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CCC statistical distribution and summary plots

Source: R/CCCStatPlot.R
CCCStatPlot.Rd

CCC statistical distribution and summary plots

Usage

CCCStatPlot(
  srt,
  method = NULL,
  condition = NULL,
  dataset = 1,
  comparison = c(1, 2),
  plot_type = c("bar", "sankey", "box", "violin", "comparison", "lr_contribution",
    "gene", "ranknet", "scatter", "role_change"),
  display_by = c("aggregation", "interaction"),
  sender.use = NULL,
  receiver.use = NULL,
  ligand.use = NULL,
  receptor.use = NULL,
  interaction.use = NULL,
  signaling = NULL,
  pairLR.use = NULL,
  slot.name = "net",
  thresh = 0.05,
  measure = c("count", "weight"),
  pattern = c("all", "outgoing", "incoming"),
  compare_by = c("overall", "celltype"),
  value = "score",
  stat_type = c("score", "count"),
  top_n = 20,
  x_text_angle = 90,
  link_alpha = 0.6,
  facet_by = NULL,
  edge_value = c("sum", "mean", "max", "count"),
  edge_threshold = 0,
  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",
  font.size = 10,
  theme_use = "theme_scop",
  theme_args = list(),
  grid_major = TRUE,
  grid_major_colour = "grey80",
  grid_major_linetype = 2,
  grid_major_linewidth = 0.3,
  combine = TRUE,
  nrow = NULL,
  ncol = NULL,
  verbose = TRUE,
  ...
)

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:

  • "bar" — horizontal bar chart of top interactions by aggregated score.

  • "sankey" — alluvial/sankey flow diagram.

  • "box" / "violin" — distribution of interaction scores across sender-receiver pairs.

  • "comparison" — comparison bars at overall or celltype level.

  • "lr_contribution" — ligand-receptor contribution bar plot.

  • "gene" — pathway-related ligand/receptor gene expression panel.

  • "ranknet" — pathway ranking comparison plot.

  • "scatter" — outgoing vs. incoming signaling strength scatter.

  • "role_change" — signaling change scatter for one cell identity.

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.

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.

pattern

Pattern used for pathway role plots.

compare_by

Comparison mode for CellChat summary plots.

value

Value column or summary statistic to use.

stat_type

For "bar": what to summarize per interaction. One of "score" (total aggregated score) or "count" (number of significant interactions).

top_n

Number of top records to retain.

x_text_angle

Rotation angle for x-axis labels.

Alpha used for network edges.

facet_by

Faceting variable for interaction-level plots.

edge_value

Aggregation statistic for network edges.

edge_threshold

Minimum edge value to keep.

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 position.

legend.direction

Legend direction.

font.size

Base font size.

theme_use

Theme function used for styling.

theme_args

Arguments passed to the theme function.

grid_major

Whether to show major panel grid lines for applicable statistical panels. Default is TRUE.

grid_major_colour

Color of major panel grid lines.

grid_major_linetype

Linetype of major panel grid lines.

grid_major_linewidth

Line width of major panel grid lines.

combine

Whether to combine multiple panels.

nrow

Number of rows in combined layout.

ncol

Number of columns in combined layout.

verbose

Whether to print messages.

...

Additional plot-specific options.

Value

A ggplot or recorded base plot object.

Examples

if (requireNamespace("CellChat", quietly = TRUE)) {
data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)

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"
)

CCCStatPlot(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA",
  plot_type = "sankey",
  display_by = "aggregation",
  top_n = 20
)

CCCStatPlot(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA",
  plot_type = "sankey",
  display_by = "interaction",
  top_n = 20
)

CCCStatPlot(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA",
  plot_type = "box",
  facet_by = "sender",
  top_n = 200
)

CCCStatPlot(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA",
  plot_type = "violin",
  facet_by = "receiver",
  top_n = 200
)

CCCStatPlot(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA",
  plot_type = "bar",
  palette = "Paired",
  top_n = 100
)

CCCStatPlot(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA",
  plot_type = "scatter"
)

CCCStatPlot(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA",
  plot_type = "lr_contribution",
  signaling = "MK"
)

CCCStatPlot(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA",
  plot_type = "gene",
  signaling = "MK"
)

CCCStatPlot(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA_vs_ConditionB",
  plot_type = "comparison",
  measure = "count",
  compare_by = "overall"
)

CCCStatPlot(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA_vs_ConditionB",
  plot_type = "comparison",
  measure = "weight",
  compare_by = "celltype",
  pattern = "all"
)

CCCStatPlot(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA_vs_ConditionB",
  plot_type = "ranknet"
)

CCCStatPlot(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA_vs_ConditionB",
  idents.use = "Ductal",
  plot_type = "role_change"
)
}
#> ℹ [2026-04-26 00:43:26] Start standard processing workflow...
#> ℹ [2026-04-26 00:43:28] Checking a list of <Seurat>...
#> ! [2026-04-26 00:43:28] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-04-26 00:43:28] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-04-26 00:43:31] Perform `Seurat::FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-04-26 00:43:32] Use the separate HVF from `srt_list`
#> ℹ [2026-04-26 00:43:32] Number of available HVF: 2000
#> ℹ [2026-04-26 00:43:32] Finished check
#> ℹ [2026-04-26 00:43:33] Perform `Seurat::ScaleData()`
#> ℹ [2026-04-26 00:43:34] Perform pca linear dimension reduction
#> ℹ [2026-04-26 00:43:42] Use stored estimated dimensions 1:20 for Standardpca
#> ℹ [2026-04-26 00:43:42] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-04-26 00:43:42] Reorder clusters...
#> ℹ [2026-04-26 00:43:42] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-04-26 00:43:42] Perform umap nonlinear dimension reduction
#> ℹ [2026-04-26 00:43:42] Perform umap nonlinear dimension reduction using Standardpca (1:20)
#> ℹ [2026-04-26 00:43:45] Perform umap nonlinear dimension reduction using Standardpca (1:20)
#> ✔ [2026-04-26 00:43:48] Standard processing workflow completed
#> ℹ [2026-04-26 00:43:48] Start CellChat analysis
#> ℹ [2026-04-26 00:46:52] 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, Ngn3-high-EP, Endocrine, Ngn3-low-EP, Pre-endocrine 
#> 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 <<< [2026-04-26 00:46:53.712543]"
#> [1] ">>> CellChat inference is done. Parameter values are stored in `object@options$parameter` <<< [2026-04-26 00:47:12.185159]"
#> ℹ [2026-04-26 00:47:12] Processing condition: "ConditionB"
#> [1] "Create a CellChat object from a data matrix"
#> Set cell identities for the new CellChat object 
#> The cell groups used for CellChat analysis are  Endocrine, Ngn3-high-EP, Ductal, Ngn3-low-EP, Pre-endocrine 
#> The number of highly variable ligand-receptor pairs used for signaling inference is 601 
#> triMean is used for calculating the average gene expression per cell group. 
#> [1] ">>> Run CellChat on sc/snRNA-seq data <<< [2026-04-26 00:47:13.315338]"
#> [1] ">>> CellChat inference is done. Parameter values are stored in `object@options$parameter` <<< [2026-04-26 00:47:34.629431]"
#> ℹ [2026-04-26 00:47:34] Merging CellChat objects for comparison "ConditionA_vs_ConditionB"
#> Merge the following slots: 'data.signaling','images','net', 'netP','meta', 'idents', 'var.features' , 'DB', and 'LR'.
#> ✔ [2026-04-26 00:47:34] CellChat analysis completed
#> ! [2026-04-26 00:47:57] `thisplot::StatPlot()` sankey is count-based. For `CCCStatPlot()` with `plot_type = 'sankey'`, `edge_value` is used to rank/filter pairs, but flow width is shown by interaction count.
#> ℹ [2026-04-26 00:48:00] Setting `group.by` to "Features" as `plot.by` is set to "feature"
#> Error in ccc_stat_comparison_plot(srt = srt, method = method, condition = condition,     comparison = comparison, measure = measure, compare_by = compare_by,     pattern = pattern, title = title, subtitle = subtitle, palette = palette_cfg$cell_palette,     palcolor = palette_cfg$cell_palcolor, legend.position = legend.position,     legend.direction = legend.direction, font.size = font.size,     theme_use = theme_use, theme_args = theme_args, grid_major = grid_major,     grid_major_colour = grid_major_colour, grid_major_linetype = grid_major_linetype,     grid_major_linewidth = grid_major_linewidth): unused arguments (grid_major = grid_major, grid_major_colour = grid_major_colour, grid_major_linetype = grid_major_linetype, grid_major_linewidth = grid_major_linewidth)