PAGA plot

Generates a PAGA plot based on the given Seurat object and PAGA result.

Usage

PAGAPlot(
  srt,
  paga = NULL,
  type = "connectivities",
  reduction = NULL,
  dims = c(1, 2),
  cells = NULL,
  show_transition = FALSE,
  node_palette = "Chinese",
  node_palcolor = NULL,
  node_size = 4,
  node_alpha = 1,
  node_highlight = NULL,
  node_highlight_color = "red",
  label = FALSE,
  label.size = 3.5,
  label.fg = "white",
  label.bg = "black",
  label.bg.r = 0.1,
  label_insitu = FALSE,
  label_repel = FALSE,
  label_repulsion = 20,
  label_point_size = 1,
  label_point_color = "black",
  label_segment_color = "black",
  edge_threshold = 0.01,
  edge_line = c("straight", "curved"),
  edge_line_curvature = 0.3,
  edge_line_angle = 90,
  edge_size = c(0.2, 1),
  edge_color = "grey40",
  edge_alpha = 0.5,
  edge_shorten = 0,
  edge_offset = 0,
  edge_highlight = NULL,
  edge_highlight_color = "red",
  transition_threshold = 0.01,
  transition_line = c("straight", "curved"),
  transition_line_curvature = 0.3,
  transition_line_angle = 90,
  transition_size = c(0.2, 1),
  transition_color = "black",
  transition_alpha = 1,
  transition_arrow_type = "closed",
  transition_arrow_angle = 20,
  transition_arrow_length = grid::unit(0.02, "npc"),
  transition_shorten = 0.05,
  transition_offset = 0,
  transition_highlight = NULL,
  transition_highlight_color = "red",
  aspect.ratio = 1,
  title = "PAGA",
  subtitle = NULL,
  xlab = NULL,
  ylab = NULL,
  legend.position = "right",
  legend.direction = "vertical",
  theme_use = "theme_scop",
  theme_args = list(),
  return_layer = FALSE,
  verbose = TRUE
)

Arguments

srt: A Seurat object.
paga: The PAGA result from the Seurat object. Default is srt@tools[["PAGA"]], falling back to srt@misc[["paga"]] (e.g. a PAGA result converted from an AnnData object).
type: The type of plot to generate. Possible values are "connectivities" (default) and "connectivities_tree".
reduction: Reduction to plot. NULL uses DefaultReduction.
dims: Length-2 vector of dimensions to plot.
cells: Cell names to include.
show_transition: Whether to display transitions between different cell states.
node_palette: A character vector specifying the name of the color palette for node groups.
node_palcolor: A character vector specifying the names of the colors for each node group.
node_size: A numeric value or column name of node specifying the size of the nodes.
node_alpha: A numeric value or column name of node specifying the transparency of the nodes.
node_highlight: A character vector specifying the names of nodes to highlight.
node_highlight_color: A character vector specifying the color for highlighting nodes.
label, label_insitu, label.size, label.fg, label.bg, label.bg.r: Group labels. label_insitu = FALSE uses numbers instead of group names.
label_repel, label_repulsion, label_point_size, label_point_color, label_segment_color: Repel labels away from cluster centers.
edge_threshold: The threshold for removing edges.
edge_line: A character vector specifying the type of line for edges (straight, curved).
edge_line_curvature: The curvature of curved edges.
edge_line_angle: The angle of curved edges.
edge_size: A numeric vector specifying the range of edge sizes.
edge_color: A character vector specifying the color of the edges.
edge_alpha: The transparency of the edges.
edge_shorten: The length of the edge shorten.
edge_offset: The length of the edge offset.
edge_highlight: A character vector specifying the names of edges to highlight.
edge_highlight_color: A character vector specifying the color for highlighting edges.
transition_threshold: The threshold for removing transitions.
transition_line: A character vector specifying the type of line for transitions (straight, curved).
transition_line_curvature: The curvature of curved transitions.
transition_line_angle: The angle of curved transitions.
transition_size: A numeric vector specifying the range of transition sizes.
transition_color: A character vector specifying the color of the transitions.
transition_alpha: The transparency of the transitions.
transition_arrow_type: A character vector specifying the type of arrow for transitions (closed, open).
transition_arrow_angle: The angle of the transition arrow.
transition_arrow_length: The length of the transition arrow.
transition_shorten: The length of the transition shorten.
transition_offset: The length of the transition offset.
transition_highlight: A character vector specifying the names of transitions to highlight.
transition_highlight_color: A character vector specifying the color for highlighting transitions.
aspect.ratio: Panel aspect ratio.
title: The text for the title.
subtitle: Plot subtitle.
xlab: Label for the x axis.
ylab: Label for the y axis.
legend.position: Legend position passed to theme().
legend.direction: Legend direction passed to theme().
theme_use, theme_args: Theme name or function, plus extra theme arguments.
return_layer: Whether to return the plot layers as a list. Defaults is FALSE.
verbose: Whether to print messages.

Examples

data(pancreas_sub)
pancreas_sub <- RunStandardWorkflow(pancreas_sub)
#> ℹ [2026-08-30 04:40:12] Start standard processing workflow...
#> ℹ [2026-08-30 04:40:12] Checking a list of <Seurat>...
#> ! [2026-08-30 04:40:12] Data 1/1 of the `srt_list` is "unknown"
#> Warning: Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-08-30 04:40:12] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-08-30 04:40:12] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-08-30 04:40:12] Use the separate HVF from `srt_list`
#> ℹ [2026-08-30 04:40:13] Number of available HVF: 2000
#> ℹ [2026-08-30 04:40:13] Finished check
#> ℹ [2026-08-30 04:40:13] Perform `ScaleData()`
#> ℹ [2026-08-30 04:40:13] Perform pca linear dimension reduction
#> ℹ [2026-08-30 04:40:13] Use stored estimated dimensions 1:23 for Standardpca
#> ℹ [2026-08-30 04:40:13] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-08-30 04:40:13] Reorder clusters...
#> ℹ [2026-08-30 04:40:13] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-08-30 04:40:13] Perform umap nonlinear dimension reduction
#> ✔ [2026-08-30 04:40:21] Standard processing workflow completed
pancreas_sub <- RunPAGA(
  pancreas_sub,
  group.by = "SubCellType",
  linear_reduction = "PCA",
  nonlinear_reduction = "UMAP",
  backend = "cpp",
  return_seurat = TRUE
)
#> ℹ [2026-08-30 04:40:21] Running PAGA with BiocNeighbors using 29 neighbors
#> ✔ [2026-08-30 04:40:21] PAGA cpp backend completed

PAGAPlot(pancreas_sub)


PAGAPlot(
  pancreas_sub,
  type = "connectivities_tree"
)


PAGAPlot(
  pancreas_sub,
  reduction = "PCA"
)


PAGAPlot(
  pancreas_sub,
  reduction = "UMAP"
)


PAGAPlot(
  pancreas_sub,
  edge_shorten = 0.05
)


PAGAPlot(
  pancreas_sub,
  label = TRUE
)


PAGAPlot(
  pancreas_sub,
  label = TRUE,
  label_insitu = TRUE
)


PAGAPlot(
  pancreas_sub,
  label = TRUE,
  label_insitu = TRUE,
  label_repel = TRUE
)


PAGAPlot(
  pancreas_sub,
  edge_line = "curved"
)


PAGAPlot(
  pancreas_sub,
  node_size = "GroupSize"
)


PAGAPlot(
  pancreas_sub,
  node_highlight = "Ductal"
)


PAGAPlot(
  pancreas_sub,
  edge_highlight = paste(
    "Pre-endocrine",
    levels(pancreas_sub$SubCellType),
    sep = "-"
  )
)

Usage

Arguments

See also

Examples