.name = "net",
  thresh = 0.05,
  measure = c("weight", "count"),
  value = "sum",
  top_n = 20,
  ligand = NULL,
  receptor = NULL,
  reg.by = NULL,
  reg_palette = "Set1",
  reg_palcolor = NULL,
  expr.by = NULL,
  layout = c("circle", "hierarchy", "chord", "kk", "fr", "nicely", "lgl", "mds",
    "graphopt"),
  link_curvature = 0.2,
  link_alpha = 0.6,
  edge_value = c("sum", "mean", "max", "count"),
  edge_threshold = 0,
  edge_size = c(0.5, 1.8),
  edge_color = NULL,
  edge_alpha = 0.6,
  edge_line = c("curved", "straight"),
  edge_curvature = 0.2,
  directed = FALSE,
  arrow_type = "closed",
  arrow_angle = 20,
  arrow_length = grid::unit(0.02, "npc"),
  node_size = 5,
  node_alpha = 0.9,
  palette = "Chinese",
  palcolor = NULL,
  cell_palette = NULL,
  cell_palcolor = NULL,
  link_palette = NULL,
  link_palcolor = NULL,
  title = NULL,
  subtitle = NULL,
  legend.position = "right",
  legend.direction = "vertical",
  legend.title = NULL,
  font.size = 10,
  theme_use = "theme_scop",
  theme_args = list(),
  verbose = TRUE,
  combine_methods = c("separate", "support", "rank", "legacy"),
  resource = NULL,
  sample = NULL,
  spot_size = 1.2,
  spot_alpha = 0.35,
  composition_display = c("pie", "dominant", "none"),
  composition_radius = NULL,
  ...
)

Arguments

srt

A Seurat object.

method

Communication result type to use.

condition

Result name or comparison name.

dataset

Dataset index or name.

comparison

Comparison indices or names.

plot_type

Plot type. One of "circle", "chord", "pathway", "individual_lr", "arrow", "sigmoid", "bipartite", "embedding_network", "diff_network", or "spatial". The spatial view overlays stored communication on the selected slice coordinates and supports SpatialCellChat, SpaTalk, and COMMOT results.

display_by

Whether to summarize by "aggregation" or "interaction".

sender.use

Sender cell types to keep.

receiver.use

Receiver cell types to keep.

ligand.use

Ligands to keep.

receptor.use

Receptors to keep.

interaction.use

Interaction names to keep.

group.by

For plot_type = "embedding_network": metadata column used to define cell groups. If NULL, the grouping stored in the CCC result is used when available.

reduction

For plot_type = "embedding_network": dimensional reduction to use. If NULL, the default reduction is used.

dims

For plot_type = "embedding_network": dimensions to plot.

signaling

Signaling pathway to focus on.

pairLR.use

Specific ligand-receptor pair(s) to keep.

slot.name

CellChat slot name.

thresh

Significance threshold used when extracting communication results.

measure

Summary measure for CellChat objects.

value

Value column or summary statistic to use.

top_n

Number of top records to retain.

ligand

For plot_type = "bipartite": the ligand name to focus on. If NULL, the ligand with the highest total score is used.

receptor

For plot_type = "bipartite": optional receptor names to restrict to. If NULL, all receptors paired with ligand are shown.

reg.by

For plot_type = "bipartite": optional metadata column in srt used to color edges by regulation status (e.g. up/down). If NULL, edges are colored by sender cell type.

reg_palette

For plot_type = "bipartite": named character vector or palette name for regulation categories.

reg_palcolor

For plot_type = "bipartite": custom colors for regulation palette.

expr.by

For plot_type = "bipartite": optional metadata or score column used to scale edge line width. If NULL, all edges have equal width.

layout

Layout used for graph-based network views. "chord" can also be requested via plot_type = "circle" for backward compatibility.

Curvature used for circle-like differential links and flow edges.

Alpha used for network edges.

edge_value

Aggregation statistic for network edges.

edge_threshold

Minimum edge value to keep.

edge_size

Range used for scaling edge widths.

edge_color

Optional edge color override. For differential networks, this may also be a length-2 vector for negative/positive changes.

edge_alpha

Alpha used for embedding-network edges.

edge_line

Edge geometry for plot_type = "arrow", "sigmoid", and "embedding_network".

edge_curvature

Curvature used for curved flow/embedding edges.

directed

Whether to draw arrows for directed networks.

arrow_type

Arrow head type passed to grid::arrow().

arrow_angle

Arrow head angle passed to grid::arrow().

arrow_length

Arrow length passed to grid::arrow().

node_size

Base node size.

node_alpha

Node alpha.

palette

Main palette name.

palcolor

Main custom palette colors.

cell_palette

Cell annotation palette name.

cell_palcolor

Custom cell annotation colors.

Link palette name.

Custom link palette colors.

title

Plot title.

subtitle

Plot subtitle.

legend.position

Legend placement ("none", "left", "right", "bottom", "top"), direction, and title. legend.title = NULL uses the group name.

legend.direction

Legend direction: "horizontal" or "vertical".

legend.title

Legend title.

font.size

Base font size.

theme_use, theme_args

Theme name or function, plus extra theme arguments.

verbose

Whether to print messages.

combine_methods

Behavior when method = "CCC". "separate" returns one panel per backend, "support" counts supporting backends, "rank" combines within-method percentile ranks for visualization, and "legacy" retains the deprecated raw-score aggregation.

resource, sample

Optional resource and sample/context filters for unified CCC results.

spot_size, spot_alpha

Size and alpha of ordinary spatial observations.

composition_display

How composition-mode observations are rendered: proportional pies, dominant cell type, or neutral points.

composition_radius

Radius of composition pies in display-coordinate units. NULL derives a density-aware default.

...

Additional plot-specific options. For chord plots, reduce, max.groups, small.gap, big.gap, and lab.cex can be used to adjust the CellChat-like chord layout.

Value

A ggplot, patchwork, or recorded plot object.

Examples

data(pancreas_sub)
pancreas_sub <- RunStandardWorkflow(pancreas_sub)
#> ℹ [2026-08-30 04:02:15] Start standard processing workflow...
#> ℹ [2026-08-30 04:02:15] Checking a list of <Seurat>...
#> ! [2026-08-30 04:02:15] Data 1/1 of the `srt_list` is "unknown"
#> Warning: Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-08-30 04:02:15] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-08-30 04:02:15] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-08-30 04:02:16] Use the separate HVF from `srt_list`
#> ℹ [2026-08-30 04:02:16] Number of available HVF: 2000
#> ℹ [2026-08-30 04:02:16] Finished check
#> ℹ [2026-08-30 04:02:16] Perform `ScaleData()`
#> ℹ [2026-08-30 04:02:16] Perform pca linear dimension reduction
#> ℹ [2026-08-30 04:02:16] Use stored estimated dimensions 1:23 for Standardpca
#> ℹ [2026-08-30 04:02:17] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-08-30 04:02:17] Reorder clusters...
#> ℹ [2026-08-30 04:02:17] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-08-30 04:02:17] Perform umap nonlinear dimension reduction
#> ✔ [2026-08-30 04:02:22] Standard processing workflow completed

pc1 <- Seurat::Embeddings(pancreas_sub, "Standardpca")[, 1]
ct <- as.character(pancreas_sub$CellType)
ct_medians <- tapply(pc1, ct, median)
pancreas_sub$Condition <- ifelse(
  pc1 > ct_medians[ct],
  "ConditionA",
  "ConditionB"
)

pancreas_sub <- RunCellChat(
  pancreas_sub,
  group.by = "CellType",
  group_column = "Condition",
  group_cmp = list(c("ConditionA", "ConditionB")),
  species = "Mus_musculus"
)
#> ℹ [2026-08-30 04:02:22] Start CellChat analysis
#> ℹ [2026-08-30 04:02:22] Processing condition: "ConditionA"
#> [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, Endocrine, Ngn3-high-EP, Ngn3-low-EP, Pre-endocrine 
#> ! [2026-08-30 04:02:22] Function "CellChatDB.mouse" not found in CellChat namespace
#> Warning: Function "CellChatDB.mouse" not found in CellChat namespace
#> The number of highly variable ligand-receptor pairs used for signaling inference is 542 
#> triMean is used for calculating the average gene expression per cell group. 
#> [1] ">>> Run CellChat on sc/snRNA-seq data <<< [2026-08-30 04:02:24.142755]"
#> [1] ">>> CellChat inference is done. Parameter values are stored in `object@options$parameter` <<< [2026-08-30 04:02:39.875564]"
#> ℹ [2026-08-30 04:02:39] Processing condition: "ConditionB"
#> [1] "Create a CellChat object from a data matrix"
#> Set cell identities for the new CellChat object 
#> The cell groups used for CellChat analysis are  Ductal, Endocrine, Ngn3-high-EP, Ngn3-low-EP, Pre-endocrine 
#> ! [2026-08-30 04:02:40] Function "CellChatDB.mouse" not found in CellChat namespace
#> Warning: Function "CellChatDB.mouse" not found in CellChat namespace
#> The number of highly variable ligand-receptor pairs used for signaling inference is 597 
#> triMean is used for calculating the average gene expression per cell group. 
#> [1] ">>> Run CellChat on sc/snRNA-seq data <<< [2026-08-30 04:02:41.405111]"
#> [1] ">>> CellChat inference is done. Parameter values are stored in `object@options$parameter` <<< [2026-08-30 04:02:57.511116]"
#> ℹ [2026-08-30 04:02:57] Merging CellChat objects for comparison "ConditionA_vs_ConditionB"
#> Merge the following slots: 'data.signaling','images','net', 'netP','meta', 'idents', 'var.features' , 'DB', and 'LR'.
#> ✔ [2026-08-30 04:02:58] CellChat analysis completed

CCCNetworkPlot(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA",
  plot_type = "circle",
  display_by = "aggregation",
  value = "count",
  top_n = 20
)



CCCNetworkPlot(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA",
  plot_type = "circle",
  display_by = "aggregation",
  value = "weight",
  top_n = 20
)



CCCNetworkPlot(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA",
  plot_type = "chord",
  display_by = "aggregation",
  top_n = 12
)



CCCNetworkPlot(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA",
  plot_type = "arrow",
  display_by = "interaction",
  sender.use = "Ductal",
  receiver.use = "Ngn3-low-EP",
  edge_line = "straight",
  directed = TRUE,
  top_n = 3
)


CCCNetworkPlot(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA",
  plot_type = "arrow",
  display_by = "interaction",
  top_n = 20
)


CCCNetworkPlot(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA",
  plot_type = "sigmoid",
  display_by = "interaction",
  top_n = 20
)


CCCNetworkPlot(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA",
  plot_type = "bipartite",
  display_by = "aggregation",
  top_n = 20
)


CCCNetworkPlot(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA",
  plot_type = "embedding_network",
  group.by = "CellType",
  reduction = "UMAP",
  top_n = 20,
  label = TRUE
)


CCCNetworkPlot(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA",
  plot_type = "pathway",
  signaling = "MK"
)



CCCNetworkPlot(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA",
  plot_type = "individual_lr",
  signaling = "MK",
  pairLR.use = "MDK_SDC1"
)



CCCNetworkPlot(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA_vs_ConditionB",
  plot_type = "diff_network",
  measure = "count"
)