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CCC heatmap and dot matrix plot

Source: R/CCCHeatmap.R
CCCHeatmap.Rd

CCC heatmap and dot matrix plot

Usage

CCCHeatmap(
  srt,
  method = NULL,
  condition = NULL,
  dataset = 1,
  comparison = c(1, 2),
  plot_type = c("heatmap", "focused_heatmap", "dot", "matrix_dot", "tile",
    "source_target_dot", "source_target_tile", "sample_dot", "bubble", "bubble_lr",
    "pathway_bubble", "ligand_target", "role_heatmap", "role_network",
    "role_network_marsilea", "diff_heatmap"),
  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("outgoing", "incoming", "all"),
  value = "sum",
  top_n = 500,
  top_anno = "bar",
  right_anno = "cell",
  left_anno = "bar",
  bottom_anno = "cell",
  bar_value = "sum",
  add_text = NULL,
  cluster_rows = FALSE,
  cluster_columns = FALSE,
  color.by = c("score", "pvalue"),
  x_text_angle = 90,
  facet_by = NULL,
  show_row_names = TRUE,
  show_column_names = TRUE,
  edge_value = c("sum", "mean", "max", "count"),
  border = TRUE,
  value_palette = "RdBu",
  value_palcolor = NULL,
  cell_palette = "Chinese",
  cell_palcolor = NULL,
  palette = "Chinese",
  palcolor = NULL,
  width = NULL,
  height = NULL,
  units = "inch",
  title = NULL,
  subtitle = NULL,
  legend.position = "right",
  legend.direction = "vertical",
  font.size = 10,
  theme_use = "theme_scop",
  theme_args = list(),
  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 "heatmap" or "dot". "bubble" is a CellChat-specific interaction bubble matrix. "ligand_target" is a special heatmap path available only with NicheNet/MultiNicheNet results. "role_heatmap" and "diff_heatmap" are CellChat-specific pathway role views.

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.

value

Value column or summary statistic to use.

top_n

Number of top records to retain.

top_anno, bottom_anno

Column-side annotations for sender groups. Each side accepts NULL, "bar", "box", "point", "line", "histogram", "density", "violin", "cell", or a character vector containing multiple values. Defaults are top_anno = "bar" and bottom_anno = "cell".

left_anno, right_anno

Row-side annotations for receiver groups. Each side accepts NULL, "bar", "box", "point", "line", "histogram", "density", "violin", "cell", or a character vector containing multiple values. Defaults are left_anno = "bar" and right_anno = "cell".

bar_value

Aggregation metric shown in the bar annotations. One or more of "count", "sum", "mean", or "max". Multiple values add multiple annotation tracks. Default "sum".

add_text

Logical. Show numeric value labels inside each cell (heatmap mode only). Default TRUE for aggregation mode, FALSE for interaction mode.

cluster_rows, cluster_columns

Whether to cluster heatmap rows/columns. Defaults are both FALSE.

color.by

For interaction heatmaps, value used for tile coloring. Usually "score" or "pvalue".

x_text_angle

Rotation angle for x-axis labels.

facet_by

Faceting variable for interaction-level plots.

show_row_names, show_column_names

Whether to draw row/column names for the heatmap body.

edge_value

Aggregation statistic for network edges.

border

Logical. Whether to draw borders for the heatmap body and all annotation tracks. Default TRUE.

value_palette

Palette used for heatmap value fills.

value_palcolor

Optional custom colors for value_palette.

cell_palette

Cell annotation palette name.

cell_palcolor

Custom cell annotation colors.

palette

Main palette name.

palcolor

Main custom palette colors.

width, height

Optional heatmap body width and height. When only one is supplied, the other is inferred from the matrix dimensions to keep cells square. When both are NULL, a square-cell size is computed automatically.

units

Units for width and height. Default "inch".

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.

verbose

Whether to print messages.

...

Additional plot-specific options.

Value

A ggplot / patchwork object wrapping the ComplexHeatmap grob.

Examples

data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2026-05-14 05:24:02] Start standard processing workflow...
#> ℹ [2026-05-14 05:24:04] Checking a list of <Seurat>...
#> ! [2026-05-14 05:24:04] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-05-14 05:24:04] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-05-14 05:24:05] Perform `Seurat::FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-05-14 05:24:05] Use the separate HVF from `srt_list`
#> ℹ [2026-05-14 05:24:05] Number of available HVF: 2000
#> ℹ [2026-05-14 05:24:06] Finished check
#> ℹ [2026-05-14 05:24:06] Perform `Seurat::ScaleData()`
#> ℹ [2026-05-14 05:24:06] Perform pca linear dimension reduction
#> ℹ [2026-05-14 05:24:16] Use stored estimated dimensions 1:20 for Standardpca
#> ℹ [2026-05-14 05:24:17] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-05-14 05:24:17] Reorder clusters...
#> ℹ [2026-05-14 05:24:17] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-05-14 05:24:17] Perform umap nonlinear dimension reduction
#> ℹ [2026-05-14 05:24:17] Perform umap nonlinear dimension reduction using Standardpca (1:20)
#> ℹ [2026-05-14 05:24:20] Perform umap nonlinear dimension reduction using Standardpca (1:20)
#> ✔ [2026-05-14 05:24:23] 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 [2026-05-14 05:24:23] Start CellChat analysis
#> ℹ [2026-05-14 05:27:21] 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 <<< [2026-05-14 05:27:22.885775]"
#> [1] ">>> CellChat inference is done. Parameter values are stored in `object@options$parameter` <<< [2026-05-14 05:27:41.813448]"
#> ℹ [2026-05-14 05:27:41] Processing condition: "ConditionB"
#> [1] "Create a CellChat object from a data matrix"
#> Set cell identities for the new CellChat object 
#> The cell groups used for CellChat analysis are  Endocrine, Ngn3-high-EP, Ductal, Ngn3-low-EP, Pre-endocrine 
#> The number of highly variable ligand-receptor pairs used for signaling inference is 601 
#> triMean is used for calculating the average gene expression per cell group. 
#> [1] ">>> Run CellChat on sc/snRNA-seq data <<< [2026-05-14 05:27:43.049499]"
#> [1] ">>> CellChat inference is done. Parameter values are stored in `object@options$parameter` <<< [2026-05-14 05:28:03.373663]"
#> ℹ [2026-05-14 05:28:03] Merging CellChat objects for comparison "ConditionA_vs_ConditionB"
#> Merge the following slots: 'data.signaling','images','net', 'netP','meta', 'idents', 'var.features' , 'DB', and 'LR'.
#> ✔ [2026-05-14 05:28:03] CellChat analysis completed

CCCHeatmap(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA",
  plot_type = "heatmap",
  display_by = "aggregation",
  top_n = 20
)


CCCHeatmap(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA",
  plot_type = "dot",
  display_by = "interaction",
  sender.use = "Ductal",
  top_n = 20
)


CCCHeatmap(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA",
  plot_type = "dot",
  display_by = "interaction",
  receiver.use = "Ngn3-low-EP",
  top_n = 20
)


CCCHeatmap(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA",
  plot_type = "heatmap",
  display_by = "aggregation",
  top_n = 20
)


CCCHeatmap(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA",
  plot_type = "heatmap",
  display_by = "interaction",
  facet_by = "sender",
  top_n = 10
)


CCCHeatmap(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA",
  plot_type = "heatmap",
  display_by = "interaction",
  facet_by = "sender",
  color.by = "pvalue",
  top_n = 10
)


CCCHeatmap(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA",
  plot_type = "bubble",
  top_n = 5
)


CCCHeatmap(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA_vs_ConditionB",
  plot_type = "bubble",
  top_n = 5
)


CCCHeatmap(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA",
  plot_type = "role_heatmap",
  #' pattern = "outgoing",
  width = 0.6,
  height = 2.5
)


CCCHeatmap(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA",
  plot_type = "role_heatmap",
  pattern = "outgoing",
  width = 0.6,
  height = 2.5
)


CCCHeatmap(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA_vs_ConditionB",
  plot_type = "role_heatmap",
  pattern = "incoming",
  palette = "Paired",
  width = 0.6,
  height = 3.5
)


CCCHeatmap(
  pancreas_sub,
  method = "CellChat",
  condition = "ConditionA_vs_ConditionB",
  plot_type = "diff_heatmap",
  pattern = "all",
  palette = "Paired",
  top_n = 20,
  width = 0.6,
  height = 3.5
)